Planet R

October 28, 2016


New package MIDN with initial version 1.0

Package: MIDN
Type: Package
Title: Nearly Exact Sample Size Calculation for Exact Powerful Nonrandomized Tests for Differences Between Binomial Proportions
Version: 1.0
Date: 2016-10-18
Author: Stefan Wellek, Peter Ziegler
Maintainer: Peter Ziegler <>
Description: Implementation of the mid-n algorithms presented in Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact sample size calculation for superiority trials with binary outcome.
License: CC0
Depends: BiasedUrn
NeedsCompilation: no
Packaged: 2016-10-28 07:46:54 UTC; ziegler
Repository: CRAN
Date/Publication: 2016-10-28 11:31:15

More information about MIDN at CRAN

October 28, 2016 11:15 AM

New package spellcheckr with initial version 0.1.2

Package: spellcheckr
Type: Package
Title: Correct the Spelling of a Given Word in the English Language
Version: 0.1.2
Author: Selva Prabhakaran
Maintainer: Selva Prabhakaran <>
Description: Corrects the spelling of a given word in English using a modification of Peter Norvig's spell correct algorithm (see <>) which handles up to three edits. The algorithm tries to find the spelling with maximum probability of intended correction out of all possible candidate corrections from the original word.
License: GPL-2 | GPL-3
Encoding: UTF-8
LazyData: true
Imports: data.table, dplyr, stringr
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-10-27 13:30:41 UTC; selvaprabhakaran
Depends: R (>= 2.10)
Repository: CRAN
Date/Publication: 2016-10-28 10:04:43

More information about spellcheckr at CRAN

October 28, 2016 09:16 AM

New package raptr with initial version 0.0.1

Package: raptr
Type: Package
Title: Representative and Adequate Prioritization Toolkit in R
Version: 0.0.1
Authors@R: c(person(c("Jeffrey", "O"), "Hanson", email='', role = c("aut", "cre")), person(c("Jonathon", "R"), "Rhodes", role = c("aut")), person(c("Hugh", "P"), "Possingham", role = c("aut")), person(c("Richard", "A"), "Fuller", role = c("aut")))
Description: Biodiversity is in crisis. The overarching aim of conservation is to preserve biodiversity patterns and processes. To this end, protected areas are established to buffer species and preserve biodiversity processes. But resources are limited and so protected areas must be cost-effective. This package contains tools to generate plans for protected areas (prioritizations), using spatially explicit targets for biodiversity patterns and processes. To obtain solutions in a feasible amount of time, this package uses the commercial 'Gurobi' software package (obtained from <>). Additionally, the 'rgurobi' package can also be installed to provide extra functionality (obtained from <>).
Imports: utils, boot, grDevices, PBSmapping, graphics, stats, scales, shape, Matrix, adehabitatHR, RgoogleMaps, RandomFields, RColorBrewer, plyr, parallel, doParallel, rgeos, hypervolume, ks, gdalUtils, mvtnorm, ggplot2, testthat
Depends: R(>= 3.1.0), methods, sp, rgdal, raster
LinkingTo: Rcpp, RcppEigen, BH
LazyData: true
License: GPL-3
VignetteBuilder: knitr
Suggests: knitr, dplyr, vegan, gurobi, rgurobi, gridExtra, rgl
SystemRequirements: C++11
Collate: 'dependencies.R' 'RcppExports.R' 'raptr-internal.R' 'generics.R' 'DemandPoints.R' 'misc.R' 'PlanningUnitPoints.R' 'AttributeSpace.R' 'AttributeSpaces.R' 'GurobiOpts.R' 'ManualOpts.R' 'calcSpeciesAverageInPus.R' 'calcBoundaryData.R' 'RapData.R' 'RapReliableOpts.R' 'RapResults.R' 'RapUnreliableOpts.R' 'RapUnsolved.R' 'RapSolved.R' 'SpatialPolygons2PolySet.R' 'data.R' 'rap.R' 'raptr.R' 'rrap.proportion.held.R' 'sim.pus.R' '' 'sim.species.R' 'urap.proportion.held.R' 'zzz.R'
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-10-27 23:48:45 UTC; uqjhans4
Author: Jeffrey O Hanson [aut, cre], Jonathon R Rhodes [aut], Hugh P Possingham [aut], Richard A Fuller [aut]
Maintainer: Jeffrey O Hanson <>
Repository: CRAN
Date/Publication: 2016-10-28 10:51:18

More information about raptr at CRAN

October 28, 2016 09:15 AM

New package ggdmc with initial version

Package: ggdmc
Type: Package
Title: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities
Date: 2016-09-30
Author: Yi-Shin Lin [aut, cre], Andrew Heathcote [aut]
Maintainer: Yi-Shin Lin <>
Description: A fast engine for computing hierarchical Bayesian model implemented in the Dynamic Model of Choice.
License: GPL-2
LazyData: TRUE
Depends: R (>= 3.0.2)
Imports: Rcpp (>= 0.12.3), ggplot2 (>= 2.1.0), coda, rtdists (>= 0.6-6), gridExtra (>= 2.2-1), ggmcmc (>= 0.7.3), ggthemes (>= 3.0.1), stats (>= 3.2.2), loo (>= 0.1.6)
LinkingTo: Rcpp (>= 0.12.3), RcppArmadillo (>= 0.6.700.6.0)
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-10-27 23:15:37 UTC; yslin
Repository: CRAN
Date/Publication: 2016-10-28 10:51:12

More information about ggdmc at CRAN

October 28, 2016 09:14 AM

Journal of Statistical Software

Simulation-Based Power Calculations for Mixed Effects Modeling: ipdpower in Stata

Simulations are a practical and reliable approach to power calculations, especially for multi-level mixed effects models where the analytic solutions can be very complex. In addition, power calculations are model-specific and multi-level mixed effects models are defined by a plethora of parameters. In other words, model variations in this context are numerous and so are the tailored algebraic calculations. This article describes ipdpower in Stata, a new simulations-based command that calculates power for mixed effects two-level data structures. Although the command was developed having individual patient data meta-analyses and primary care databases analyses in mind, where patients are nested within studies and general practices respectively, the methods apply to any two-level structure.

by Evangelos Kontopantelis, David A Springate, Rosa Parisi, David Reeves at October 28, 2016 12:00 AM

synthpop: Bespoke Creation of Synthetic Data in R

In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example.

by Beata Nowok, Gillian M. Raab, Chris Dibben at October 28, 2016 12:00 AM

October 27, 2016

Journal of Statistical Software

Stochastic Newton Sampler: The R Package sns

The R package sns implements the stochastic Newton sampler (SNS), a MetropolisHastings Markov chain Monte Carlo (MCMC) algorithm where the proposal density function is a multivariate Gaussian based on a local, second-order Taylor-series expansion of log-density. The mean of the proposal function is the full Newton step in the NewtonRaphson optimization algorithm. Taking advantage of the local, multivariate geometry captured in log-density Hessian allows SNS to be more efficient than univariate samplers, approaching independent sampling as the density function increasingly resembles a multivariate Gaussian. SNS requires the log-density Hessian to be negative-definite everywhere in order to construct a valid proposal function. This property holds, or can be easily checked, for many GLM-like models. When the initial point is far from density peak, running SNS in non-stochastic mode by taking the Newton step - augmented with line search - allows the MCMC chain to converge to high-density areas faster. For high-dimensional problems, partitioning the state space into lower-dimensional subsets, and applying SNS to the subsets within a Gibbs sampling framework can significantly improve the mixing of SNS chains. In addition to the above strategies for improving convergence and mixing, sns offers utilities for diagnostics and visualization, sample-based calculation of Bayesian predictive posterior distributions, numerical differentiation, and log-density validation.

by Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T. A. Sharabiani at October 27, 2016 12:00 AM

%ERA: A SAS Macro for Extended Redundancy Analysis

A new approach to structural equation modeling based on so-called extended redundancy analysis has been recently proposed in the literature, enhanced with the added characteristic of generalizing redundancy analysis and reduced-rank regression models for more than two blocks. In this approach, the relationships between the observed exogenous variables and the observed endogenous variables are moderated by the presence of unobservable composites that were estimated as linear combinations of exogenous variables, permitting a great flexibility to specify and fit a variety of structural relationships. In this paper, we propose the SAS macro %ERA to specify and fit structural relationships in the extended redundancy analysis (ERA) framework. Two examples (simulation and real data) are provided in order to reproduce results appearing in the original article where ERA was proposed.

by Pietro Giorgio Lovaglio, Gianmarco Vacca at October 27, 2016 12:00 AM

October 26, 2016

Dirk Eddelbuettel

Rblpapi 0.3.5

A new release of Rblpapi is now on CRAN. Rblpapi provides a direct interface between R and the Bloomberg Terminal via the C++ API provided by Bloomberg Labs (but note that a valid Bloomberg license and installation is required).

This is the sixth release since the package first appeared on CRAN last year. This release brings new functionality via new (getPortfolio()) and extended functions (getTicks()) as well as several fixes:

Changes in Rblpapi version 0.3.5 (2016-10-25)

  • Add new function getPortfolio to retrieve portfolio data via bds (John in #176)

  • Extend getTicks() to (optionally) return non-numeric data as part of data.frame or data.table (Dirk in #200)

  • Similarly extend getMultipleTicks (Dirk in #202)

  • Correct statement on timestamp for getBars (Closes issue #192)

  • Minor edits to a few files in order to either please R(-devel) CMD check --as-cran, or update documentation

Courtesy of CRANberries, there is also a diffstat report for the this release. As always, more detailed information is on the Rblpapi page. Questions, comments etc should go to the issue tickets system at the GitHub repo.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

October 26, 2016 02:14 AM

October 24, 2016

Dirk Eddelbuettel

Word Marathon Majors: Five Star Finisher!

A little over eight years ago, I wrote a short blog post which somewhat dryly noted that I had completed the five marathons constituting the World Marathon Majors. I had completed Boston, Chicago and New York during 2007, adding London and then Berlin (with a personal best) in 2008. The World Marathon Majors existed then, but I was not aware of a website. The organisation was aiming to raise the profile of the professional and very high-end aspect of the sport. But marathoning is funny as they let somewhat regular folks like you and me into the same race. And I always wondered if someone kept track of regular folks completing the suite...

I have been running a little less the last few years, though I did get around to complete the Illinois Marathon earlier this year (only tweeted about it and still have not added anything to the running section of my blog). But two weeks ago, I was once again handing out water cups at the Chicago Marathon, sending along two tweets when the elite wheelchair and elite male runners flew by. To the first, the World Marathon Majors account replied, which lead me to their website. Which in turn lead me to the Five Star Finisher page, and the newer / larger Six Star Finisher page now that Tokyo has been added.

And in short, one can now request one's record to be added (if they check out). So I did. And now I am on the Five Star Finisher page!

I don't think I'll ever surpass that as a runner. The table header and my row look like this:

Table header Dirk Eddelbuettel

If only my fifth / sixth grade physical education teacher could see that---he was one of those early running nuts from the 1970s and made us run towards / around this (by now enlarged) pond and boy did I hate that :) Guess it did have some long lasting effects. And I casually circled the lake a few years ago, starting much further away from my parents place. Once you are in the groove for distance...

But leaving that aside, running has been fun and I with some luck I may have another one or two marathons or Ragnar Relays left. The only really bad part about this is that I may have to get myself to Tokyo after all (for something that is not an ISM workshop) ...

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

October 24, 2016 02:41 AM

October 22, 2016

Dirk Eddelbuettel

RcppArmadillo 0.7.500.0.0

armadillo image

A few days ago, Conrad released Armadillo 7.500.0. The corresponding RcppArmadillo release 0.7.500.0.0 is now on CRAN (and will get into Debian shortly).

Armadillo is a powerful and expressive C++ template library for linear algebra aiming towards a good balance between speed and ease of use with a syntax deliberately close to a Matlab. RcppArmadillo integrates this library with the R environment and language--and is widely used by (currently) 274 other packages on CRAN.

Changes in this release relative to the previous CRAN release are as follows:

Changes in RcppArmadillo version 0.7.500.0.0 (2016-10-20)

  • Upgraded to Armadillo release 7.500.0 (Coup d'Etat)

    • Expanded qz() to optionally specify ordering of the Schur form

    • Expanded each_slice() to support matrix multiplication

Courtesy of CRANberries, there is a diffstat report. More detailed information is on the RcppArmadillo page. Questions, comments etc should go to the rcpp-devel mailing list off the R-Forge page.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

October 22, 2016 03:43 PM

October 21, 2016

Dirk Eddelbuettel

anytime 0.0.4: New features and fixes

A brand-new release of anytime is now on CRAN following the three earlier releases since mid-September. anytime aims to convert anything in integer, numeric, character, factor, ordered, ... format to POSIXct (or Date) objects -- and does so without requiring a format string. See the anytime page for a few examples.

With release 0.0.4, we add two nice new features. First, NA, NaN and Inf are now simply skipped (similar to what the corresponding Base R functions do). Second, we now also accept large numeric values so that, _e.g., anytime(as.numeric(Sys.time()) also works, effectively adding another input type. We also have squashed an issue reported by the 'undefined behaviour' sanitizer, and the widened the test for when we try to deploy the gettz package get missing timezone information.

A quick example of the new features:

anydate(c(NA, NaN, Inf, as.numeric(as.POSIXct("2016-09-01 10:11:12"))))
[1] NA           NA           NA           "2016-09-01"

The NEWS file summarises the release:

Changes in anytime version 0.0.4 (2016-10-20)

  • Before converting via lexical_cast, assign to atomic type via template logic to avoid an UBSAN issue (PR #15 closing issue #14)

  • More robust initialization and timezone information gathering.

  • More robust processing of non-finite input also coping with non-finite values such as NA, NaN and Inf which all return NA

  • Allow numeric POSIXt representation on input, also creating proper POSIXct (or, if requested, Date)

Courtesy of CRANberries, there is a comparison to the previous release. More information is on the anytime page.

For questions or comments use the issue tracker off the GitHub repo.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

October 21, 2016 02:25 AM

October 18, 2016

Bioconductor Project Working Papers

Doubly-robust Nonparametric Inference on the Average Treatment Effect

Doubly-robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double-robustness does not readily extend to inference. We present a general theoretical study of the behavior of doubly-robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different approaches for constructing such estimators and investigate the extent to which they may be modified to also allow doubly-robust inference. We find that while targeted maximum likelihood estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose. We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly-robust inference in other problems.

by David Benkeser et al. at October 18, 2016 11:21 PM

Online Cross-Validation-Based Ensemble Learning

Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate the practical performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database.

by David Benkeser et al. at October 18, 2016 11:11 PM

October 05, 2016

Bioconductor Project Working Papers

October 04, 2016

Bioconductor Project Working Papers

Performance-constrained Binary Classification Using Ensemble Learning: an Application to Cost-efficient Targeted PrEP Strategies

Binary classifications problems are ubiquitous in health and social science applications. In many cases, one wishes to balance two conflicting criteria for an optimal binary classifier. For instance, in resource-limited settings, an HIV prevention program based on offering Pre-Exposure Prophylaxis (PrEP) to select high-risk individuals must balance the sensitivity of the binary classifier in detecting future seroconverters (and hence offering them PrEP regimens) with the total number of PrEP regimens that is financially and logistically feasible for the program to deliver. In this article, we consider a general class of performance-constrained binary classification problems wherein the objective function and the constraint are both monotonic with respect to a threshold function. These include the minimization of the Rate of Positive Predictions subject to a lower bound on the sensitivity, and vice versa, and the Neyman-Pearson paradigm, which minimizes the type II error subject to an upper bound on the type I error. We propose an ensemble approach to these binary classification problems based on the Super Learner algorithm, characterized by weights combining the constituent risk prediction algorithms and a discriminating risk threshold for classification that aim to minimize the given constrained optimality criterion. We then illustrate the application of the proposed classifier to develop an individual PrEP targeting strategy in a resource-limited setting, with the goal of minimizing the number of PrEP offerings while achieving a minimum required sensitivity. This proof of concept data analysis uses baseline data from the ongoing Sustainable East Africa Research in Community Health study.

by Wenjing Zheng et al. at October 04, 2016 09:44 PM

September 29, 2016

Statistical Modelling

Time-dependent ROC methodology to evaluate the predictive accuracy of semiparametric multi-state models in the presence of competing risks: An application to peritoneal dialysis programme


The evaluation of peritoneal dialysis (PD) programmes requires the use of statistical methods that suit the complexity of such programmes. Multi-state regression models taking competing risks into account are a good example of suitable approaches. In this work, multi-state structured additive regression (STAR) models combined with penalized splines (P-splines) are proposed to evaluate peritoneal dialysis programmes. These models are very flexible since they may consider smooth estimates of baseline transition intensities and the inclusion of time-varying and smooth covariate effects at each transition. A key issue in survival analysis is the quantification of the time-dependent predictive accuracy of a given regression model, which is typically assessed using receiver operating characteristic (ROC)’based methodologies. The main objective of the present study is to adapt the concept of time-dependent ROC curve, and their corresponding area under the curve (AUC), to a multi-state competing risks framework. All statistical methodologies discussed in this work were applied to PD survival data. Using a multi-state competing risks framework, this study explored the effects of major clinical covariates on survival such as age, sex, diabetes and previous renal replacement therapy. Such multi-state model was composed of one transient state (peritonitis) and several absorbing states (death, transfer to haemodialysis and renal transplantation). The application of STAR models combined with time-dependent ROC curves revealed important conclusions not previously reported in the nephrology literature when using standard statistical methodologies. For practical application, all the statistical methods proposed in this article were implemented in R and we wrote and made available a script named as NestedCompRisks.

by Teixeira, L., Cadarso-Suarez, C., Rodrigues, A., Mendonca, D. at September 29, 2016 05:16 AM

A multivariate single-index model for longitudinal data


Index measures are commonly used in medical research and clinical practice, primarily for quantification of health risks in individual subjects or patients. The utility of an index measure is ultimately contingent on its ability to predict health outcomes. Construction of medical indices has largely been based on heuristic arguments, although the acceptance of a new index typically requires objective validation, preferably with multiple outcomes. In this article, we propose an analytical tool for index development and validation. We use a multivariate single-index model to ascertain the best functional form for risk index construction. Methodologically, the proposed model represents a multivariate extension of the traditional single-index models. Such an extension is important because it assures that the resultant index simultaneously works for multiple outcomes. The model is developed in the general framework of longitudinal data analysis. We use penalized cubic splines to characterize the index components while leaving the other subject characteristics as additive components. The splines are estimated directly by penalizing nonlinear least squares, and we show that the model can be implemented using existing software. To illustrate, we examine the formation of an adiposity index for prediction of systolic and diastolic blood pressure in children. We assess the performance of the method through a simulation study.

by Wu, J., Tu, W. at September 29, 2016 05:16 AM

Semi-parametric frailty model for clustered interval-censored data


The shared frailty model is a popular tool to analyze correlated right-censored time-to-event data. In the shared frailty model, the latent frailty is assumed to be shared by the members of a cluster and is assigned a parametric distribution, typically a gamma distribution due to its conjugacy. In the case of interval-censored time-to-event data, the inclusion of frailties results in complicated intractable likelihoods. Here, we propose a flexible frailty model for analyzing such data by assuming a smooth semi-parametric form for the conditional time-to-event distribution and a parametric or a flexible form for the frailty distribution. The results of a simulation study suggest that the estimation of regression parameters is robust to misspecification of the frailty distribution (even when the frailty distribution is multimodal or skewed). Given sufficiently large sample sizes and number of clusters, the flexible approach produces smooth and accurate posterior estimates for the baseline survival function and for the frailty density, and it can correctly detect and identify unusual frailty density forms. The methodology is illustrated using dental data from the Signal Tandmobiel® study.

by Yavuz, A. C., Lambert, P. at September 29, 2016 05:16 AM

Bayesian dynamic modelling to assess differential treatment effects on panic attack frequencies


To represent the complex structure of intensive longitudinal data of multiple individuals, we propose a hierarchical Bayesian Dynamic Model (BDM). This BDM is a generalized linear hierarchical model where the individual parameters do not necessarily follow a normal distribution. The model parameters can be estimated on the basis of relatively small sample sizes and in the presence of missing time points. We present the BDM and discuss the model identification, convergence and selection. The use of the BDM is illustrated using data from a randomized clinical trial to study the differential effects of three treatments for panic disorder. The data involves the number of panic attacks experienced weekly (73 individuals, 10–52 time points) during treatment. Presuming that the counts are Poisson distributed, the BDM considered involves a linear trend model with an exponential link function. The final model included a moving average parameter and an external variable (duration of symptoms pre-treatment). Our results show that cognitive behavioural therapy is less effective in reducing panic attacks than serotonin selective re-uptake inhibitors or a combination of both. Post hoc analyses revealed that males show a slightly higher number of panic attacks at the onset of treatment than females.

by Krone, T., Albers, C., Timmerman, M. at September 29, 2016 05:16 AM

July 26, 2016

RCpp Gallery

RcppHoney Introduction


In C++ we often have containers that are not compatible with R or Rcpp with data already in them (std::vector, std::set, etc.). One would like to be able to operate on these containers without having to copy them into Rcpp structures like IntegerVector. RcppHoney aims to address this problem by providing operators and functions with R semantics that can be used on any iterator-based container.


RcppHoney allows any iterator-based container to be “hooked” in. Once a container type is hooked to RcppHoney, it is granted operators (+, -, *, /, etc.) and a host of other mathematical functions that can be run on it. It also becomes interoperable with any other hooked data structure. This lets us write expressions that look like std::vector + Rcpp::IntegerVector + log(Rcpp::NumericVector) and get the expected results.


RcppHoney has several structures that are hooked in by default. Currently they are std::vector, std::set, and Rcpp::VectorBase. The ability to hook in custom structures is also provided.

All operators and functions are implemented as expression templates to minimize memory usage and enhance performance. The goal here is to only copy the data into an R compatible structure when we must (i.e. when we return it to R). This is achieved through the use of the RcppHoney::operand class. RcppHoney::operand provides an iterable interface to the result types of operators and functions.

RcppHoney currently provides all the basic mathematical operators (+, -, *, /) as well as some common functions (abs, sin, cos, exp, etc.). Eventually all of the functionality provided by Rcpp::sugar as well as anything else we can think of will be supported.

Enough about the abstract though…let’s see it in action.


The following example shows how to hook in a custom data structure (in this case std::list) as well as the types of expressions that can be created once a data structure is hooked in.

// [[Rcpp::depends(RcppHoney)]]

#include <RcppCommon.h>
#include <RcppHoneyForward.hpp> // we have to do this because we're going to hook in a non-default structure
#include <list>

// We have to declare our hooks before we include RcppHoney.hpp
namespace RcppHoney {
namespace hooks {

// Hook in all std::list types (could be more specific)
template< typename T, typename A >
traits::true_type is_hooked(const std::list< T, A > &val);

// Tell RcppHoney that NA has meaning in std::list
template< typename T, typename A >
traits::true_type has_na(const std::list< T, A > &val);

// Tell RcppHoney that it needs to create basic (e.g. std::list + std::list) operators
template< typename T, typename A >
traits::true_type needs_basic_operators(const std::list< T, A > &val);

// Tell RcppHoney that it needs to create scalar (e.g. std::list + int/double) operators
template< typename T, typename A >
traits::true_type needs_scalar_operators(const std::list< T, A > &val);

// Tell RcppHoney that this set of types is part of the FAMILY_USER + 1 family.
// This is used in conjunction with needs_basic_operators.  If you have
// needs_basic_operators return RcppHoney::traits::false_type, then only types
// that are not part of the same family will have binary operators created
// between them.
template< typename T, typename A >
traits::int_constant< FAMILY_USER + 1 > family(const std::list< T, A > &val);

} // namespace hooks
} // namespace RcppHoney

#include <RcppHoney.hpp>

// [[Rcpp::export]]
Rcpp::NumericVector example_manually_hooked() {

    // We manually hooked std::list in to RcppHoney so we'll create one
    std::list< int > l;
    l.push_back(1); l.push_back(2); l.push_back(3); l.push_back(4); l.push_back(5);

    // std::vector is already hooked in to RcppHoney in default_hooks.hpp so we'll
    // create one of those too
    std::vector< int > v(l.begin(), l.end());

    // And for good measure, let's create an Rcpp::NumericVector which is also hooked by default
    Rcpp::NumericVector v2(v.begin(), v.end());

    // Now do some weird operations incorporating std::vector, std::list, Rcpp::NumericVector
    // and some RcppHoney functions and return it.  The return value will be equal to the following
    // R snippet:
    //     v <- 1:5
    //     result <- 42 + v + v + log(v) - v - v + sqrt(v) + -v + 42

    // We can store our result in any of RcppHoney::LogicalVector, RcppHoney::IntegerVector, or
    // RcppHoney::NumericVector and simply return it to R.  These classes inherit from their
    // Rcpp counterparts and add a new constructor.  The only copy of the data, in this case, is when
    // we assign our expression to retval.  Since it is then a "native" R type, returning it is a
    // shallow copy.  Alternatively we could write this as:
    //     return Rcpp::wrap(1 + v + RcppHoney::log(v) - v - 1 + RcppHoney::sqrt(v) + -v2);

    RcppHoney::NumericVector retval
        =  42 + l + v + RcppHoney::log(v) - v - l + RcppHoney::sqrt(v) + -v2 + 42;
    return retval;


RcppHoney is a powerful tool for allowing different container types to interoperate under Rcpp. It can save development time as well as help the user generate faster and more readable code.

RcppHoney is available via CRAN though as it is still in an alpha state and changing rapidly, it is recommended that you install it from source. Source code is available at

July 26, 2016 12:00 AM

July 18, 2016

R you ready?

Populating data frame cells with more than one value

Data frames are lists

Most R users will know that data frames are lists. You can easily verify that a data frame is a list by typing

d <- data.frame(id=1:2, name=c("Jon", "Mark"))
 id name
1 1 Jon
2 2 Mark
[1] TRUE

However, data frames are lists with some special properties. For example, all entries in the list must have the same length (here 2), etc. You can find a nice description of the differences between lists and data frames here. To access the first column of d, we find that it contains a vector (and a factor in case of column name). Note, that [[ ]] is an operator to select a list element. As data frames are lists, they will work here as well.

[1] TRUE

Data frame columns can contain lists

A long time, I was unaware of the fact, that data frames may also contain lists as columns instead of vectors. For example, let’s assume Jon’s children are Mary and James, and Mark’s children are called Greta and Sally. Their names are stored in a list with two elements. We can add them to the data frame like this:

d$children <-  list(c("Mary", "James"), c("Greta", "Sally"))
 id name children
1 1 Jon Mary, James
2 2 Mark Greta, Sally

A single data frame entry in column children now contains more than one value. Given that the column is a list, not a vector, we cannot go as usual when modifying an entry of the column. For example, to change Jon’s children, we cannot do

> d[1 , "children"] <- c("Mary", "James", "Thomas")

Error in `[<`(`*tmp*`, 1, "children", value = c("Mary", "James", :
replacement has 3 rows, data has 1

Taking into account the list structure of the column, we can type the following to change the values in a single cell.

d[1 , "children"][[1]] <- list(c("Mary", "James", "Thomas"))

# or also

d$children[1] <- list(c("Mary", "James", "Thomas"))
 id name children
1 1 Jon Mary, James, Thomas
2 2 Mark Greta, Sally

You can also create a data frame having a list as a column using the <tt>data.frame</tt> function, but with a little tweak. The list column has to be wrapped inside the function <tt>I</tt>. This will protect it from several conversions taking place in <tt>data.frame</tt> (see <tt>?I</tt> documentation).

d <- data.frame(id = 1:2,
                   name = c("Jon", "Mark"),
                   children = I(list(c("Mary", "James"),
                                     c("Greta", "Sally")))

This is an interesting feature, which gives me a deeper understanding of what a data frame is. But when exactly would I want to use it? I have not encountered the need to use it very often yet (though of course there may be plenty of situations where it makes sense). But today I had a case where this feature seemed particularly useful.

Converting lists and data frames to JSON

I had two separate types of information. One stored in a data frame and the other one in a list Referring to the example above, I had

d <- data.frame(id=1:2, name=c("Jon", "Mark"))
 id name
1 1 Jon
2 2 Mark


ch <- list(c("Mary", "James"), c("Greta", "Sally"))
[1] "Mary" "James"

[1] "Greta" "Sally"

I needed to return an array of JSON objects which look like this.

    "id": 1,
    "name": "Jon",
    "children": ["Mary", "James"]
    "id": 2,
    "name": "Mark",
    "children": ["Greta", "Sally"]

Working with the superb jsonlite package to convert R to JSON, I could do the following to get the result above.


l <- split(d, seq(nrow(d))) # convert data frame rows to list
l <- unname(l)              # remove list names
for (i in seq_along(l))     # add element from ch to list
    l[[i]] <- c(l[[i]], children=ch[i])

toJSON(l, pretty=T, auto_unbox = T) # convert to JSON

The results are correct, but getting there involved quite a number of tedious steps. These can be avoided by directly placing the list into a column of the data frame. Then jsonlite::toJSON takes care of the rest.

d$children <- list(c("Mary", "James"), c("Greta", "Sally"))
toJSON(d, pretty=T, auto_unbox = T)
    "id": 1,
    "name": "Jon",
    "children": ["Mary", "James"]
    "id": 2,
    "name": "Mark",
    "children": ["Greta", "Sally"]

Nice :) What we do here, is basically creating the same nested list structure as above, only now it is disguised as a data frame. However, this approach is much more convenient.

by markheckmann at July 18, 2016 05:01 PM

June 25, 2016

RCpp Gallery

Introducing Rcpp::algorithm


A while back, I saw a post on StackOverflow where the user was trying to use Rcpp::sugar::sum() on an RcppParallel::RVector. Obviously, this does not work (as Rcpp Sugar pertains to Rcpp types, but not RcppParallel which cannot rely on SEXP-based representation to allow multi-threaded execution). It raised the question “Why doesn’t something more generic exist to provide functions with R semantics that can be used on arbitrary data structures?” As a result, I set out to create a set of such functions in Rcpp::algorithm which follow the pattern of std::algorithm.


Currently Rcpp::algorithm contains only a few simple functions. If these are found to be useful, more will be added. Examples of using the currently implemented iterator-based functions are below.

sum, sum_nona, prod, and prod_nona

#include <Rcpp.h>

using namespace Rcpp;

// [[Rcpp::export]]
double sum_of_matrix_row(NumericMatrix m, int row) {
    NumericMatrix::Row r = m.row(row);

    return algorithm::sum(r.begin(), r.end());

min, max, and mean

#include <Rcpp.h>

using namespace Rcpp;

// [[Rcpp::export]]
double mean_of_matrix_row(NumericMatrix m, int row) {
    NumericMatrix::Row r = m.row(row);

    return algorithm::mean(r.begin(), r.end());

log, exp, and sqrt

#include <Rcpp.h>

using namespace Rcpp;

// [[Rcpp::export]]
NumericVector log_of_matrix_row(NumericMatrix m, int row) {
    NumericMatrix::Row r = m.row(row);

    NumericVector retval(m.cols());
    algorithm::log(r.begin(), r.end(), retval.begin());

    return retval;

Additional Benefits

Through the coding of these simple “algorithms”, a few needs arose.

First, the ability to deduce the appropriate C numeric type given an Rcpp iterator was necessary. This gave birth to the Rcpp::algorithm::helpers::decays_to_ctype and Rcpp::algorithm::helpers::ctype type traits. Given a type, these allow you to determine whether it can be cast to a C numeric type and which type that would be.

Second, the need arose for more information about R types. This gave birth to the Rcpp::algorithm::helpers::rtype traits. These are defined as follows:

template< typename T >
struct rtype_helper {};

struct rtype_helper< double > {
    typedef double type;
    static inline double NA() { return NA_REAL; }
    static inline RCPP_CONSTEXPR double ZERO() { return 0.0; }
    static inline RCPP_CONSTEXPR double ONE() { return 1.0; }

struct rtype_helper< int > {
    typedef int type;
    static inline int NA() { return NA_INTEGER; }
    static inline RCPP_CONSTEXPR int ZERO() { return 0; }
    static inline RCPP_CONSTEXPR int ONE() { return 1; }

template< typename T >
struct rtype {
    typedef typename rtype_helper< typename ctype< T >::type >::type type;
    typedef rtype_helper< typename ctype< T >::type > helper_type;
    static RCPP_CONSTEXPR int RTYPE = helper_type::RTYPE;
    static inline T NA() { return helper_type::NA(); }
    static inline RCPP_CONSTEXPR T ZERO() { return helper_type::ZERO(); }
    static inline RCPP_CONSTEXPR T ONE() { return helper_type::ONE(); }

These additional benefits may actually prove more useful than the algorithms themselves. Only time will tell.

Wrapping Up

There are now some simple iterator-based algorithms that can be used with any structure that supports iterators. They apply the same semantics as the analogous Rcpp::sugar functions, but give us more flexibility in their usage. If you find these to be useful, feel free to request more.

June 25, 2016 12:00 AM

Custom Templated as and wrap Functions within Rcpp.


Consider a need to be able to interface with a data type that is not presently supported by Rcpp. The data type might come from a new library, or from within one of our own applications. In either cases, Rcpp is faced with an issue of consciousness as the new data type is not similar to known types so the autocoversion or seamless R to C++ integration cannot be applied correctly. The issue is two-fold as we need to consider both directions:

  1. Converting from R to C++ using Rcpp::as<T>(obj)
  2. Converting from C++ to R using Rcpp::wrap(obj)

Luckily, there is a wonderful Rcpp vignette called Extending Rcpp that addresses custom objects. However, the details listed are more abstracted than one would like. So, I am going to try to take you through the steps with a bit of commentary. Please note that the approach used is via Templates and partial specialization and will result in some nice automagic at the end.

The overview of the discussion will focus on:

  • Stage 1 - Forward Declarations
  • Stage 2 - Including the Rcpp Header
  • Stage 3 - Implementation of Forward Declarations
  • Stage 4 - Testing Functionality
  • Stage 5 - All together

Explanation of Stages

Stage 1 - Forward Declarations

In the first stage, we must declare our intent to the features we wish to use prior to engaging Rcpp.h. To do so, we will load a different header file and add some definitions to the Rcpp::traits namespace.

Principally, when we start writing the file, the first header that we must load is RcppCommon.h and not the usual Rcpp.h!! If we do not place the forward declaration prior to the Rcpp.h call, we will be unable to appropriately register our extension.

Then, we must add in the different plugin markup for sourceCpp() to set the appropriate flags during the compilation of the code. After the plugins, we will include the actual headers that we want to use. In this document, we will focus on Boost headers for the concrete example. Lastly, we must add two special Rcpp function declaration, Rcpp::as<T>(obj) and Rcpp::wrap(obj), within the Rcpp::traits namespace. To enable multiple types, we must create an Exporter class instead of a more direct call to template <> ClassName as( SEXP ).

// -------------- Stage 1: Forward Declarations with `RcppCommon.h`

#include <RcppCommon.h>

// Flags for C++ compiler: include Boost headers, use the C++11 standard

// [[Rcpp::depends(BH)]]
// [[Rcpp::plugins("cpp11")]]

// Third party library includes that provide the template class of ublas
#include <boost/numeric/ublas/matrix_sparse.hpp>
#include <boost/numeric/ublas/matrix.hpp>

// Provide Forward Declarations
namespace Rcpp {

    namespace traits{
        // Setup non-intrusive extension via template specialization for
        // 'ublas' class boost::numeric::ublas
        // Support for wrap
        template <typename T> SEXP wrap(const boost::numeric::ublas::vector<T> & obj);
        // Support for as<T>
        template <typename T> class Exporter< boost::numeric::ublas::vector<T> >;

Stage 2 - Include the Rcpp.h header

It might seem frivolous to have a stage just to declare import order, but if Rcpp.h is included before the forward declaration then Rcpp::traits is not updated and we enter the abyss. Template programming can be delicate, respecting this include order is one of many small details one must get right.


// -------------- Stage 2: Including Rcpp.h

// ------ Place <Rcpp.h> AFTER the Forward Declaration!!!!

#include <Rcpp.h>

// ------ Place Implementations of Forward Declarations AFTER <Rcpp.h>!

Stage 3 - Implementing the Declarations

Now, we must actually implement the forward declarations. In particular, the only implementation that will be slightly problematic is the as<> since the wrap() is straight forward.


To implement wrap() we must appeal to a built-in type conversion index within Rcpp which is called Rcpp::traits::r_sexptype_traits<T>::rtype. From this, we are able to obtain an int containing the RTYPE and then construct an Rcpp::Vector. For the construction of a matrix, the same ideas hold true.


For as<>(), we need to consider the template that will be passed in. Furthermore, we setup a typedef directly underneath the Exporter class definition to easily define an OUT object to be used within the get() method. Outside of that, we use the same trick to move back and forth from a C++ T template type to an R type (implemented as one of several SEXP types).

In order to accomplish the as<>, or the direct port from R to C++, I had to do something dirty: I copied the vector contents. The code that governs this output is given within the get() of the Exporter class. You may wish to spend some time looking into changing the assignment using pointers perhaps. I am not very well versed with ublas so I did not see an easy approach to resolve the pointer pass.

// -------------- Stage 3: Implementation the Declarations

// Define template specializations for as<> and wrap
namespace Rcpp {

    namespace traits{
        // Defined wrap case
        template <typename T> SEXP wrap(const boost::numeric::ublas::vector<T> & obj){
            const int RTYPE = Rcpp::traits::r_sexptype_traits<T>::rtype ;
            return Rcpp::Vector< RTYPE >(obj.begin(), obj.end());
        // Defined as< > case
        template<typename T> class Exporter< boost::numeric::ublas::vector<T> > {
            typedef typename boost::numeric::ublas::vector<T> OUT ;
            // Convert the type to a valid rtype. 
            const static int RTYPE = Rcpp::traits::r_sexptype_traits< T >::rtype ;
            Rcpp::Vector<RTYPE> vec;
            Exporter(SEXP x) : vec(x) {
                if (TYPEOF(x) != RTYPE)
                    throw std::invalid_argument("Wrong R type for mapped 1D array");
            OUT get() {
                // Need to figure out a way to perhaps do a pointer pass?
                OUT x(vec.size());
                std::copy(vec.begin(), vec.end(), x.begin()); // have to copy data
                return x;

Stage 4 - Testing

Okay, let’s see if what we worked on paid off (spoiler It did! spoiler). To check, we should look at two different areas:

  1. Trace diagnostics within the function and;
  2. An automagic test.

Both of which are given below. Note that I’ve opted to shorten the ublas setup to just be:

// -------------- Stage 4: Testing

// Here we define a shortcut to the Boost ublas class to enable multiple ublas
// types via a template.
// ublas::vector<T> => ublas::vector<double>, ... , ublas::vector<int>
namespace ublas = ::boost::numeric::ublas;

Trace Diagnostics

// [[Rcpp::export]]
void containment_test(Rcpp::NumericVector x1) {
    Rcpp::Rcout << "Converting from Rcpp::NumericVector to ublas::vector<double>" << std::endl;

    // initialize the vector to all zero
    ublas::vector<double> x = Rcpp::as< ublas::vector<double> >(x1); 
    Rcpp::Rcout << "Running output test with ublas::vector<double>" << std::endl;
    for (unsigned i = 0; i < x.size (); ++ i)
        Rcpp::Rcout  << x(i) << std::endl;
    Rcpp::Rcout << "Converting from ublas::vector<double> to Rcpp::NumericVector" << std::endl;
    Rcpp::NumericVector test = Rcpp::wrap(x);
    Rcpp::Rcout << "Running output test with Rcpp::NumericVector" << std::endl;
    for (unsigned i = 0; i < test.size (); ++ i)
        Rcpp::Rcout  << test(i) << std::endl;

Test Call:



Converting from Rcpp::NumericVector to ublas::vector<double>
Running output test with ublas::vector<double>
Converting from ublas::vector<double> to Rcpp::NumericVector
Running output test with Rcpp::NumericVector

This test performed as expected. Onto the next test!

Automagic test

// [[Rcpp::export]]
ublas::vector<double> automagic_ublas_rcpp(ublas::vector<double> x1) {
    return x1;

Test Call:



[1] 1.0 2.0 3.2 1.2


Stage 5 - All together

Here is the combination of the above code chunks given by stage. If you copy and paste this into your .cpp file, then everything should work. If not, let me know.

// -------------- Stage 1: Forward Declarations with `RcppCommon.h`

#include <RcppCommon.h>

// Flags for C++ compiler: include Boost headers, use the C++11 standard

// [[Rcpp::depends(BH)]]
// [[Rcpp::plugins("cpp11")]]

// Third party library includes that provide the template class of ublas
#include <boost/numeric/ublas/matrix_sparse.hpp>
#include <boost/numeric/ublas/matrix.hpp>

// Provide Forward Declarations
namespace Rcpp {

    namespace traits{
        // Setup non-intrusive extension via template specialization for
        // 'ublas' class boost::numeric::ublas
        // Support for wrap
        template <typename T> SEXP wrap(const boost::numeric::ublas::vector<T> & obj);
        // Support for as<T>
        template <typename T> class Exporter< boost::numeric::ublas::vector<T> >;

// -------------- Stage 2: Including Rcpp.h

// ------ Place <Rcpp.h> AFTER the Forward Declaration!!!!

#include <Rcpp.h>

// ------ Place Implementations of Forward Declarations AFTER <Rcpp.h>!

// -------------- Stage 3: Implementation the Declarations

// Define template specializations for as<> and wrap
namespace Rcpp {

    namespace traits{
        // Defined wrap case
        template <typename T> SEXP wrap(const boost::numeric::ublas::vector<T> & obj){
            const int RTYPE = Rcpp::traits::r_sexptype_traits<T>::rtype ;
            return Rcpp::Vector< RTYPE >(obj.begin(), obj.end());
        // Defined as< > case
        template<typename T> class Exporter< boost::numeric::ublas::vector<T> > {
            typedef typename boost::numeric::ublas::vector<T> OUT ;
            // Convert the type to a valid rtype. 
            const static int RTYPE = Rcpp::traits::r_sexptype_traits< T >::rtype ;
            Rcpp::Vector<RTYPE> vec;
            Exporter(SEXP x) : vec(x) {
                if (TYPEOF(x) != RTYPE)
                    throw std::invalid_argument("Wrong R type for mapped 1D array");
            OUT get() {
                // Need to figure out a way to perhaps do a pointer pass?
                OUT x(vec.size());
                std::copy(vec.begin(), vec.end(), x.begin()); // have to copy data
                return x;

// -------------- Stage 4: Testing

// Here we define a shortcut to the Boost ublas class to enable multiple ublas
// types via a template.
// ublas::vector<T> => ublas::vector<double>, ... , ublas::vector<int>
namespace ublas = ::boost::numeric::ublas;

// [[Rcpp::export]]
void containment_test(Rcpp::NumericVector x1) {
    Rcpp::Rcout << "Converting from Rcpp::NumericVector to ublas::vector<double>" << std::endl;

    // initialize the vector to all zero
    ublas::vector<double> x = Rcpp::as< ublas::vector<double> >(x1); 
    Rcpp::Rcout << "Running output test with ublas::vector<double>" << std::endl;
    for (unsigned i = 0; i < x.size (); ++ i)
        Rcpp::Rcout  << x(i) << std::endl;
    Rcpp::Rcout << "Converting from ublas::vector<double> to Rcpp::NumericVector" << std::endl;
    Rcpp::NumericVector test = Rcpp::wrap(x);
    Rcpp::Rcout << "Running output test with Rcpp::NumericVector" << std::endl;
    for (unsigned i = 0; i < test.size (); ++ i)
        Rcpp::Rcout  << test(i) << std::endl;

// [[Rcpp::export]]
ublas::vector<double> automagic_ublas_rcpp(ublas::vector<double> x1) {
    return x1;

Closing Remarks

Whew… That was a lot. Hopefully, the above provided enough information as you may want to extend this post’s content past 1D vectors to perhaps a ublas::matrix and so on. In addition, then you now have the autoconvert magic of Rcpp for ublas::vector<double>! Moreover, all one needs to do is specify the either the parameters or return type of the function to be ublas::vector<double> – and Voilà, automagic conversion!

June 25, 2016 12:00 AM

June 23, 2016

RCpp Gallery

Working with Rcpp::StringVector

Vectors are fundamental containers in R. This makes them equally important in Rcpp. Vectors can be useful for storing multiple elements of a common class (e.g., integer, numeric, character). In Rcpp, vectors come in the form of NumericVector, CharacterVector, LogicalVector, StringVector and more. Look in the header file Rcpp/include/Rcpp/vector/instantiation.h for more types. Here we explore how to work with Rcpp::StringVector as a way to manage non-numeric data.

We typically interface with Rcpp by creating functions. There are several ways to include Rcpp functions in R. The examples here can be copied and pasted into a text file named ‘source.cpp’ and compiled with the command Rcpp::sourceCpp("source.cpp") made from the R command prompt.


Here we create a simple function which initializes an empty vector of three elements in size and returns it.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function() {
    Rcpp::StringVector myvector(3);
    return myvector;

We can call this function from R as follows.

x <- basic_function()
[1] "" "" ""

The first two lines are pretty much mandatory and you should copy and paste them into all your code until you understand them. The first line tells the program to use Rcpp. The second line exports this function for use, as opposed to keeping it as an internal function that is unavailable to users. Some people like to include using namespace Rcpp; to load the namespace. I prefer to use the scope operator (::) when calling functions. This is a matter of style and is therefore somewhat arbitrary. Whatever your perspective on this, its best to maintain consistency so that your code will be easier for others to understand.

We see that we’ve returned a vector of length three. We can also see that the default value is a string which contains nothing (“”). This is not a vector of NAs (missing data), even though NAs are supported by Rcpp::StringVector.

Accessing elements

The individual elements of a StringVector can be easily accessed. Here we’ll create an Rcpp function that accepts an Rcpp::StringVector as an argument. We’ll print the elements from within Rcpp. Then we’ll return the vector to R.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function( Rcpp::StringVector myvector ) {

    for( int i=0; i < myvector.size(); i++ ){
        Rcpp::Rcout << "i is: " << i << ", the element value is: " << myvector(i);
        Rcpp::Rcout << "\n";
    return myvector;

After we’ve compiled it we can call it from R.

x1 <- c("apple", "banana", "orange")
[1] "apple"  "banana" "orange"
x2 <- basic_function(x1)
i is: 0, the element value is: apple
i is: 1, the element value is: banana
i is: 2, the element value is: orange
[1] "apple"  "banana" "orange"

We see that the R vector contains the elements “apple”, “banana” and “orange.” Within Rcpp we print each element to standard out with Rcpp::Rcout << statements. And we see that these values are returned to the vector x2.

We’ve also introduced the method .size() which returns the number of elements in an object. This brings up an important difference among C++ and R. Many function names in R may contain periods. For example, the function name write.table() delimits words with a period. However, in C++ the period indicates a method. This means that C++ object names can’t include a period. Camel code or underscores are good alternatives.

There are at least two other important issues to learn from the above example. First, in R we typically access elements with square brackets. While some C++ objects are also accessed with square brackets, the Rcpp::StringVector is accessed with either parentheses or square brackets. In the case of the Rcpp::StringVector these appear interchangeable. However, be very careful, they are different in other containers. A second, but very important, difference between R and C++ is that in R the vectors are 1-based, meaning the first element is accessed with a 1. In C++ the vectors are zero-based, meaning the first element is accessed with a zero. This creates an opportunity for one-off errors. If you notice that the number of elements you’ve passed to C++ and back are off by one element, this would be something good to check.

Elements of elements

In C++, a std::string can be see as a vector of chars. Individual elements of a Rcpp::StringVector behave similarly. Accessing each element of a StringVector is similar to working with a std::string. Here we access each character of the second element of our StringVector.

#include <Rcpp.h>
// [[Rcpp::export]]
void basic_function(Rcpp::StringVector x) {
    for(int i=0; i < x[1].size(); i++){
        Rcpp::Rcout << "i is: " << i << ", element is: ";
        Rcpp::Rcout << x[1][i];
        Rcpp::Rcout << "\n";

And call the code from R.

x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
i is: 0, element is: b
i is: 1, element is: a
i is: 2, element is: n
i is: 3, element is: a
i is: 4, element is: n
i is: 5, element is: a

We see that we’ve accessed and printed the individual characters of the second element of the vector. We accomplish this by using the square brackets to access element one of the vector, and then use a second set of square brackets to access each character of this element.

Modifying elements

The modification of elements is fairly straight forward. We use the index (begining at zero) to modify the vector elements.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector x) {
    Rcpp::StringVector myvector = x;
    myvector[1] = "watermelon";
    myvector(2) = "kumquat";
    return myvector;
x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
[1] "apple"      "watermelon" "kumquat"   

We’ve successfully changed the second element from ‘banana’ to ‘watermelon’ and the third element from ‘orange’ to ‘kumquat.’ This also illustrates that Rcpp::StringVectors are flexible in their use of both square and round brackets. Trying that with standard library containers will usually result in an error.

In the above example we’ve passed an Rcpp::StringVector to a function and returned a new Rcpp::StringVector. By copying the container in this manner it may seem intuitive to work on it. If efficient use of memory is desired it is important to realize that pointers are being passed to the Rcpp function. This means we can create a function which returns void and modifies the elements we’re interested in modifying without the overhead of copying the container.

#include <Rcpp.h>
// [[Rcpp::export]]
void basic_function(Rcpp::StringVector x) {
    x(1) = "watermelon";
x1 <- c("apple", "banana", "orange")
[1] "apple"      "watermelon" "orange"    

Erasing elements

If we want to remove an element from a StringVector we can use the .erase() method.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector x) {
    Rcpp::StringVector myvector = x;
    return myvector;

And see our changes with R code.

x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
[1] "apple"  "orange"

We see that we’ve erased the second element from the array.

Growing and shrinking Rcpp::StringVectors

If you have an Rcpp::StringVector and you want to add elements, you can use the method .push_back(). While Rcpp has push functionality, it does not appear to have pop functionality. However, using techniques learned above, we could use object.erase(object.size()) to attain similar functionality. Here I illustrate their use to remove an element and then add two elements.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector x) {
    x.erase( x.size() - 1 ); // C++ vectors are zero based so remember the -1!
    for(int i=0; i < x.size(); i++){
        Rcpp::Rcout << "i is: " << i << ", the element value is: " << x(i);
        Rcpp::Rcout << "\n";
    return x;

And implement our example in R.

x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
i is: 0, the element value is: apple
i is: 1, the element value is: banana
[1] "apple"   "banana"  "avocado" "kumquat"

From the Rcpp output we see that we’ve removed the last element from the vector. We also see that we’ve added two elements to the ‘back’ of the vector.

If we want to add to the front of our vector we can accomplish that as well. There does not appear to be ‘push_front’ or ‘pop_front’ methods, but we have the tools necessary to accomplish these tasks. We use the erase and insert methods to push and pop to the front of our Rcpp::StringVector.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector x) {
    x.insert(0, "avocado");
    x.insert(0, "kumquat");
    return x;

And implement our example in R.

x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
[1] "kumquat" "avocado" "orange" 

In general, growing and shrinking data structures comes with a performance cost. And if you’re interested in Rcpp, you’re probably interested in performance. You’ll typically be better off setting a container size and sticking with it. But there are times when growing and shrinking your container can be really helpful. My recommendation is to use this functionality sparingly.

Missing data

In R we handle missing data with ‘NAs.’ In C++ the concept of missing data does not exist. Instead, some sort of placeholder, such as -999, has to be used. The Rcpp containers do support missing data to help make the interface between R and C++ easy. We can see this by continuing our existing example, but use it to set the second element as missing.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector myvector) {
    myvector(1) = NA_STRING;
    return myvector;
x1 <- c("apple", "banana", "orange")
x2 <- basic_function(x1)
[1] "apple"  NA       "orange"

Finding other methods

The Rcpp header files contain valuable information about objects defined in Rcpp. However, they’re rather technical and may not be very approachable to the novice. (This document is an attempt to help users bridge that gap between a novice and someone who reads headers.) If you don’t know where the header files are, you can use .libPaths() to list the locations of your libraries. In one of these locations you should find a directory called ‘Rcpp.’ Within this directory you should find a directory named ‘include’ which is where the headers are. For example, the header for the String object on my system is at: Rcpp/include/Rcpp/String.h

Type conversion

Once we have our data in an Rcpp function we may want to make use of the functionality of C++ containers. This will require us to convert our Rcpp container to another C++ form. Once we’ve processed these data we may want convert them back to Rcpp (so we can return them to R). A good example is converting an element of a StringVector to a std::string.

Implicit type conversion

Conversion from an Rcpp:StringVector to a std::string is a compatible conversion. This means we can accomplish this implicitly by simply setting the value of one container to the other.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector StringV_to_vstrings(Rcpp::StringVector StringV){
    std::vector<std::string> vstrings(StringV.size());
    int i;
    for (i = 0; i < StringV.size(); i++){
        vstrings[i] = StringV(i);

    Rcpp::StringVector StringV2(StringV.size());
    StringV2 = vstrings;
x1 <- c("apple", "banana", "orange")
x2 <- StringV_to_vstrings(x1)
[1] "apple"  "banana" "orange"

Note that while we have to load each element of the std::vector individually. However, the loading of the Rcpp::StringVector has been vectorized so that it works similar to R vectors.

Explicit type conversion

In some instances we may need explicit type conversion. Rcpp provides an ‘as’ method to accomplish this.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector StringV_to_vstrings(Rcpp::StringVector StringV){
    std::vector<std::string> vstrings(StringV.size());
    int i;
    for (i = 0; i < StringV.size(); i++){
        vstrings[i] = Rcpp::as< std::string >(StringV(i));

    Rcpp::StringVector StringV2(StringV.size());
    StringV2 = vstrings;
x1 <- c("apple", "banana", "orange")
x2 <- StringV_to_vstrings(x1)
[1] "apple"  "banana" "orange"

Type conversion is a lengthy topic and is frequently specific to the types which are being converted to and from. Hopefully this introduction is enough to get you started with the tools provided in Rcpp.


R objects include attributes which help describe the object. This is another concept that is absent in C++. Again, the Rcpp objects implement attributes to help us and to maintain a behavior that is similar to R.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector myvector) {
    std::vector< std::string > mystrings =  myvector.attr("names");
    mystrings[2] = "citrus";
    myvector.attr("names") = mystrings;
    return myvector;
x1 <- c("apple", "banana", "orange")
names(x1) <- c("pome", "berry", "hesperidium")
       pome       berry hesperidium 
    "apple"    "banana"    "orange" 
x2 <- basic_function(x1)
    pome    berry   citrus 
 "apple" "banana" "orange" 

Here we’ve stored the names of the Rcpp:StringVector in a std::vector of strings. We’ve then modified one of the elements and reset the names attribute with this changed vector. This illustrates the use of standard library containers along with those provided by Rcpp. But we need to be a little careful of what we’re doing here. If we store the values of our vector in a vector of std::string we lose our attributes because neither a std::vector or std::string has attributes.

#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::StringVector basic_function(Rcpp::StringVector myvector) {
    std::vector< std::string > mystrings(myvector.size()); 
    mystrings[0] = myvector(0);
    mystrings[1] = myvector(1);
    mystrings[2] = myvector(2);
    myvector = mystrings;
    return myvector;
x1 <- c("apple", "banana", "orange")
names(x1) <- c("pome", "berry", "hesperidium")
x2 <- basic_function(x1)
[1] "apple"  "banana" "orange"

Note that while we can assign a vector of strings to a Rcpp::StringVector we can not do the inverse. Instead we need to assign each element to the vector of strings. And we need to remember to keep our square brackets and round brackets associated with the correct data structure.

More information

Below are some links I’ve found useful in writing this document. Hopefully you’ll find them as gateways for your exploration of Rcpp.

Once you’ve crossed from R to C++ there are many of sources of information online. One of my favorites is included below.

June 23, 2016 12:00 AM

May 13, 2016

Journal of the Royal Statistical Society: Series A

February 28, 2016

Journal of the Royal Statistical Society: Series C

January 29, 2016

Journal of the Royal Statistical Society: Series B

December 27, 2015


R and Python: Gradient Descent

One of the problems often dealt in Statistics is minimization of the objective function. And contrary to the linear models, there is no analytical solution for models that are nonlinear on the parameters such as logistic regression, neural networks, and nonlinear regression models (like Michaelis-Menten model). In this situation, we have to use mathematical programming or optimization. And one popular optimization algorithm is the gradient descent, which we're going to illustrate here. To start with, let's consider a simple function with closed-form solution given by \begin{equation} f(\beta) \triangleq \beta^4 - 3\beta^3 + 2. \end{equation} We want to minimize this function with respect to $\beta$. The quick solution to this, as what calculus taught us, is to compute for the first derivative of the function, that is \begin{equation} \frac{\text{d}f(\beta)}{\text{d}\beta}=4\beta^3-9\beta^2. \end{equation} Setting this to 0 to obtain the stationary point gives us \begin{align} \frac{\text{d}f(\beta)}{\text{d}\beta}&\overset{\text{set}}{=}0\nonumber\\ 4\hat{\beta}^3-9\hat{\beta}^2&=0\nonumber\\ 4\hat{\beta}^3&=9\hat{\beta}^2\nonumber\\ 4\hat{\beta}&=9\nonumber\\ \hat{\beta}&=\frac{9}{4}. \end{align} The following plot shows the minimum of the function at $\hat{\beta}=\frac{9}{4}$ (red line in the plot below).

R ScriptNow let's consider minimizing this problem using gradient descent with the following algorithm:
  1. Initialize $\mathbf{x}_{r},r=0$
  2. while $\lVert \mathbf{x}_{r}-\mathbf{x}_{r+1}\rVert > \nu$
  3.         $\mathbf{x}_{r+1}\leftarrow \mathbf{x}_{r} - \gamma\nabla f(\mathbf{x}_r)$
  4.         $r\leftarrow r + 1$
  5. end while
  6. return $\mathbf{x}_{r}$ and $r$
where $\nabla f(\mathbf{x}_r)$ is the gradient of the cost function, $\gamma$ is the learning-rate parameter of the algorithm, and $\nu$ is the precision parameter. For the function above, let the initial guess be $\hat{\beta}_0=4$ and $\gamma=.001$ with $\nu=.00001$. Then $\nabla f(\hat{\beta}_0)=112$, so that \[\hat{\beta}_1=\hat{\beta}_0-.001(112)=3.888.\] And $|\hat{\beta}_1 - \hat{\beta}_0| = 0.112> \nu$. Repeat the process until at some $r$, $|\hat{\beta}_{r}-\hat{\beta}_{r+1}| \ngtr \nu$. It will turn out that 350 iterations are needed to satisfy the desired inequality, the plot of which is in the following figure with estimated minimum $\hat{\beta}_{350}=2.250483\approx\frac{9}{4}$.

R Script with PlotPython ScriptObviously the convergence is slow, and we can adjust this by tuning the learning-rate parameter, for example if we try to increase it into $\gamma=.01$ (change gamma to .01 in the codes above) the algorithm will converge at 42nd iteration. To support that claim, see the steps of its gradient in the plot below.

If we try to change the starting value from 4 to .1 (change beta_new to .1) with $\gamma=.01$, the algorithm converges at 173rd iteration with estimate $\hat{\beta}_{173}=2.249962\approx\frac{9}{4}$ (see the plot below).

Now let's consider another function known as Rosenbrock defined as \begin{equation} f(\mathbf{w})\triangleq(1 - w_1) ^ 2 + 100 (w_2 - w_1^2)^2. \end{equation} The gradient is \begin{align} \nabla f(\mathbf{w})&=[-2(1 - w_1) - 400(w_2 - w_1^2) w_1]\mathbf{i}+200(w_2-w_1^2)\mathbf{j}\nonumber\\ &=\left[\begin{array}{c} -2(1 - w_1) - 400(w_2 - w_1^2) w_1\\ 200(w_2-w_1^2) \end{array}\right]. \end{align} Let the initial guess be $\hat{\mathbf{w}}_0=\left[\begin{array}{c}-1.8\\-.8\end{array}\right]$, $\gamma=.0002$, and $\nu=.00001$. Then $\nabla f(\hat{\mathbf{w}}_0)=\left[\begin{array}{c} -2914.4\\-808.0\end{array}\right]$. So that \begin{equation}\nonumber \hat{\mathbf{w}}_1=\hat{\mathbf{w}}_0-\gamma\nabla f(\hat{\mathbf{w}}_0)=\left[\begin{array}{c} -1.21712 \\-0.63840\end{array}\right]. \end{equation} And $\lVert\hat{\mathbf{w}}_0-\hat{\mathbf{w}}_1\rVert=0.6048666>\nu$. Repeat the process until at some $r$, $\lVert\hat{\mathbf{w}}_r-\hat{\mathbf{w}}_{r+1}\rVert\ngtr \nu$. It will turn out that 23,374 iterations are needed for the desired inequality with estimate $\hat{\mathbf{w}}_{23375}=\left[\begin{array}{c} 0.9464841 \\0.8956111\end{array}\right]$, the contour plot is depicted in the figure below.
R Script with Contour PlotPython ScriptNotice that I did not use ggplot for the contour plot, this is because the plot needs to be updated 23,374 times just to accommodate for the arrows for the trajectory of the gradient vectors, and ggplot is just slow. Finally, we can also visualize the gradient points on the surface as shown in the following figure.
R ScriptIn my future blog post, I hope to apply this algorithm on statistical models like linear/nonlinear regression models for simple illustration.

by Al Asaad ( at December 27, 2015 01:52 AM

R: Principal Component Analysis on Imaging

Ever wonder what's the mathematics behind face recognition on most gadgets like digital camera and smartphones? Well for most part it has something to do with statistics. One statistical tool that is capable of doing such feature is the Principal Component Analysis (PCA). In this post, however, we will not do (sorry to disappoint you) face recognition as we reserve this for future post while I'm still doing research on it. Instead, we go through its basic concept and use it for data reduction on spectral bands of the image using R.

Let's view it mathematically

Consider a line $L$ in a parametric form described as a set of all vectors $k\cdot\mathbf{u}+\mathbf{v}$ parameterized by $k\in \mathbb{R}$, where $\mathbf{v}$ is a vector orthogonal to a normalized vector $\mathbf{u}$. Below is the graphical equivalent of the statement:
So if given a point $\mathbf{x}=[x_1,x_2]^T$, the orthogonal projection of this point on the line $L$ is given by $(\mathbf{u}^T\mathbf{x})\mathbf{u}+\mathbf{v}$. Graphically, we mean

$Proj$ is the projection of the point $\mathbf{x}$ on the line, where the position of it is defined by the scalar $\mathbf{u}^{T}\mathbf{x}$. Therefore, if we consider $\mathbf{X}=[X_1, X_2]^T$ be a random vector, then the random variable $Y=\mathbf{u}^T\mathbf{X}$ describes the variability of the data on the direction of the normalized vector $\mathbf{u}$. So that $Y$ is a linear combination of $X_i, i=1,2$. The principal component analysis identifies a linear combinations of the original variables $\mathbf{X}$ that contain most of the information, in the sense of variability, contained in the data. The general assumption is that useful information is proportional to the variability. PCA is used for data dimensionality reduction and for interpretation of data. (Ref 1. Bajorski, 2012)

To better understand this, consider two dimensional data set, below is the plot of it along with two lines ($L_1$ and $L_2$) that are orthogonal to each other:
If we project the points orthogonally to both lines we have,

So that if normalized vector $\mathbf{u}_1$ defines the direction of $L_1$, then the variability of the points on $L_1$ is described by the random variable $Y_1=\mathbf{u}_1^T\mathbf{X}$. Also if $\mathbf{u}_2$ is a normalized vector that defines the direction of $L_2$, then the variability of the points on this line is described by the random variable $Y_2=\mathbf{u}_2^T\mathbf{X}$. The first principal component is one with maximum variability. So in this case, we can see that $Y_2$ is more variable than $Y_1$, since the points projected on $L_2$ are more dispersed than in $L_1$. In practice, however, the linear combinations $Y_i = \mathbf{u}_i^T\mathbf{X}, i=1,2,\cdots,p$ is maximized sequentially so that $Y_1$ is the linear combination of the first principal component, $Y_2$ is the linear combination of the second principal component, and so on. Further, the estimate of the direction vector $\mathbf{u}$ is simply the normalized eigenvector $\mathbf{e}$ of the variance-covariance matrix $\mathbf{\Sigma}$ of the original variable $\mathbf{X}$. And the variability explained by the principal component is the corresponding eigenvalue $\lambda$. For more details on theory of PCA refer to (Bajorski, 2012) at Reference 1 below.

As promised we will do dimensionality reduction using PCA. We will use the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data from (Barjorski, 2012), you can use other locations of AVIRIS data that can be downloaded here. However, since for most cases the AVIRIS data contains thousands of bands so for simplicity we will stick with the data given in (Bajorski, 2012) as it was cleaned reducing to 152 bands only.

What is spectral bands?

In imaging, spectral bands refer to the third dimension of the image usually denoted as $\lambda$. For example, RGB image contains red, green and blue bands as shown below along with the first two dimensions $x$ and $y$ that define the resolution of the image.

These are few of the bands that are visible to our eyes, there are other bands that are not visible to us like infrared, and many other in electromagnetic spectrum. That is why in most cases AVIRIS data contains huge number of bands each captures different characteristics of the image. Below is the proper description of the data.


The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), is a sensor collecting spectral radiance in the range of wavelengths from 400 to 2500 nm. It has been flown on various aircraft platforms, and many images of the Earth’s surface are available. A 100 by 100 pixel AVIRIS image of an urban area in Rochester, NY, near the Lake Ontario shoreline is shown below. The scene has a wide range of natural and man-made material including a mixture of commercial/warehouse and residential neighborhoods, which adds a wide range of spectral diversity. Prior to processing, invalid bands (due to atmospheric water absorption) were removed, reducing the overall dimensionality to 152 bands. This image has been used in Bajorski et al. (2004) and Bajorski (2011a, 2011b). The first 152 values in the AVIRIS Data represent the spectral radiance values (a spectral curve) for the top left pixel. This is followed by spectral curves of the pixels in the first row, followed by the next row, and so on. (Ref. 1 Bajorski, 2012)

To load the data, run the following code:

Above code uses EBImage package, and can be installed from my previous post.

Why do we need to reduce the dimension of the data?

Before we jump in to our analysis, in case you may ask why? Well sometimes it's just difficult to do analysis on high dimensional data, especially on interpreting it. This is because there are dimensions that aren't significant (like redundancy) which adds to our problem on the analysis. So in order to deal with this, we remove those nuisance dimension and deal with the significant one.

To perform PCA in R, we use the function princomp as seen below:

The structure of princomp consist of a list shown above, we will give description to selected outputs. Others can be found in the documentation of the function by executing ?princomp.
  • sdev - standard deviation, the square root of the eigenvalues $\lambda$ of the variance-covariance matrix $\mathbf{\Sigma}$ of the data, dat.mat;
  • loadings - eigenvectors $\mathbf{e}$ of the variance-covariance matrix $\mathbf{\Sigma}$ of the data, dat.mat;
  • scores - the principal component scores.
Recall that the objective of PCA is to find for a linear combination $Y=\mathbf{u}^T\mathbf{X}$ that will maximize the variance $Var(Y)$. So that from the output, the estimate of the components of $\mathbf{u}$ is the entries of the loadings which is a matrix of eigenvectors, where the columns corresponds to the eigenvectors of the sequence of principal components, that is if the first principal component is given by $Y_1=\mathbf{u}_1^T\mathbf{X}$, then the estimate of $\mathbf{u}_1$ which is $\mathbf{e}_1$ (eigenvector) is the set of coefficients obtained from the first column of the loadings. The explained variability of the first principal component is the square of the first standard deviation sdev, the explained variability of the second principal component is the square of the second standard deviation sdev, and so on. Now let's interpret the loadings (coefficients) of the first three principal components. Below is the plot of this,
Base above, the coefficients of the first principal component (PC1) are almost all negative. A closer look, the variability in this principal component is mainly explained by the weighted average of radiance of the spectral bands 35 to 100. Analogously, PC2 mainly represents the variability of the weighted average of radiance of spectral bands 1 to 34. And further, the fluctuation of the coefficients of PC3 makes it difficult to tell on which bands greatly contribute on its variability. Aside from examining the loadings, another way to see the impact of the PCs is through the impact plot where the impact curve $\sqrt{\lambda_j}\mathbf{e}_j$ are plotted, I want you to explore that.

Moving on, let's investigate the percent of variability in $X_i$ explained by the $j$th principal component, below is the formula of this, \begin{equation}\nonumber \frac{\lambda_j\cdot e_{ij}^2}{s_{ii}}, \end{equation} where $s_{ii}$ is the estimated variance of $X_i$. So that below is the percent of explained variability in $X_i$ of the first three principal components including the cumulative percent variability (sum of PC1, PC2, and PC3),
For the variability of the first 33 bands, PC2 takes on about 90 percent of the explained variability as seen in the above plot. And still have great contribution further to 102 to 152 bands. On the other hand, from bands 37 to 100, PC1 explains almost all the variability with PC2 and PC3 explain 0 to 1 percent only. The sum of the percentage of explained variability of these principal components is indicated as orange line in the above plot, which is the cumulative percent variability.

To wrap up this section, here is the percentage of the explained variability of the first 10 PCs.

Table 1: Variability Explained by the First Ten Principal Components for the AVIRIS data.

Above variability were obtained by noting that the variability explained by the principal component is simply the eigenvalue (square of the sdev) of the variance-covariance matrix $\mathbf{\Sigma}$ of the original variable $\mathbf{X}$, hence the percentage of variability explained by the $j$th PC is equal to its corresponding eigenvalue $\lambda_j$ divided by the overall variability which is the sum of the eigenvalues, $\sum_{j=1}^{p}\lambda_j$, as we see in the following code,

Stopping Rules

Given the list of percentage of variability explained by the PCs in Table 1, how many principal components should we take into account that would best represent the variability of the original data? To answer that, we introduce the following stopping rules that will guide us on deciding the number of PCs:
  1. Scree plot;
  2. Simple fare-share;
  3. Broken-stick; and,
  4. Relative broken-stick.
The scree plot is the plot of the variability of the PCs, that is the plot of the eigenvalues. Where we look for an elbow or sudden drop of the eigenvalues on the plot, hence for our example we have
Therefore, we need return the first two principal components based on the elbow shape. However, if the eigenvalues differ by order of magnitude, it is recommended to use the logarithmic scale which is illustrated below,
Unfortunately, sometimes it won't work as we can see here, it's just difficult to determine where the elbow is. The succeeding discussions on the last three stopping rules are based on (Bajorski, 2012). The simple fair-share stopping rule identifies the largest $k$ such that $\lambda_k$ is larger than its fair share, that is larger than $(\lambda_1+\lambda_2+\cdots+\lambda_p)/p$. To illustrate this, consider the following:

Thus, we need to stop at second principal component.

If one was concerned that the above method produces too many principal components, a broken-stick rule could be used. The rule is that it identifies the principal components with largest $k$ such that $\lambda_j/(\lambda_1+\lambda_2+\cdots +\lambda_p)>a_j$, for all $j\leq k$, where \begin{equation}\nonumber a_j = \frac{1}{p}\sum_{i=j}^{p}\frac{1}{i},\quad j =1,\cdots, p. \end{equation} Let's try it,

Above result coincides with the first two stopping rule. The draw back of simple fair-share and broken-stick rules is that it do not work well when the eigenvalues differ by orders of magnitude. In such case, we then use the relative broken-stick rule, where we analyze $\lambda_j$ as the first eigenvalue in the set $\lambda_j\geq \lambda_{j+1}\geq\cdots\geq\lambda_{p}$, where $j < p$. The dimensionality $k$ is chosen as the largest value such that $\lambda_j/(\lambda_j+\cdots +\lambda_p)>b_j$, for all $j\leq k$, where \begin{equation}\nonumber b_j = \frac{1}{p-j+1}\sum_{i=1}^{p-j+1}\frac{1}{i}. \end{equation} Applying this to the data we have,
According to the numerical output, the first 34 principal components are enough to represent the variability of the original data.

Principal Component Scores

The principal component scores is the resulting new data set obtained from the linear combinations $Y_j=\mathbf{e}_j(\mathbf{x}-\bar{\mathbf{x}}), j = 1,\cdots, p$. So that if we use the first three stopping rules, then below is the scores (in image) of PC1 and PC2,
If we base on the relative broken-stick rule then we return the first 34 PCs, and below is the corresponding scores (in image).
Click on the image to zoom in.

Residual Analysis

Of course when doing PCA there are errors to be considered unless one would return all the PCs, but that would not make any sense because why would someone apply PCA when you still take into account all the dimensions? An overview of the errors in PCA without going through the theory is that, the overall error is simply the excluded variability explained by the $k$th to $p$th principal components, $k>j$.


by Al Asaad ( at December 27, 2015 01:52 AM

R: k-Means Clustering on Imaging

Enough with the theory we recently published, let's take a break and have fun on the application of Statistics used in Data Mining and Machine Learning, the k-Means Clustering.
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. (Wikipedia, Ref 1.)
We will apply this method to an image, wherein we group the pixels into k different clusters. Below is the image that we are going to use,
Colorful Bird From Wall321
We will utilize the following packages for input and output:
  1. jpeg - Read and write JPEG images; and,
  2. ggplot2 - An implementation of the Grammar of Graphics.

Download and Read the Image

Let's get started by downloading the image to our workspace, and tell R that our data is a JPEG file.

Cleaning the Data

Extract the necessary information from the image and organize this for our computation:

The image is represented by large array of pixels with dimension rows by columns by channels -- red, green, and blue or RGB.


Plot the original image using the following codes:


Apply k-Means clustering on the image:

Plot the clustered colours:

Possible clusters of pixels on different k-Means:

Originalk = 6
Table 1: Different k-Means Clustering.
k = 5k = 4
k = 3k = 2

I suggest you try it!


  1. K-means clustering. Wikipedia. Retrieved September 11, 2014.

by Al Asaad ( at December 27, 2015 01:52 AM

December 16, 2015


R and Python: Theory of Linear Least Squares

In my previous article, we talked about implementations of linear regression models in R, Python and SAS. On the theoretical sides, however, I briefly mentioned the estimation procedure for the parameter $\boldsymbol{\beta}$. So to help us understand how software does the estimation procedure, we'll look at the mathematics behind it. We will also perform the estimation manually in R and in Python, that means we're not going to use any special packages, this will help us appreciate the theory.

Linear Least Squares

Consider the linear regression model, \[ y_i=f_i(\mathbf{x}|\boldsymbol{\beta})+\varepsilon_i,\quad\mathbf{x}_i=\left[ \begin{array}{cccc} 1&x_{11}&\cdots&x_{1p} \end{array}\right],\quad\boldsymbol{\beta}=\left[\begin{array}{c}\beta_0\\\beta_1\\\vdots\\\beta_p\end{array}\right], \] where $y_i$ is the response or the dependent variable at the $i$th case, $i=1,\cdots, N$. The $f_i(\mathbf{x}|\boldsymbol{\beta})$ is the deterministic part of the model that depends on both the parameters $\boldsymbol{\beta}\in\mathbb{R}^{p+1}$ and the predictor variable $\mathbf{x}_i$, which in matrix form, say $\mathbf{X}$, is represented as follows \[ \mathbf{X}=\left[ \begin{array}{cccccc} 1&x_{11}&\cdots&x_{1p}\\ 1&x_{21}&\cdots&x_{2p}\\ \vdots&\vdots&\ddots&\vdots\\ 1&x_{N1}&\cdots&x_{Np}\\ \end{array} \right]. \] $\varepsilon_i$ is the error term at the $i$th case which we assumed to be Gaussian distributed with mean 0 and variance $\sigma^2$. So that \[ \mathbb{E}y_i=f_i(\mathbf{x}|\boldsymbol{\beta}), \] i.e. $f_i(\mathbf{x}|\boldsymbol{\beta})$ is the expectation function. The uncertainty around the response variable is also modelled by Gaussian distribution. Specifically, if $Y=f(\mathbf{x}|\boldsymbol{\beta})+\varepsilon$ and $y\in Y$ such that $y>0$, then \begin{align*} \mathbb{P}[Y\leq y]&=\mathbb{P}[f(x|\beta)+\varepsilon\leq y]\\ &=\mathbb{P}[\varepsilon\leq y-f(\mathbf{x}|\boldsymbol{\beta})]=\mathbb{P}\left[\frac{\varepsilon}{\sigma}\leq \frac{y-f(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]\\ &=\Phi\left[\frac{y-f(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right], \end{align*} where $\Phi$ denotes the Gaussian distribution with density denoted by $\phi$ below. Hence $Y\sim\mathcal{N}(f(\mathbf{x}|\boldsymbol{\beta}),\sigma^2)$. That is, \begin{align*} \frac{\operatorname{d}}{\operatorname{d}y}\Phi\left[\frac{y-f(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]&=\phi\left[\frac{y-f(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]\frac{1}{\sigma}=\mathbb{P}[y|f(\mathbf{x}|\boldsymbol{\beta}),\sigma^2]\\ &=\frac{1}{\sqrt{2\pi}\sigma}\exp\left\{-\frac{1}{2}\left[\frac{y-f(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]^2\right\}. \end{align*} If the data are independent and identically distributed, then the log-likelihood function of $y$ is, \begin{align*} \mathcal{L}[\boldsymbol{\beta}|\mathbf{y},\mathbf{X},\sigma]&=\mathbb{P}[\mathbf{y}|\mathbf{X},\boldsymbol{\beta},\sigma]=\prod_{i=1}^N\frac{1}{\sqrt{2\pi}\sigma}\exp\left\{-\frac{1}{2}\left[\frac{y_i-f_i(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]^2\right\}\\ &=\frac{1}{(2\pi)^{\frac{n}{2}}\sigma^n}\exp\left\{-\frac{1}{2}\sum_{i=1}^N\left[\frac{y_i-f_i(\mathbf{x}|\boldsymbol{\beta})}{\sigma}\right]^2\right\}\\ \log\mathcal{L}[\boldsymbol{\beta}|\mathbf{y},\mathbf{X},\sigma]&=-\frac{n}{2}\log2\pi-n\log\sigma-\frac{1}{2\sigma^2}\sum_{i=1}^N\left[y_i-f_i(\mathbf{x}|\boldsymbol{\beta})\right]^2. \end{align*} And because the likelihood function tells us about the plausibility of the parameter $\boldsymbol{\beta}$ in explaining the sample data. We therefore want to find the best estimate of $\boldsymbol{\beta}$ that likely generated the sample. Thus our goal is to maximize the likelihood function which is equivalent to maximizing the log-likelihood with respect to $\boldsymbol{\beta}$. And that's simply done by taking the partial derivative with respect to the parameter $\boldsymbol{\beta}$. Therefore, the first two terms in the right hand side of the equation above can be disregarded since it does not depend on $\boldsymbol{\beta}$. Also, the location of the maximum log-likelihood with respect to $\boldsymbol{\beta}$ is not affected by arbitrary positive scalar multiplication, so the factor $\frac{1}{2\sigma^2}$ can be omitted. And we are left with the following equation, \begin{equation}\label{eq:1} -\sum_{i=1}^N\left[y_i-f_i(\mathbf{x}|\boldsymbol{\beta})\right]^2. \end{equation} One last thing is that, instead of maximizing the log-likelihood function we can do minimization on the negative log-likelihood. Hence we are interested on minimizing the negative of Equation (\ref{eq:1}) which is \begin{equation}\label{eq:2} \sum_{i=1}^N\left[y_i-f_i(\mathbf{x}|\boldsymbol{\beta})\right]^2, \end{equation} popularly known as the residual sum of squares (RSS). So RSS is a consequence of maximum log-likelihood under the Gaussian assumption of the uncertainty around the response variable $y$. For models with two parameters, say $\beta_0$ and $\beta_1$ the RSS can be visualized like the one in my previous article, that is
Error Surface
Performing differentiation under $(p+1)$-dimensional parameter $\boldsymbol{\beta}$ is manageable in the context of linear algebra, so Equation (\ref{eq:2}) is equivalent to \begin{align*} \lVert\mathbf{y}-\mathbf{X}\boldsymbol{\beta}\rVert^2&=\langle\mathbf{y}-\mathbf{X}\boldsymbol{\beta},\mathbf{y}-\mathbf{X}\boldsymbol{\beta}\rangle=\mathbf{y}^{\text{T}}\mathbf{y}-\mathbf{y}^{\text{T}}\mathbf{X}\boldsymbol{\beta}-(\mathbf{X}\boldsymbol{\beta})^{\text{T}}\mathbf{y}+(\mathbf{X}\boldsymbol{\beta})^{\text{T}}\mathbf{X}\boldsymbol{\beta}\\ &=\mathbf{y}^{\text{T}}\mathbf{y}-\mathbf{y}^{\text{T}}\mathbf{X}\boldsymbol{\beta}-\boldsymbol{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{y}+\boldsymbol{\beta}^{\text{T}}\mathbf{X}^{\text{T}}\mathbf{X}\boldsymbol{\beta} \end{align*} And the derivative with respect to the parameter is \begin{align*} \frac{\operatorname{\partial}}{\operatorname{\partial}\boldsymbol{\beta}}\lVert\mathbf{y}-\mathbf{X}\boldsymbol{\beta}\rVert^2&=-2\mathbf{X}^{\text{T}}\mathbf{y}+2\mathbf{X}^{\text{T}}\mathbf{X}\boldsymbol{\beta} \end{align*} Taking the critical point by setting the above equation to zero vector, we have \begin{align} \frac{\operatorname{\partial}}{\operatorname{\partial}\boldsymbol{\beta}}\lVert\mathbf{y}-\mathbf{X}\hat{\boldsymbol{\beta}}\rVert^2&\overset{\text{set}}{=}\mathbf{0}\nonumber\\ -\mathbf{X}^{\text{T}}\mathbf{y}+\mathbf{X}^{\text{T}}\mathbf{X}\hat{\boldsymbol{\beta}}&=\mathbf{0}\nonumber\\ \mathbf{X}^{\text{T}}\mathbf{X}\hat{\boldsymbol{\beta}}&=\mathbf{X}^{\text{T}}\mathbf{y}\label{eq:norm} \end{align} Equation (\ref{eq:norm}) is called the normal equation. If $\mathbf{X}$ is full rank, then we can compute the inverse of $\mathbf{X}^{\text{T}}\mathbf{X}$, \begin{align} \mathbf{X}^{\text{T}}\mathbf{X}\hat{\boldsymbol{\beta}}&=\mathbf{X}^{\text{T}}\mathbf{y}\nonumber\\ (\mathbf{X}^{\text{T}}\mathbf{X})^{-1}\mathbf{X}^{\text{T}}\mathbf{X}\hat{\boldsymbol{\beta}}&=(\mathbf{X}^{\text{T}}\mathbf{X})^{-1}\mathbf{X}^{\text{T}}\mathbf{y}\nonumber\\ \hat{\boldsymbol{\beta}}&=(\mathbf{X}^{\text{T}}\mathbf{X})^{-1}\mathbf{X}^{\text{T}}\mathbf{y}.\label{eq:betahat} \end{align} That's it, since both $\mathbf{X}$ and $\mathbf{y}$ are known.


If $\mathbf{X}$ is full rank and spans the subspace $V\subseteq\mathbb{R}^N$, where $\mathbb{E}\mathbf{y}=\mathbf{X}\boldsymbol{\beta}\in V$. Then the predicted values of $\mathbf{y}$ is given by, \begin{equation}\label{eq:pred} \hat{\mathbf{y}}=\mathbb{E}\mathbf{y}=\mathbf{P}_{V}\mathbf{y}=\mathbf{X}(\mathbf{X}^{\text{T}}\mathbf{X})^{-1}\mathbf{X}^{\text{T}}\mathbf{y}, \end{equation} where $\mathbf{P}$ is the projection matrix onto the space $V$. For proof of the projection matrix in Equation (\ref{eq:pred}) please refer to reference (1) below. Notice that this is equivalent to \begin{equation}\label{eq:yhbh} \hat{\mathbf{y}}=\mathbb{E}\mathbf{y}=\mathbf{X}\hat{\boldsymbol{\beta}}. \end{equation}


Let's fire up R and Python and see how we can apply those equations we derived. For purpose of illustration, we're going to simulate data from Gaussian distributed population. To do so, consider the following codes

R ScriptPython ScriptHere we have two predictors x1 and x2, and our response variable y is generated by the parameters $\beta_1=3.5$ and $\beta_2=2.8$, and it has Gaussian noise with variance 7. While we set the same random seeds for both R and Python, we should not expect the random values generated in both languages to be identical, instead both values are independent and identically distributed (iid). For visualization, I will use Python Plotly, you can also translate it to R Plotly.

Now let's estimate the parameter $\boldsymbol{\beta}$ which by default we set to $\beta_1=3.5$ and $\beta_2=2.8$. We will use Equation (\ref{eq:betahat}) for estimation. So that we have

R ScriptPython ScriptThat's a good estimate, and again just a reminder, the estimate in R and in Python are different because we have different random samples, the important thing is that both are iid. To proceed, we'll do prediction using Equations (\ref{eq:pred}). That is,

R ScriptPython ScriptThe first column above is the data y and the second column is the prediction due to Equation (\ref{eq:pred}). Thus if we are to expand the prediction into an expectation plane, then we have

You have to rotate the plot by the way to see the plane, I still can't figure out how to change it in Plotly. Anyway, at this point we can proceed computing for other statistics like the variance of the error, and so on. But I will leave it for you to explore. Our aim here is just to give us an understanding on what is happening inside the internals of our software when we try to estimate the parameters of the linear regression models.


  1. Arnold, Steven F. (1981). The Theory of Linear Models and Multivariate Analysis. Wiley.
  2. OLS in Matrix Form

by Al Asaad ( at December 16, 2015 11:02 AM

May 12, 2015

Chris Lawrence

That'll leave a mark

Here’s a phrase you never want to see in print (in a legal decision, no less) pertaining to your academic research: “The IRB process, however, was improperly engaged by the Dartmouth researcher and ignored completely by the Stanford researchers.”

Whole thing here; it’s a doozy.

by Chris Lawrence at May 12, 2015 12:00 AM

April 14, 2015

R you ready?

Beautiful plots while simulating loss in two-part procrustes problem

loss2Today I was working on a two-part procrustes problem and wanted to find out why my minimization algorithm sometimes does not converge properly or renders unexpected results. The loss function to be minimized is

\displaystyle  L(\mathbf{Q},c) = \| c \mathbf{A_1Q} - \mathbf{B_1} \|^2 + \| \mathbf{A_2Q} - \mathbf{B_2} \|^2 \rightarrow min

with \| \cdot \| denoting the Frobenius norm, c is an unknown scalar and \mathbf{Q} an unknown rotation matrix, i.e. \mathbf{Q}^T\mathbf{Q}=\mathbf{I}. \;\mathbf{A_1}, \mathbf{A_2}, \mathbf{B_1}, and \mathbf{B_1} are four real valued matrices. The minimum for c is easily found by setting the partial derivation of L(\mathbf{Q},c) w.r.t c equal to zero.

\displaystyle  c = \frac {tr \; \mathbf{Q}^T \mathbf{A_1}^T \mathbf{B_1}}  { \| \mathbf{A_1} \|^2 }

By plugging c into the loss function L(\mathbf{Q},c) we get a new loss function L(\mathbf{Q}) that only depends on \mathbf{Q}. This is the starting situation.

When trying to find out why the algorithm to minimize L(\mathbf{Q}) did not work as expected, I got stuck. So I decided to conduct a small simulation and generate random rotation matrices to study the relation between the parameter c and the value of the loss function L(\mathbf{Q}). Before looking at the results for the entire two-part procrustes problem from above, let’s visualize the results for the first part of the loss function only, i.e.

\displaystyle  L(\mathbf{Q},c) = \| c \mathbf{A_1Q} - \mathbf{B_1} \|^2 \rightarrow min

Here, c has the same minimum as for the whole formula above. For the simulation I used

\mathbf{A_1}= \begin{pmatrix}  0.0 & 0.4 & -0.5 \\  -0.4 & -0.8 & -0.5 \\  -0.1 & -0.5 & 0.2 \\  \end{pmatrix} \mkern18mu \qquad \text{and} \qquad \mkern36mu \mathbf{B_1}= \begin{pmatrix}  -0.1 & -0.8 & -0.1 \\  0.3 & 0.2 & -0.9 \\  0.1 & -0.3 & -0.5 \\  \end{pmatrix}

as input matrices. Generating many random rotation matrices \mathbf{Q} and plotting c against the value of the loss function yields the following plot.

This is a well behaved relation, for each scaling parameter c the loss is identical. Now let’s look at the full two-part loss function. As input matrices I used

\displaystyle  A1= \begin{pmatrix}  0.0 & 0.4 & -0.5 \\  -0.4 & -0.8 & -0.5 \\  -0.1 & -0.5 & 0.2 \\  \end{pmatrix} \mkern18mu , \mkern36mu B1= \begin{pmatrix}  -0.1 & -0.8 & -0.1 \\  0.3 & 0.2 & -0.9 \\  0.1 & -0.3 & -0.5 \\  \end{pmatrix}
A2= \begin{pmatrix}  0 & 0 & 1 \\  1 & 0 & 0 \\  0 & 1 & 0 \\  \end{pmatrix} \mkern18mu , \mkern36mu B2= \begin{pmatrix}  0 & 0 & 1 \\  1 & 0 & 0 \\  0 & 1 & 0 \\  \end{pmatrix}

and the following R-code.

# trace function
tr <- function(X) sum(diag(X))

# random matrix type 1
rmat_1 <- function(n=3, p=3, min=-1, max=1){
  matrix(runif(n*p, min, max), ncol=p)

# random matrix type 2, sparse
rmat_2 <- function(p=3) {
  diag(p)[, sample(1:p, p)]

# generate random rotation matrix Q. Based on Q find 
# optimal scaling factor c and calculate loss function value
one_sample <- function(n=2, p=2)
  Q <- mixAK::rRotationMatrix(n=1, dim=p) %*%         # random rotation matrix det(Q) = 1
    diag(sample(c(-1,1), p, rep=T))                   # additional reflections, so det(Q) in {-1,1}
  s <- tr( t(Q) %*% t(A1) %*% B1 ) / norm(A1, "F")^2  # scaling factor c
  rss <- norm(s*A1 %*% Q - B1, "F")^2 +               # get residual sum of squares
         norm(A2 %*% Q - B2, "F")^2 
  c(s=s, rss=rss)

# find c and rss or many random rotation matrices
set.seed(10)  # nice case for 3 x 3
n <- 3
p <- 3
A1 <- round(rmat_1(n, p), 1)
B1 <- round(rmat_1(n, p), 1)
A2 <- rmat_2(p)
B2 <- rmat_2(p)

x <- plyr::rdply(40000, one_sample(3,3)) 
plot(x$s, x$rss, pch=16, cex=.4, xlab="c", ylab="L(Q)", col="#00000010")

This time the result turns out to be very different and … beautiful :)

Here, we do not have a one to one relation between the scaling parameter and the loss function any more. I do not quite know what to make of this yet. But for now I am happy that it has aestethic value. Below you find some more beautiful graphics with different matrices as inputs.


by markheckmann at April 14, 2015 04:53 PM

February 24, 2015

Douglas Bates

RCall: Running an embedded R in Julia

I have used R (and S before it) for a couple of decades. In the last few years most of my coding has been in Julia, a language for technical computing that can provide remarkable performance for a dynamically typed language via Just-In-Time (JIT) compilation of functions and via multiple dispatch.

Nonetheless there are facilities in R that I would like to have access to from Julia. I created the RCall package for Julia to do exactly that. This IJulia notebook provides an introduction to RCall.

This is not a novel idea by any means. Julia already has PyCall and JavaCall packages that provide access to Python and to Java. These packages are used extensively and are much more sophisticated than RCall, at present. Many other languages have facilities to run an embedded instance of R. In fact, Python has several such interfaces.

The things I plan to do using RCall is to access datasets from R and R packages, to fit models that are not currently implemented in Julia and to use R graphics, especially the ggplot2 and lattice packages. Unfortunately I am not currently able to start a graphics device from the embedded R but I expect that to be fixed soon.

I can tell you the most remarkable aspect of RCall although it may not mean much if you haven't tried to do this kind of thing. It is written entirely in Julia. There is absolutely no "glue" code written in a compiled language like C or C++. As I said, this may not mean much to you unless you have tried to do something like this, in which case it is astonishing.

by Douglas Bates ( at February 24, 2015 11:05 PM

January 16, 2015

Modern Toolmaking


My package caretEnsemble, for making ensembles of caret models, is now on CRAN.

Check it out, and let me know what you think! (Submit bug reports and feature requests to the issue tracker)

by Zachary Deane-Mayer ( at January 16, 2015 10:22 PM

January 15, 2015

Gregor Gorjanc

cpumemlog: Monitor CPU and RAM usage of a process (and its children)

Long time no see ...

Today I pushed the cpumemlog script to GitHub Read more about this useful utility at the GitHub site.

by Gregor Gorjanc ( at January 15, 2015 10:16 PM

December 15, 2014

R you ready?

QQ-plots in R vs. SPSS – A look at the differences


We teach two software packages, R and SPSS, in Quantitative Methods 101 for psychology freshman at Bremen University (Germany). Sometimes confusion arises, when the software packages produce different results. This may be due to specifics in the implemention of a method or, as in most cases, to different default settings. One of these situations occurs when the QQ-plot is introduced. Below we see two QQ-plots, produced by SPSS and R, respectively. The data used in the plots were generated by:

x <- sample(0:9, 100, rep=T)    


QQ-plot in SPSS using Blom's method


qqnorm(x, datax=T)      # uses Blom's method by default
qqline(x, datax=T)

There are some obvious differences:

  1. The most obvious one is that the R plot seems to contain more data points than the SPSS plot. Actually, this is not the case. Some data points are plotted on top of each in SPSS while they are spread out vertically in the R plot. The reason for this difference is that SPSS uses a different approach assigning probabilities to the values. We will expore the two approaches below.
  2. The scaling of the y-axis differs. R uses quantiles from the standard normal distribution. SPSS by default rescales these values using the mean and standard deviation from the original data. This allows to directly compare the original and theoretical values. This is a simple linear transformation and will not be explained any further here.
  3. The QQ-lines are not identical. R uses the 1st and 3rd quartile from both distributions to draw the line. This is different in SPSS where of a line is drawn for identical values on both axes. We will expore the differences below.

QQ-plots from scratch

To get a better understanding of the difference we will build the R and SPSS-flavored QQ-plot from scratch.

R type

In order to calculate theoretical quantiles corresponding to the observed values, we first need to find a way to assign a probability to each value of the original data. A lot of different approaches exist for this purpose (for an overview see e.g. Castillo-Gutiérrez, Lozano-Aguilera, & Estudillo-Martínez, 2012b). They usually build on the ranks of the observed data points to calculate corresponding p-values, i.e. the plotting positions for each point. The qqnorm function uses two formulae for this purpose, depending on the number of observations n (Blom’s mfethod, see ?qqnorm; Blom, 1958). With r being the rank, for n > 10 it will use the formula p = (r - 1/2) / n, for n \leq 10 the formula p = (r - 3/8) / (n + 1/4) to determine the probability value p for each observation (see the help files for the functions qqnorm and ppoint). For simplicity reasons, we will only implement the n > 10 case here.

n <- length(x)          # number of observations
r <- order(order(x))    # order of values, i.e. ranks without averaged ties
p <- (r - 1/2) / n      # assign to ranks using Blom's method
y <- qnorm(p)           # theoretical standard normal quantiles for p values
plot(x, y)              # plot empirical against theoretical values

Before we take at look at the code, note that our plot is identical to the plot generated by qqnorm above, except that the QQ-line is missing. The main point that makes the difference between R and SPSS is found in the command order(order(x)). The command calculates ranks for the observations using ordinal ranking. This means that all observations get different ranks and no average ranks are calculated for ties, i.e. for observations with equal values. Another approach would be to apply fractional ranking and calculate average values for ties. This is what the function rank does. The following codes shows the difference between the two approaches to assign ranks.

v <- c(1,1,2,3,3)
order(order(v))     # ordinal ranking used by R
## [1] 1 2 3 4 5
rank(v)             # fractional ranking used by SPSS
## [1] 1.5 1.5 3.0 4.5 4.5

R uses ordinal ranking and SPSS uses fractional ranking by default to assign ranks to values. Thus, the positions do not overlap in R as each ordered observation is assigned a different rank and therefore a different p-value. We will pick up the second approach again later, when we reproduce the SPSS-flavored plot in R.1

The second difference between the plots concerned the scaling of the y-axis and was already clarified above.

The last point to understand is how the QQ-line is drawn in R. Looking at the probs argument of qqline reveals that it uses the 1st and 3rd quartile of the original data and theoretical distribution to determine the reference points for the line. We will draw the line between the quartiles in red and overlay it with the line produced by qqline to see if our code is correct.

plot(x, y)                      # plot empirical against theoretical values
ps <- c(.25, .75)               # reference probabilities
a <- quantile(x, ps)            # empirical quantiles
b <- qnorm(ps)                  # theoretical quantiles
lines(a, b, lwd=4, col="red")   # our QQ line in red
qqline(x, datax=T)              # R QQ line

The reason for different lines in R and SPSS is that several approaches to fitting a straight line exist (for an overview see e.g. Castillo-Gutiérrez, Lozano-Aguilera, & Estudillo-Martínez, 2012a). Each approach has different advantages. The method used by R is more robust when we expect values to diverge from normality in the tails, and we are primarily interested in the normality of the middle range of our data. In other words, the method of fitting an adequate QQ-line depends on the purpose of the plot. An explanation of the rationale of the R approach can e.g. be found here.

SPSS type

The default SPSS approach also uses Blom’s method to assign probabilities to ranks (you may choose other methods is SPSS) and differs from the one above in the following aspects:

  • a) As already mentioned, SPSS uses ranks with averaged ties (fractional rankings) not the plain order ranks (ordinal ranking) as in R to derive the corresponding probabilities for each data point. The rest of the code is identical to the one above, though I am not sure if SPSS distinguishes between the n  10 case.
  • b) The theoretical quantiles are scaled to match the estimated mean and standard deviation of the original data.
  • c) The QQ-line goes through all quantiles with identical values on the x and y axis.
n <- length(x)                # number of observations
r <- rank(x)                  # a) ranks using fractional ranking (averaging ties)
p <- (r - 1/2) / n            # assign to ranks using Blom's method
y <- qnorm(p)                 # theoretical standard normal quantiles for p values
y <- y * sd(x) + mean(x)      # b) transform SND quantiles to mean and sd from original data
plot(x, y)                    # plot empirical against theoretical values

Lastly, let us add the line. As the scaling of both axes is the same, the line goes through the origin with a slope of 1.

abline(0,1)                   # c) slope 0 through origin

The comparison to the SPSS output shows that they are (visually) identical.

Function for SPSS-type QQ-plot

The whole point of this demonstration was to pinpoint and explain the differences between a QQ-plot generated in R and SPSS, so it will no longer be a reason for confusion. Note, however, that SPSS offers a whole range of options to generate the plot. For example, you can select the method to assign probabilities to ranks and decide how to treat ties. The plots above used the default setting (Blom’s method and averaging across ties). Personally I like the SPSS version. That is why I implemented the function qqnorm_spss in the ryouready package, that accompanies the course. The formulae for the different methods to assign probabilities to ranks can be found in Castillo-Gutiérrez et al. (2012b). The implentation is a preliminary version that has not yet been thoroughly tested. You can find the code here. Please report any bugs or suggestions for improvements (which are very welcome) in the github issues section.

install_github("markheckmann/ryouready")                # install from github repo
library(ryouready)                                      # load package
qq <- qqnorm_spss(x, method=1, ties.method="average")   # Blom's method with averaged ties
plot(qq)                                                # generate QQ-plot
ggplot(qq)                                              # use ggplot2 to generate QQ-plot


  1. Technical sidenote: Internally, qqnorm uses the function ppoints to generate the p-values. Type in stats:::qqnorm.default to the console to have a look at the code. 

by markheckmann at December 15, 2014 08:55 AM

October 20, 2014

Modern Toolmaking

For faster R on a mac, use veclib

Update: The links to all my github gists on blogger are broken, and I can't figure out how to fix them.  If you know how to insert gitub gists on a dynamic blogger template, please let me known.

In the meantime, here are instructions with links to the code:
First of all, use homebrew to compile openblas.  It's easy!  Second of all, you can also use homebrew to install R! (But maybe stick with the CRAN version unless you really want to compile your own R binary)

To use openblas with R, follow these instructions:

To use veclib with R, follow these intructions:


Inspired by this post, I decided to try using OpenBLAS for R on my mac.  However, it turns out there's a simpler option, using the vecLib BLAS library, which is provided by Apple as part of the accelerate framework.

If you are using R 2.15, follow these instructions to change your BLAS from the default to vecLib:

However, as noted in r-sig-mac, these instructions do not work for R 3.0.  You have to directly link to the accelerate framework's version of vecLib:

Finally, test your new blas using this script:

On my system (a retina macbook pro), the default BLAS takes 141 seconds and vecLib takes 43 seconds, which is a significant speedup.  If you plan to use vecLib, note the following warning from the R development team "Although fast, it is not under our control and may possibly deliver inaccurate results."

So far, I have not encountered any issues using vecLib, but it's only been a few hours :-).

UPDATE: you can also install OpenBLAS on a mac:

If you do this, make sure to change the directories to point to the correct location on your system  (e.g. change /users/zach/source to whatever directory you clone the git repo into).  On my system, the benchmark script takes ~41 seconds when using openBLAS, which is a small but significant speedup.

by Zachary Deane-Mayer ( at October 20, 2014 04:24 PM

September 19, 2014

Chris Lawrence

What could a federal UK look like?

Assuming that the “no” vote prevails in the Scottish independence referendum, the next question for the United Kingdom is to consider constitutional reform to implement a quasi-federal system and resolve the West Lothian question once and for all. In some ways, it may also provide an opportunity to resolve the stalled reform of the upper house as well. Here’s the rough outline of a proposal that might work.

  • Devolve identical powers to England, Northern Ireland, Scotland, and Wales, with the proviso that local self-rule can be suspended if necessary by the federal legislature (by a supermajority).

  • The existing House of Commons becomes the House of Commons for England, which (along with the Sovereign) shall comprise the English Parliament. This parliament would function much as the existing devolved legislatures in Scotland and Wales; the consociational structure of the Northern Ireland Assembly (requiring double majorities) would not be replicated.

  • The House of Lords is abolished, and replaced with a directly-elected Senate of the United Kingdom. The Senate will have authority to legislate on the non-devolved powers (in American parlance, “delegated” powers) such as foreign and European Union affairs, trade and commerce, national defense, and on matters involving Crown dependencies and territories, the authority to legislate on devolved matters in the event self-government is suspended in a constituent country, and dilatory powers including a qualified veto (requiring a supermajority) over the legislation proposed by a constituent country’s parliament. The latter power would effectively replace the review powers of the existing House of Lords; it would function much as the Council of Revision in Madison’s original plan for the U.S. Constitution.

    As the Senate will have relatively limited powers, it need not be as large as the existing Lords or Commons. To ensure the countries other than England have a meaningful voice, given that nearly 85% of the UK’s population is in England, two-thirds of the seats would be allocated proportionally based on population and one-third allocated equally to the four constituent countries. This would still result in a chamber with a large English majority (around 64.4%) but nonetheless would ensure the other three countries would have meaningful representation as well.

by Chris Lawrence at September 19, 2014 12:00 AM

September 12, 2014

R you ready?

Using colorized PNG pictograms in R base plots

Today I stumbled across a figure in an explanation on multiple factor analysis which contained pictograms.



Figure 1 from Abdi & Valentin (2007), p. 8.

I wanted to reproduce a similar figure in R using pictograms and additionally color them e.g. by group membership . I have almost no knowledge about image processing, so I tried out several methods of how to achieve what I want. The first thing I did was read in an PNG file and look at the data structure. The package png allows to read in PNG files. Note that all of the below may not work on Windows machines, as it does not support semi-transparency (see ?readPNG).

img <- readPNG(system.file("img", "Rlogo.png", package="png"))
## [1] "array"
## [1]  76 100   4

The object is a numerical array with four layers (red, green, blue, alpha; short RGBA). Let’s have a look at the first layer (red) and replace all non-zero entries by a one and the zeros by a dot. This will show us the pattern of non-zero values and we already see the contours.

l4 <- img[,,1]
l4[l4 > 0] <- 1
l4[l4 == 0] <- "."
d <- apply(l4, 1, function(x) {
 cat(paste0(x, collapse=""), "\n") 

To display the image in R one way is to raster the image (i.e. the RGBA layers are collapsed into a layer of single HEX value) and print it using rasterImage.


rimg <- as.raster(img) # raster multilayer object
r <- nrow(rimg) / ncol(rimg) # image ratio
plot(c(0,1), c(0,r), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(rimg, 0, 0, 1, r) 


Let’s have a look at a small part the rastered image object. It is a matrix of HEX values.

rimg[40:50, 1:6]
## [1,] "#C4C5C202" "#858981E8" "#838881FF" "#888D86FF" "#8D918AFF" "#8F938CFF"
## [2,] "#00000000" "#848881A0" "#80847CFF" "#858A83FF" "#898E87FF" "#8D918BFF"
## [3,] "#00000000" "#8B8E884C" "#7D817AFF" "#82867EFF" "#868B84FF" "#8A8E88FF"
## [4,] "#00000000" "#9FA29D04" "#7E827BE6" "#7E817AFF" "#838780FF" "#878C85FF"
## [5,] "#00000000" "#00000000" "#81857D7C" "#797E75FF" "#7F827BFF" "#838781FF"
## [6,] "#00000000" "#00000000" "#898C8510" "#787D75EE" "#797E76FF" "#7F837BFF"
## [7,] "#00000000" "#00000000" "#00000000" "#7F837C7B" "#747971FF" "#797E76FF"
## [8,] "#00000000" "#00000000" "#00000000" "#999C9608" "#767C73DB" "#747971FF"
## [9,] "#00000000" "#00000000" "#00000000" "#00000000" "#80847D40" "#71766EFD"
## [10,] "#00000000" "#00000000" "#00000000" "#00000000" "#00000000" "#787D7589"
## [11,] "#00000000" "#00000000" "#00000000" "#00000000" "#00000000" "#999C9604"

And print this small part.

plot(c(0,1), c(0,.6), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(rimg[40:50, 1:6], 0, 0, 1, .6) 


Now we have an idea of how the image object and the rastered object look like from the inside. Let’s start to modify the images to suit our needs.

In order to change the color of the pictograms, my first idea was to convert the graphics to greyscale and remap the values to a color ramp of may choice. To convert to greyscale there are tons of methods around (see e.g. here). I just pick one of them I found on SO by chance. With R=Red, G=Green and B=Blue we have

brightness = sqrt(0.299 * R^2 + 0.587 * G^2 + 0.114 * B^2)

This approach modifies the PNG files after they have been coerced into a raster object.

# function to calculate brightness values
brightness <- function(hex) {
  v <- col2rgb(hex)
  sqrt(0.299 * v[1]^2 + 0.587 * v[2]^2 + 0.114 * v[3]^2) /255

# given a color ramp, map brightness to ramp also taking into account 
# the alpha level. The defaul color ramp is grey
img_to_colorramp <- function(img, ramp=grey) {
  cv <- as.vector(img)
  b <- sapply(cv, brightness)
  g <- ramp(b)
  a <- substr(cv, 8,9)     # get alpha values
  ga <- paste0(g, a)       # add alpha values to new colors
  img.grey <- matrix(ga, nrow(img), ncol(img), byrow=TRUE)  

# read png and modify
img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img <- as.raster(img)           # raster multilayer object
r <- nrow(img) / ncol(img)      # image ratio
s <- 3.5                        # size

plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)

rasterImage(img, 0, 0, 0+s/r, 0+s)  # original
img2 <- img_to_colorramp(img)       # modify using grey scale
rasterImage(img2, 5, 0, 5+s/r, 0+s)


Great, it works! Now Let’s go and try out some other color palettes using colorRamp to create a color ramp.

plot(c(0,10),c(0,8.5), type = "n", xlab = "", ylab = "", asp=1)

img1 <- img_to_colorramp(img)
rasterImage(img1, 0, 5, 0+s/r, 5+s)

reds <- function(x) 
  rgb(colorRamp(c("darkred", "white"))(x), maxColorValue = 255)
img2 <- img_to_colorramp(img, reds)
rasterImage(img2, 5, 5, 5+s/r, 5+s)

greens <- function(x) 
  rgb(colorRamp(c("darkgreen", "white"))(x), maxColorValue = 255)
img3 <- img_to_colorramp(img, greens)
rasterImage(img3, 0, 0, 0+s/r, 0+s)

single_color <- function(...) "#0000BB"
img4 <- img_to_colorramp(img, single_color)
rasterImage(img4, 5, 0, 5+s/r, 0+s)


Okay, that basically does the job. Now we will apply it to the wine pictograms.
Let’s use this wine glass from Wikimedia Commons. It’s quite big so I uploaded a reduced size version to imgur . We will use it for our purposes.

# load file from web
f <- tempfile()
download.file("", f)
img <- readPNG(f)
img <- as.raster(img)
r <- nrow(img) / ncol(img)
s <- 1

# let's create a function that returns a ramp function to save typing
ramp <- function(colors) 
  function(x) rgb(colorRamp(colors)(x), maxColorValue = 255)

# create dataframe with coordinates and colors
x <- data.frame(x=rnorm(16, c(2,2,4,4)), 
                y=rnorm(16, c(1,3)), 
                colors=c("black", "darkred", "garkgreen", "darkblue"))

plot(c(1,6), c(0,5), type="n", xlab="", ylab="", asp=1)
for (i in 1L:nrow(x)) {
  colorramp <- ramp(c(x[i,3], "white"))
  img2 <- img_to_colorramp(img, colorramp)
  rasterImage(img2, x[i,1], x[i,2], x[i,1]+s/r, x[i,2]+s)


Another approach would be to modifying the RGB layers before rastering to HEX values.

img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img2 <- img
img[,,1] <- 0    # remove Red component
img[,,2] <- 0    # remove Green component
img[,,3] <- 1    # Set Blue to max
img <- as.raster(img)
r <- nrow(img) / ncol(img)  # size ratio
s <- 3.5   # size
plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(img, 0, 0, 0+s/r, 0+s)

img2[,,1] <- 1   # Red to max
img2[,,2] <- 0
img2[,,3] <- 0
rasterImage(as.raster(img2), 5, 0, 5+s/r, 0+s)


To just colorize the image, we could weight each layer.

# wrap weighting into function
weight_layers <- function(img, w) {
  for (i in seq_along(w))
    img[,,i] <- img[,,i] * w[i]

plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
img <- readPNG(system.file("img", "Rlogo.png", package="png"))
img2 <- weight_layers(img, c(.2, 1,.2))
rasterImage(img2, 0, 0, 0+s/r, 0+s)

img3 <- weight_layers(img, c(1,0,0))
rasterImage(img3, 5, 0, 5+s/r, 0+s)


After playing around and hard-coding the modifications I started to search and found the EBimage package which has a lot of features for image processing that make ones life (in this case only a bit) easier.

f <- system.file("img", "Rlogo.png", package="png")
img <- readImage(f) 
img2 <- img

img[,,2] = 0      # zero out green layer
img[,,3] = 0      # zero out blue layer
img <- as.raster(img)

img2[,,1] = 0
img2[,,3] = 0
img2 <- as.raster(img2)

r <- nrow(img) / ncol(img)
s <- 3.5
plot(c(0,10), c(0,3.5), type = "n", xlab = "", ylab = "", asp=1)
rasterImage(img, 0, 0, 0+s/r, 0+s)
rasterImage(img2, 5, 0, 5+s/r, 0+s)


EBImage is a good choice and fairly easy to handle. Now let’s again print the pictograms.

f <- tempfile(fileext=".png")
download.file("", f)
img <- readImage(f)

# will replace whole image layers by one value
# only makes sense if there is a alpha layer that 
# gives the contours
mod_color <- function(img, col) {
  v <- col2rgb(col) / 255
  img = channel(img, 'rgb')
  img[,,1] = v[1]   # Red
  img[,,2] = v[2]   # Green
  img[,,3] = v[3]   # Blue

r <- nrow(img) / ncol(img)  # get image ratio
s <- 1                      # size

# create random data
x <- data.frame(x=rnorm(16, c(2,2,4,4)), 
                y=rnorm(16, c(1,3)), 

# plot pictograms 
plot(c(1,6), c(0,5), type="n", xlab="", ylab="", asp=1)
for (i in 1L:nrow(x)) {
  img2 <- mod_color(img, x[i, 3])
  rasterImage(img2, x[i,1], x[i,2], x[i,1]+s*r, x[i,2]+s)


Note, that above I did not bother to center each pictogram to position it correctly. This still needs to be done. Anyway, that’s it! Mission completed.


Abdi, H., & Valentin, D. (2007). Multiple factor analysis (MFA). In N. Salkind (Ed.), Encyclopedia of Measurement and Statistics (pp. 1–14). Thousand Oaks, CA: Sage Publications. Retrieved from

by markheckmann at September 12, 2014 09:19 AM

June 18, 2014

Chris Lawrence

Soccer queries answered

Kevin Drum asks a bunch of questions about soccer:

  1. Outside the penalty area there’s a hemisphere about 20 yards wide. I can’t recall ever seeing it used for anything. What’s it for?
  2. On several occasions, I’ve noticed that if the ball goes out of bounds at the end of stoppage time, the referee doesn’t whistle the match over. Instead, he waits for the throw-in, and then immediately whistles the match over. What’s the point of this?
  3. Speaking of stoppage time, how has it managed to last through the years? I know, I know: tradition. But seriously. Having a timekeeper who stops the clock for goals, free kicks, etc. has lots of upside and no downside. Right? It wouldn’t change the game in any way, it would just make timekeeping more accurate, more consistent, and more transparent for the fans and players. Why keep up the current pretense?
  4. What’s the best way to get a better sense of what’s a foul and what’s a legal tackle? Obviously you can’t tell from the players’ reactions, since they all writhe around like landed fish if they so much as trip over their own shoelaces. Reading the rules provides the basics, but doesn’t really help a newbie very much. Maybe a video that shows a lot of different tackles and explains why each one is legal, not legal, bookable, etc.?

The first one’s easy: there’s a general rule that no defensive player can be within 10 yards of the spot of a direct free kick. A penalty kick (which is a type of direct free kick) takes place in the 18-yard box, and no players other than the player taking the kick and the goalkeeper are allowed in the box. However, owing to geometry, the 18 yard box and the 10 yard exclusion zone don’t fully coincide, hence the penalty arc. (That’s also why there are two tiny hash-marks on the goal line and side line 10 yards from the corner flag. And why now referees have a can of shaving cream to mark the 10 yards for other free kicks, one of the few MLS innovations that has been a good idea.)

Second one’s also easy: the half and the game cannot end while the ball is out of play.

Third one’s harder. First, keeping time inexactly forestalls the silly premature celebrations that are common in most US sports. You’d never see the Stanford-Cal play happen in a soccer game. Second, it allows some slippage for short delays and doesn’t require exact timekeeping; granted, this was more valuable before instant replays and fourth officials, but most US sports require a lot of administrative record-keeping by ancillary officials. A soccer game can be played with one official (and often is, particularly at the amateur level) without having to change timing rules;* in developing countries in particular this lowers the barriers to entry for the sport (along with the low equipment requirements) without changing the nature of the game appreciably. Perhaps most importantly, if the clock was allowed to stop regularly it would create an excuse for commercial timeouts and advertising breaks, which would interrupt the flow of the game and potentially reduce the advantages of better-conditioned and more skilled athletes. (MLS tried this, along with other exciting American ideas like “no tied games,” and it was as appealing to actual soccer fans as ketchup on filet mignon would be to a foodie, and perhaps more importantly didn’t make any non-soccer fans watch.)

Fourth, the key distinction is usually whether there was an obvious attempt to play the ball; in addition, in the modern game, even some attempts to play the ball are considered inherently dangerous (tackling from behind, many sliding tackles, etc.) and therefore are fouls even if they are successful in getting more ball than human.

* To call offside, you’d also probably need what in my day we called a “linesman.”

by Chris Lawrence at June 18, 2014 12:00 AM

May 07, 2014

Chris Lawrence

The mission and vision thing

Probably the worst-kept non-secret is that the next stage of the institutional evolution of my current employer is to some ill-defined concept of “university status,” which mostly involves the establishment of some to-be-determined master’s degree programs. In the context of the University System of Georgia, it means a small jump from the “state college” prestige tier (a motley collection of schools that largely started out as two-year community colleges and transfer institutions) to the “state university” tier (which is where most of the ex-normal schools hang out these days). What is yet to be determined is how that transition will affect the broader institution that will be the University of Middle Georgia.* People on high are said to be working on these things; in any event, here are my assorted random thoughts on what might be reasonable things to pursue:

  • Marketing and positioning: Unlike the situation facing many of the other USG institutions, the population of the two anchor counties of our core service area (Bibb and Houston) is growing, and Houston County in particular has a statewide reputation for the quality of its public school system. Rather than conceding that the most prepared students from these schools will go to Athens or Atlanta or Valdosta, we should strongly market our institutional advantages over these more “prestigious” institutions, particularly in terms of the student experience in the first two years and the core curriculum: we have no large lecture courses, no teaching assistants, no lengthy bus rides to and from class every day, and the vast majority of the core is taught by full-time faculty with terminal degrees. Not to mention costs to students are much lower, particularly in the case of students who do not qualify for need-based aid. Even if we were to “lose” these students as transfers to the top-tier institutions after 1–4 semesters, we’d still benefit from the tuition and fees they bring in and we would not be penalized in the upcoming state performance funding formula. Dual enrollment in Warner Robins in particular is an opportunity to showcase our institution as a real alternative for better prepared students rather than a safety school.
  • Comprehensive offerings at the bachelor’s level: As a state university, we will need to offer a comprehensive range of options for bachelor’s students to attract and retain students, both traditional and nontraditional. In particular, B.S. degrees in political science and sociology with emphasis in applied empirical skills would meet public and private employer demand for workers who have research skills and the ability to collect, manage, understand, and use data appropriately. There are other gaps in the liberal arts and sciences as well that need to be addressed to become a truly comprehensive state university.
  • Create incentives to boost the residential population: The college currently has a heavy debt burden inherited from the overbuilding of dorms at the Cochran campus. We need to identify ways to encourage students to live in Cochran, which may require public-private partnerships to try to build a “college town” atmosphere in the community near campus. We also need to work with wireless providers like Sprint and T-Mobile to ensure that students from the “big city” can fully use their cell phones and tablets in Cochran and Eastman without roaming fees or changing wireless providers.
  • Tie the institution more closely to the communities we serve: This includes both physical ties and psychological ties. The Macon campus in particular has poor physical links to the city itself for students who might walk or ride bicycles; extending the existing bike/walking trail from Wesleyan to the Macon campus should be a priority, as should pedestrian access and bike facilities along Columbus Road. Access to the Warner Robins campus is somewhat better but still could be improved. More generally, the institution is perceived as an afterthought or alternative of last resort in the community. Improving this situation and perception among community leaders and political figures may require a physical presence in or near downtown Macon, perhaps in partnership with the GCSU Graduate Center.

* There is no official name-in-waiting, but given that our former interim president seemed to believe he could will this name into existence by repeating it enough I’ll stick with it. The straw poll of faculty trivia night suggests that it’s the least bad option available, which inevitably means the regents will choose something else instead (if the last name change is anything to go by).

by Chris Lawrence at May 07, 2014 12:00 AM

February 17, 2014

Seth Falcon

Have Your SHA and Bcrypt Too


I've been putting off sharing this idea because I've heard the rumors about what happens to folks who aren't security experts when they post about security on the internet. If this blog is replaced with cat photos and rainbows, you'll know what happened.

The Sad Truth

It's 2014 and chances are you have accounts on websites that are not properly handling user passwords. I did no research to produce the following list of ways passwords are mishandled in decreasing order of frequency:

  1. Site uses a fast hashing algorithm, typically SHA1(salt + plain-password).
  2. Site doesn't salt password hashes
  3. Site stores raw passwords

We know that sites should be generating secure random salts and using an established slow hashing algorithm (bcrypt, scrypt, or PBKDF2). Why are sites not doing this?

While security issues deserve a top spot on any site's priority list, new features often trump addressing legacy security concerns. The immediacy of the risk is hard to quantify and it's easy to fall prey to a "nothing bad has happened yet, why should we change now" attitude. It's easy for other bugs, features, or performance issues to win out when measured by immediate impact. Fixing security or other "legacy" issues is the Right Thing To Do and often you will see no measurable benefit from the investment. It's like having insurance. You don't need it until you do.

Specific to the improper storage of user password data is the issue of the impact to a site imposed by upgrading. There are two common approaches to upgrading password storage. You can switch cold turkey to the improved algorithms and force password resets on all of your users. Alternatively, you can migrate incrementally such that new users and any user who changes their password gets the increased security.

The cold turkey approach is not a great user experience and sites might choose to delay an upgrade to avoid admitting to a weak security implementation and disrupting their site by forcing password resets.

The incremental approach is more appealing, but the security benefit is drastically diminished for any site with a substantial set of existing users.

Given the above migration choices, perhaps it's (slightly) less surprising that businesses choose to prioritize other work ahead of fixing poorly stored user password data.

The Idea

What if you could upgrade a site so that both new and existing users immediately benefited from the increased security, but without the disruption of password resets? It turns out that you can and it isn't very hard.

Consider a user table with columns:


Where the hashed_pass column is computed using a weak fast algorithm, for example SHA1(salt + plain_pass).

The core of the idea is to apply a proper algorithm on top of the data we already have. I'll use bcrypt to make the discussion concrete. Add columns to the user table as follows:


Process the existing user table by computing bcrypt(salt2 + hashed_pass) and storing the result in the hashed_pass column (overwriting the less secure value); save the new salt value to salt2 and set hash_type to bycrpt+sha1.

To verify a user where hash_type is bcrypt+sha1, compute bcrypt(salt2 + SHA1(salt + plain_pass)) and compare to the hashed_pass value. Note that bcrypt implementations encode the salt as a prefix of the hashed value so you could avoid the salt2 column, but it makes the idea easier to explain to have it there.

You can take this approach further and have any user that logs in (as well as new users) upgrade to a "clean" bcrypt only algorithm since you can now support different verification algorithms using hash_type. With the proper application code changes in place, the upgrade can be done live.

This scheme will also work for sites storing non-salted password hashes as well as those storing plain text passwords (THE HORROR).

Less Sadness, Maybe

Perhaps this approach makes implementing a password storage security upgrade more palatable and more likely to be prioritized. And if there's a horrible flaw in this approach, maybe you'll let me know without turning this blog into a tangle of cat photos and rainbows.

February 17, 2014 07:08 PM

December 26, 2013

Seth Falcon

A Rebar Plugin for Locking Deps: Reproducible Erlang Project Builds For Fun and Profit

What's this lock-deps of which you speak?

If you use rebar to generate an OTP release project and want to have reproducible builds, you need the rebar_lock_deps_plugin plugin. The plugin provides a lock-deps command that will generate a rebar.config.lock file containing the complete flattened set of project dependencies each pegged to a git SHA. The lock file acts similarly to Bundler's Gemfile.lock file and allows for reproducible builds (*).

Without lock-deps you might rely on the discipline of using a tag for all of your application's deps. This is insufficient if any dep depends on something not specified as a tag. It can also be a problem if a third party dep doesn't provide a tag. Generating a rebar.config.lock file solves these issues. Moreover, using lock-deps can simplify the work of putting together a release consisting of many of your own repos. If you treat the master branch as shippable, then rather than tagging each subproject and updating rebar.config throughout your project's dependency chain, you can run get-deps (without the lock file), compile, and re-lock at the latest versions throughout your project repositories.

The reproducibility of builds when using lock-deps depends on the SHAs captured in rebar.config.lock. The plugin works by scanning the cloned repos in your project's deps directory and extracting the current commit SHA. This works great until a repository's history is rewritten with a force push. If you really want reproducible builds, you need to not nuke your SHAs and you'll need to fork all third party repos to ensure that someone else doesn't screw you over in this fashion either. If you make a habit of only depending on third party repos using a tag, assume that upstream maintainers are not completely bat shit crazy, and don't force push your master branch, then you'll probably be fine.

Getting Started

Install the plugin in your project by adding the following to your rebar.config file:

%% Plugin dependency
{deps, [
    {rebar_lock_deps_plugin, ".*",
     {git, "git://", {branch, "master"}}}

%% Plugin usage
{plugins, [rebar_lock_deps_plugin]}.

To test it out do:

rebar get-deps
# the plugin has to be compiled so you can use it
rebar compile
rebar lock-deps

If you'd like to take a look at a project that uses the plugin, take a look at CHEF's erchef project.

Bonus features

If you are building an OTP release project using rebar generate then you can use rebar_lock_deps_plugin to enhance your build experience in three easy steps.

  1. Use rebar bump-rel-version version=$BUMP to automate the process of editing rel/reltool.config to update the release version. The argument $BUMP can be major, minor, or patch (default) to increment the specified part of a semver X.Y.Z version. If $BUMP is any other value, it is used as the new version verbatim. Note that this function rewrites rel/reltool.config using ~p. I check-in the reformatted version and maintain the formatting when editing. This way, the general case of a version bump via bump-rel-version results in a minimal diff.

  2. Autogenerate a change summary commit message for all project deps. Assuming you've generated a new lock file and bumped the release version, use rebar commit-release to commit the changes to rebar.config.lock and rel/reltool.config with a commit message that summarizes the changes made to each dependency between the previously locked version and the newly locked version. You can get a preview of the commit message via rebar log-changed-deps.

  3. Finally, create an annotated tag for your new release with rebar tag-release which will read the current version from rel/reltool.config and create an annotated tag named with the version.

The dependencies, they are ordered

Up to version 2.0.1 of rebar_lock_deps_plugin, the dependencies in the generated lock file were ordered alphabetically. This was a side-effect of using filelib:wildcard/1 to list the dependencies in the top-level deps directory. In most cases, the order of the full dependency set does not matter. However, if some of the code in your project uses parse transforms, then it will be important for the parse transform to be compiled and on the code path before attempting to compile code that uses the parse transform.

This issue was recently discovered by a colleague who ran into build issues using the lock file for a project that had recently integrated lager for logging. He came up with the idea of maintaining the order of deps as they appear in the various rebar.config files along with a prototype patch proving out the idea. As of rebar_lock_deps_plugin 3.0.0, the lock-deps command will (mostly) maintain the relative order of dependencies as found in the rebar.config files.

The "mostly" is that when a dep is shared across two subprojects, it will appear in the expected order for the first subproject (based on the ordering of the two subprojects). The deps for the second subproject will not be in strict rebar.config order, but the resulting order should address any compile-time dependencies and be relatively stable (only changing when project deps alter their deps with larger impact when shared deps are introduced or removed).

Digression: fun with dependencies

There are times, as a programmer, when a real-world problem looks like a text book exercise (or an interview whiteboard question). Just the other day at work we had to design some manhole covers, but I digress.

Fixing the order of the dependencies in the generated lock file is (nearly) the same as finding an install order for a set of projects with inter-dependencies. I had some fun coding up the text book solution even though the approach doesn't handle the constraint of respecting the order provided by the rebar.config files. Onward with the digression.

We have a set of "packages" where some packages depend on others and we want to determine an install order such that a package's dependencies are always installed before the package. The set of packages and the relation "depends on" form a directed acyclic graph or DAG. The topological sort of a DAG produces an install order for such a graph. The ordering is not unique. For example, with a single package C depending on A and B, valid install orders are [A, B, C] and [B, A, C].

To setup the problem, we load all of the project dependency information into a proplist mapping each package to a list of its dependencies extracted from the package's rebar.config file.

read_all_deps(Config, Dir) ->
    TopDeps = rebar_config:get(Config, deps, []),
    Acc = [{top, dep_names(TopDeps)}],
    DepDirs = filelib:wildcard(filename:join(Dir, "*")),
    Acc ++ [
     {filename:basename(D), dep_names(extract_deps(D))}
     || D <- DepDirs ].

Erlang's standard library provides the digraph and digraph_utils modules for constructing and operating on directed graphs. The digraph_utils module includes a topsort/1 function which we can make use of for our "exercise". The docs say:

Returns a topological ordering of the vertices of the digraph Digraph if such an ordering exists, false otherwise. For each vertex in the returned list, there are no out-neighbours that occur earlier in the list.

To figure out which way to point the edges when building our graph, consider two packages A and B with A depending on B. We know we want to end up with an install order of [B, A]. Rereading the topsort/1 docs, we must want an edge B => A. With that, we can build our DAG and obtain an install order with the topological sort:

load_digraph(Config, Dir) ->
    AllDeps = read_all_deps(Config, Dir),
    G = digraph:new(),
    Nodes = all_nodes(AllDeps),
    [ digraph:add_vertex(G, N) || N <- Nodes ],
    %% If A depends on B, then we add an edge A <= B
      [ digraph:add_edge(G, Dep, Item)
        || Dep <- DepList ]
      || {Item, DepList} <- AllDeps, Item =/= top ],

%% extract a sorted unique list of all deps
all_nodes(AllDeps) ->
    lists:usort(lists:foldl(fun({top, L}, Acc) ->
                                    L ++ Acc;
                               ({K, L}, Acc) ->
                                    [K|L] ++ Acc
                            end, [], AllDeps)).

The digraph module manages graphs using ETS giving it a convenient API, though one that feels un-erlang-y in its reliance on side-effects.

The above gives an install order, but doesn't take into account the relative order of deps as specified in the rebar.config files. The solution implemented in the plugin is a bit less fancy, recursing over the deps and maintaining the desired ordering. The only tricky bit being that shared deps are ignored until the end and the entire linearized list is de-duped which required a . Here's the code:

order_deps(AllDeps) ->
    Top = proplists:get_value(top, AllDeps),
    order_deps(lists:reverse(Top), AllDeps, []).

order_deps([], _AllDeps, Acc) ->
order_deps([Item|Rest], AllDeps, Acc) ->
    ItemDeps = proplists:get_value(Item, AllDeps),
    order_deps(lists:reverse(ItemDeps) ++ Rest, AllDeps, [Item | Acc]).

de_dup(AccIn) ->
    WithIndex = lists:zip(AccIn, lists:seq(1, length(AccIn))),
    UWithIndex = lists:usort(fun({A, _}, {B, _}) ->
                                     A =< B
                             end, WithIndex),
    Ans0 = lists:sort(fun({_, I1}, {_, I2}) ->
                              I1 =< I2
                      end, UWithIndex),
    [ V || {V, _} <- Ans0 ].

Conclusion and the end of this post

The great thing about posting to your blog is, you don't have to have a proper conclusion if you don't want to.

December 26, 2013 04:20 PM

December 09, 2013

Leandro Penz

Probabilistic bug hunting

Probabilistic bug hunting

Have you ever run into a bug that, no matter how careful you are trying to reproduce it, it only happens sometimes? And then, you think you've got it, and finally solved it - and tested a couple of times without any manifestation. How do you know that you have tested enough? Are you sure you were not "lucky" in your tests?

In this article we will see how to answer those questions and the math behind it without going into too much detail. This is a pragmatic guide.

The Bug

The following program is supposed to generate two random 8-bit integer and print them on stdout:

  #include <stdio.h>
  #include <fcntl.h>
  #include <unistd.h>
  /* Returns -1 if error, other number if ok. */
  int get_random_chars(char *r1, char*r2)
  	int f = open("/dev/urandom", O_RDONLY);
  	if (f < 0)
  		return -1;
  	if (read(f, r1, sizeof(*r1)) < 0)
  		return -1;
  	if (read(f, r2, sizeof(*r2)) < 0)
  		return -1;
  	return *r1 & *r2;
  int main(void)
  	char r1;
  	char r2;
  	int ret;
  	ret = get_random_chars(&r1, &r2);
  	if (ret < 0)
  		fprintf(stderr, "error");
  		printf("%d %d\n", r1, r2);
  	return ret < 0;

On my architecture (Linux on IA-32) it has a bug that makes it print "error" instead of the numbers sometimes.

The Model

Every time we run the program, the bug can either show up or not. It has a non-deterministic behaviour that requires statistical analysis.

We will model a single program run as a Bernoulli trial, with success defined as "seeing the bug", as that is the event we are interested in. We have the following parameters when using this model:

  • \(n\): the number of tests made;
  • \(k\): the number of times the bug was observed in the \(n\) tests;
  • \(p\): the unknown (and, most of the time, unknowable) probability of seeing the bug.

As a Bernoulli trial, the number of errors \(k\) of running the program \(n\) times follows a binomial distribution \(k \sim B(n,p)\). We will use this model to estimate \(p\) and to confirm the hypotheses that the bug no longer exists, after fixing the bug in whichever way we can.

By using this model we are implicitly assuming that all our tests are performed independently and identically. In order words: if the bug happens more ofter in one environment, we either test always in that environment or never; if the bug gets more and more frequent the longer the computer is running, we reset the computer after each trial. If we don't do that, we are effectively estimating the value of \(p\) with trials from different experiments, while in truth each experiment has its own \(p\). We will find a single value anyway, but it has no meaning and can lead us to wrong conclusions.

Physical analogy

Another way of thinking about the model and the strategy is by creating a physical analogy with a box that has an unknown number of green and red balls:

  • Bernoulli trial: taking a single ball out of the box and looking at its color - if it is red, we have observed the bug, otherwise we haven't. We then put the ball back in the box.
  • \(n\): the total number of trials we have performed.
  • \(k\): the total number of red balls seen.
  • \(p\): the total number of red balls in the box divided by the total number of green balls in the box.

Some things become clearer when we think about this analogy:

  • If we open the box and count the balls, we can know \(p\), in contrast with our original problem.
  • Without opening the box, we can estimate \(p\) by repeating the trial. As \(n\) increases, our estimate for \(p\) improves. Mathematically: \[p = \lim_{n\to\infty}\frac{k}{n}\]
  • Performing the trials in different conditions is like taking balls out of several different boxes. The results tell us nothing about any single box.

Estimating \(p\)

Before we try fixing anything, we have to know more about the bug, starting by the probability \(p\) of reproducing it. We can estimate this probability by dividing the number of times we see the bug \(k\) by the number of times we tested for it \(n\). Let's try that with our sample bug:

  $ ./hasbug
  67 -68
  $ ./hasbug
  79 -101
  $ ./hasbug

We know from the source code that \(p=25%\), but let's pretend that we don't, as will be the case with practically every non-deterministic bug. We tested 3 times, so \(k=1, n=3 \Rightarrow p \sim 33%\), right? It would be better if we tested more, but how much more, and exactly what would be better?

\(p\) precision

Let's go back to our box analogy: imagine that there are 4 balls in the box, one red and three green. That means that \(p = 1/4\). What are the possible results when we test three times?

Red balls Green balls \(p\) estimate
0 3 0%
1 2 33%
2 1 66%
3 0 100%

The less we test, the smaller our precision is. Roughly, \(p\) precision will be at most \(1/n\) - in this case, 33%. That's the step of values we can find for \(p\), and the minimal value for it.

Testing more improves the precision of our estimate.

\(p\) likelihood

Let's now approach the problem from another angle: if \(p = 1/4\), what are the odds of seeing one error in four tests? Let's name the 4 balls as 0-red, 1-green, 2-green and 3-green:

The table above has all the possible results for getting 4 balls out of the box. That's \(4^4=256\) rows, generated by this python script. The same script counts the number of red balls in each row, and outputs the following table:

k rows %
0 81 31.64%
1 108 42.19%
2 54 21.09%
3 12 4.69%
4 1 0.39%

That means that, for \(p=1/4\), we see 1 red ball and 3 green balls only 42% of the time when getting out 4 balls.

What if \(p = 1/3\) - one red ball and two green balls? We would get the following table:

k rows %
0 16 19.75%
1 32 39.51%
2 24 29.63%
3 8 9.88%
4 1 1.23%

What about \(p = 1/2\)?

k rows %
0 1 6.25%
1 4 25.00%
2 6 37.50%
3 4 25.00%
4 1 6.25%

So, let's assume that you've seen the bug once in 4 trials. What is the value of \(p\)? You know that can happen 42% of the time if \(p=1/4\), but you also know it can happen 39% of the time if \(p=1/3\), and 25% of the time if \(p=1/2\). Which one is it?

The graph bellow shows the discrete likelihood for all \(p\) percentual values for getting 1 red and 3 green balls:

The fact is that, given the data, the estimate for \(p\) follows a beta distribution \(Beta(k+1, n-k+1) = Beta(2, 4)\) (1) The graph below shows the probability distribution density of \(p\):

The R script used to generate the first plot is here, the one used for the second plot is here.

Increasing \(n\), narrowing down the interval

What happens when we test more? We obviously increase our precision, as it is at most \(1/n\), as we said before - there is no way to estimate that \(p=1/3\) when we only test twice. But there is also another effect: the distribution for \(p\) gets taller and narrower around the observed ratio \(k/n\):

Investigation framework

So, which value will we use for \(p\)?

  • The smaller the value of \(p\), the more we have to test to reach a given confidence in the bug solution.
  • We must, then, choose the probability of error that we want to tolerate, and take the smallest value of \(p\) that we can.

    A usual value for the probability of error is 5% (2.5% on each side).
  • That means that we take the value of \(p\) that leaves 2.5% of the area of the density curve out on the left side. Let's call this value \(p_{min}\).
  • That way, if the observed \(k/n\) remains somewhat constant, \(p_{min}\) will raise, converging to the "real" \(p\) value.
  • As \(p_{min}\) raises, the amount of testing we have to do after fixing the bug decreases.

By using this framework we have direct, visual and tangible incentives to test more. We can objectively measure the potential contribution of each test.

In order to calculate \(p_{min}\) with the mentioned properties, we have to solve the following equation:

\[\sum_{k=0}^{k}{n\choose{k}}p_{min} ^k(1-p_{min})^{n-k}=\frac{\alpha}{2} \]

\(alpha\) here is twice the error we want to tolerate: 5% for an error of 2.5%.

That's not a trivial equation to solve for \(p_{min}\). Fortunately, that's the formula for the confidence interval of the binomial distribution, and there are a lot of sites that can calculate it:

Is the bug fixed?

So, you have tested a lot and calculated \(p_{min}\). The next step is fixing the bug.

After fixing the bug, you will want to test again, in order to confirm that the bug is fixed. How much testing is enough testing?

Let's say that \(t\) is the number of times we test the bug after it is fixed. Then, if our fix is not effective and the bug still presents itself with a probability greater than the \(p_{min}\) that we calculated, the probability of not seeing the bug after \(t\) tests is:

\[\alpha = (1-p_{min})^t \]

Here, \(\alpha\) is also the probability of making a type I error, while \(1 - \alpha\) is the statistical significance of our tests.

We now have two options:

  • arbitrarily determining a standard statistical significance and testing enough times to assert it.
  • test as much as we can and report the achieved statistical significance.

Both options are valid. The first one is not always feasible, as the cost of each trial can be high in time and/or other kind of resources.

The standard statistical significance in the industry is 5%, we recommend either that or less.

Formally, this is very similar to a statistical hypothesis testing.

Back to the Bug

Testing 20 times

This file has the results found after running our program 5000 times. We must never throw out data, but let's pretend that we have tested our program only 20 times. The observed \(k/n\) ration and the calculated \(p_{min}\) evolved as shown in the following graph:

After those 20 tests, our \(p_{min}\) is about 12%.

Suppose that we fix the bug and test it again. The following graph shows the statistical significance corresponding to the number of tests we do:

In words: we have to test 24 times after fixing the bug to reach 95% statistical significance, and 35 to reach 99%.

Now, what happens if we test more before fixing the bug?

Testing 5000 times

Let's now use all the results and assume that we tested 5000 times before fixing the bug. The graph bellow shows \(k/n\) and \(p_{min}\):

After those 5000 tests, our \(p_{min}\) is about 23% - much closer to the real \(p\).

The following graph shows the statistical significance corresponding to the number of tests we do after fixing the bug:

We can see in that graph that after about 11 tests we reach 95%, and after about 16 we get to 99%. As we have tested more before fixing the bug, we found a higher \(p_{min}\), and that allowed us to test less after fixing the bug.

Optimal testing

We have seen that we decrease \(t\) as we increase \(n\), as that can potentially increases our lower estimate for \(p\). Of course, that value can decrease as we test, but that means that we "got lucky" in the first trials and we are getting to know the bug better - the estimate is approaching the real value in a non-deterministic way, after all.

But, how much should we test before fixing the bug? Which value is an ideal value for \(n\)?

To define an optimal value for \(n\), we will minimize the sum \(n+t\). This objective gives us the benefit of minimizing the total amount of testing without compromising our guarantees. Minimizing the testing can be fundamental if each test costs significant time and/or resources.

The graph bellow shows us the evolution of the value of \(t\) and \(t+n\) using the data we generated for our bug:

We can see clearly that there are some low values of \(n\) and \(t\) that give us the guarantees we need. Those values are \(n = 15\) and \(t = 24\), which gives us \(t+n = 39\).

While you can use this technique to minimize the total number of tests performed (even more so when testing is expensive), testing more is always a good thing, as it always improves our guarantee, be it in \(n\) by providing us with a better \(p\) or in \(t\) by increasing the statistical significance of the conclusion that the bug is fixed. So, before fixing the bug, test until you see the bug at least once, and then at least the amount specified by this technique - but also test more if you can, there is no upper bound, specially after fixing the bug. You can then report a higher confidence in the solution.


When a programmer finds a bug that behaves in a non-deterministic way, he knows he should test enough to know more about the bug, and then even more after fixing it. In this article we have presented a framework that provides criteria to define numerically how much testing is "enough" and "even more." The same technique also provides a method to objectively measure the guarantee that the amount of testing performed provides, when it is not possible to test "enough."

We have also provided a real example (even though the bug itself is artificial) where the framework is applied.

As usual, the source code of this page (R scripts, etc) can be found and downloaded in

December 09, 2013 12:00 AM

December 01, 2013

Gregor Gorjanc

Read line by line of a file in R

Are you using R for data manipulation for later use with other programs, i.e., a workflow something like this:
  1. read data sets from a disk,
  2. modify the data, and
  3. write it back to a disk.
All fine, but of data set is really big, then you will soon stumble on memory issues. If data processing is simple and you can read only chunks, say only line by line, then the following might be useful:

## File
file <- "myfile.txt"
## Create connection
con <- file(description=file, open="r")
## Hopefully you know the number of lines from some other source or
com <- paste("wc -l ", file, " | awk '{ print $1 }'", sep="")
n <- system(command=com, intern=TRUE)
## Loop over a file connection
for(i in 1:n) {
tmp <- scan(file=con, nlines=1, quiet=TRUE)
## do something on a line of data
Created by Pretty R at

by Gregor Gorjanc ( at December 01, 2013 10:55 PM

August 13, 2013

Gregor Gorjanc

Setup up the inverse of additive relationship matrix in R

Additive genetic covariance between individuals is one of the key concepts in (quantitative) genetics. When doing the prediction of additive genetic values for pedigree members, we need the inverse of the so called numerator relationship matrix (NRM) or simply A. Matrix A has off-diagonal entries equal to numerator of Wright's relationship coefficient and diagonal elements equal to 1 + inbreeding coefficient. I have blogged before about setting up such inverse in R using routine from the ASReml-R program or importing the inverse from the CFC program. However, this is not the only way to "skin this cat" in R. I am aware of the following attempts to provide this feature in R for various things (the list is probably incomplete and I would grateful if you point me to other implementations):
  • pedigree R package has function makeA() and makeAinv() with obvious meanings; there is also calcG() if you have a lot of marker data instead of pedigree information; there are also some other very handy functions calcInbreeding(), orderPed(), trimPed(), etc.
  • pedigreemm R package does not have direct implementation to get A inverse, but has all the needed ingredients, which makes the package even more interesting
  • MCMCglmm R package has function inverseA() which works with pedigree or phlyo objects; there are also handy functions such as prunePed(), rbv()sm2asreml(), etc.
  • kinship and kinship2 R packages have function kinship() to setup kinship matrix, which is equal to the half of A; there are also nice functions for plotting pedigrees etc. (see also here)
  • see also a series of R scripts for relationship matrices 
As I described before, the interesting thing is that setting up inverse of A is easier and cheaper than setting up A and inverting it. This is very important for large applications. This is an old result using the following matrix theory. We can decompose symmetric positive definite matrix as A = LU = LL' (Cholesky decomposition) or as A = LDU = LDL' (Generalized Cholesky decomposition), where L (U) is lower (upper) triangular, and D is diagonal matrix. Note that L and U in previous two equations are not the same thing (L from Cholesky is not equal to L from Generalized Cholesky decomposition)! Sorry for sloppy notation. In order to confuse you even more note that Henderson usually wrote A = TDT'. We can even do A = LSSU, where S diagonal is equal to the square root of D diagonal. This can get us back to A = LU = LL' as LSSU = LSSL' = LSS'L' = LS(LS)' = L'L (be ware of sloppy notation)! The inverse rule says that inv(A) = inv(LDU) = inv(U) inv(D) inv(L) = inv(L)' inv(D) inv(L) = inv(L)' inv(S) inv(S) inv(L). I thank to Martin Maechler for pointing out to the last (obviously) bit to me. In Henderson's notation this would be inv(A) = inv(T)' inv(D) inv(T) = inv(T)' inv(S) inv(S) inv(T) Uf ... The important bit is that with NRM (aka A) inv(L) has nice simple structure - it shows the directed graph of additive genetic values in pedigree, while inv(D) tells us about the precision (inverse variance) of additive genetic values given the additive genetic values of parents and therefore depends on knowledge of parents and their inbreeding (the more they are inbred less variation can we expect in their progeny). Both inv(L) and inv(D) are easier to setup.

Packages MCMCglmm and pedigree give us inv(A) directly (we can also get inv(D) in MCMCglmm), but pedigreemm enables us to play around with the above matrix algebra and graph theory. First we need a small example pedigree. Bellow is an example with 10 members and there is also some inbreeding and some individuals have both, one, or no parents known. It is hard to see inbreeding directly from the table, but we will improve that later (see also here).

ped <- data.frame( id=c(  1,   2,   3,   4,   5,   6,   7,   8,   9,  10),
fid=c( NA, NA, 2, 2, 4, 2, 5, 5, NA, 8),
mid=c( NA, NA, 1, NA, 3, 3, 6, 6, NA, 9))

Now we will create an object of a pedigree class and show the A = U'U stuff:

## install.packages(pkgs="pedigreemm")
ped2 <- with(ped, pedigree(sire=fid, dam=mid, label=id))
U <-
A &lt
;- crossprod(U)
round(U, digits=2)
## 10 x 10 sparse Matrix of class "dtCMatrix"
## [1,] 1 . 0.50 . 0.25 0.25 0.25 0.25 . 0.12
## [2,] . 1 0.50 0.50 0.50 0.75 0.62 0.62 . 0.31
## [3,] . . 0.71 . 0.35 0.35 0.35 0.35 . 0.18
## [4,] . . . 0.87 0.43 . 0.22 0.22 . 0.11
## [5,] . . . . 0.71 . 0.35 0.35 . 0.18
## [6,] . . . . . 0.71 0.35 0.35 . 0.18
## [7,] . . . . . . 0.64 . . .
## [8,] . . . . . . . 0.64 . 0.32
## [9,] . . . . . . . . 1 0.50
## [10,] . . . . . . . . . 0.66

## To check
U - chol(A)

round(A, digits=2)
## 10 x 10 sparse Matrix of class "dsCMatrix"
## [1,] 1.00 . 0.50 . 0.25 0.25 0.25 0.25 . 0.12
## [2,] . 1.00 0.50 0.50 0.50 0.75 0.62 0.62 . 0.31
## [3,] 0.50 0.50 1.00 0.25 0.62 0.75 0.69 0.69 . 0.34
## [4,] . 0.50 0.25 1.00 0.62 0.38 0.50 0.50 . 0.25
## [5,] 0.25 0.50 0.62 0.62 1.12 0.56 0.84 0.84 . 0.42
## [6,] 0.25 0.75 0.75 0.38 0.56 1.25 0.91 0.91 . 0.45
## [7,] 0.25 0.62 0.69 0.50 0.84 0.91 1.28 0.88 . 0.44
## [8,] 0.25 0.62 0.69 0.50 0.84 0.91 0.88 1.28 . 0.64
## [9,] . . . . . . . . 1.0 0.50
## [10,] 0.12 0.31 0.34 0.25 0.42 0.45 0.44 0.64 0.5

ote tha
m package uses Matrix classes in order to store only what we need to store, e.g., matrix U is triangular (t in "dtCMatrix") and matrix A is symmetric (s in "dsCMatrix"). To show the generalized Cholesky A = LDU (or using Henderson notation A = TDT') we use gchol() from the bdsmatrix R package. Matrix T shows the "flow" of genes in pedigree.

## install.packages(pkgs="bdsmatrix")
&lt;- gchol(as.matrix(A))
D &lt;- diag(tmp)
(T <- as(as.matrix(tmp), "dtCMatrix"))
## 10 x 10 sparse Matrix of class "dtCMatrix"
## [1,] 1.000 . . . . . . . . .
## [2,] . 1.0000 . . . . . . . .
## [3,] 0.500 0.5000 1.00 . . . . . . .
## [4,] . 0.5000 . 1.000 . . . . . .
## [5,] 0.250 0.5000 0.50 0.500 1.00 . . . . .
## [6,] 0.250 0.7500 0.50 . . 1.00 . . . .
## [7,] 0.250 0.6250 0.50 0.250 0.50 0.50 1 . . .
## [8,] 0.250 0.6250 0.50 0.250 0.50 0.50 . 1.0 . .
## [9,] . . . . . . . . 1.0 .
## [10,] 0.125 0.3125 0.25 0.125 0.25 0.25 . 0.5 0.5 1

## To chec
- T %*% diag(sqrt(D))
L - t(U)
Now the A inverse part (inv(A) = inv(T)' inv(D) inv(T) = inv(T)' inv(S) inv(S) inv(T) using Henderson's notation, note that ). The nice thing is that pedigreemm authors provided functions to get inv(T) and D.

(TInv <- as(ped2, "sparseMatrix"))
## 10 x 10 sparse Matrix of class "dtCMatrix" (unitriangular)
## 1 1.0 . . . . . . . . .
## 2 . 1.0 . . . . . . . .
## 3 -0.5 -0.5 1.0 . . . . . . .
## 4 . -0.5 . 1.0 . . . . . .
## 5 . . -0.5 -0.5 1.0 . . . . .
## 6 . -0.5 -0.5 . . 1.0 . . . .
## 7 . . . . -0.5 -0.5 1 . . .
## 8 . . . . -0.5 -0.5 . 1.0 . .
## 9 . . . . . . . . 1.0 .
## 10 . . . . . . . -0.5 -0.5 1
round(DInv <- Diagonal(x=1/Dmat(ped2)), digits=2)
## 10 x 10 diagonal matrix of class "ddiMatrix"
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 . . . . . . . . .
## [2,] . 1 . . . . . . . .
## [3,] . . 2 . . . . . . .
## [4,] . . . 1.33 . . . . . .
## [5,] . . . . 2 . . . . .
## [6,] . . . . . 2 . . . .
## [7,] . . . . . . 2.46 . . .
## [8,] . . . . . . . 2.46 . .
## [9,] . . . . . . . . 1 .
## [10,] . . . . . . . . . 2.33
round(t(TInv) %*% DInv %*% TInv, digits=2)
## 10 x 10 sparse Matrix of class "dgCMatrix"
## .
und(crossprod(sqrt(DInv) %*% TInv), digits=2)
## 10 x 10 sparse Matrix of class "dsCMatrix"
##  [1,]  1.5  0.50 -1.0  .     .     .     .     .     .     .   
## [2,] 0.5 2.33 -0.5 -0.67 . -1.00 . . . .
## [3,] -1.0 -0.50 3.0 0.50 -1.00 -1.00 . . . .
## [4,] . -0.67 0.5 1.83 -1.00 . . . . .
## [5,] . . -1.0 -1.00 3.23 1.23 -1.23 -1.23 . .
## [6,] . -1.00 -1.0 . 1.23 3.23 -1.23 -1.23 . .
## [7,] . . . . -1.23 -1.23 2.46 . . .
## [8,] . . . . -1.23 -1.23 . 3.04 0.58 -1.16
## [9,] . . . . . . . 0.58 1.58 -1.16
## [10,] . . . . . . . -1.16 -1.16

# T
o c
) - crossprod(sqrt(DInv) %*% TInv)

The second method (using crossprod) is preferred as it leads directly to symmetric matrix (dsCMatrix), which stores only upper or lower triangle. And make sure you do not do crossprod(TInv %*% sqrt(DInv)) as it is the wrong order of matrices.

As promised we will display (plot) pedigree by use of conversion functions of matrix objects to graph objects using the following code. Two examples are provided using the graph and igraph packages. The former does a very good job on this example, but otherwise igraph seems to have much nicer support for editing etc.

## source("")
## biocLite(pkgs=c("graph", "Rgraphviz"))
g <- as(t(TInv), "graph")

## install.packages(pkgs="igraph")
i &l
t;- igraph.from.graphNEL(graphNEL=g)
i)$label <- 1:10
(i, layout=layout.kamada.kawai)
## tkplot(i)

by Gregor Gorjanc ( at August 13, 2013 02:28 PM

July 02, 2013

Gregor Gorjanc

Parse arguments of an R script

R can be used also as a scripting tool. We just need to add shebang in the first line of a file (script):


and then the R code should follow.

Often we want to pass arguments to such a script, which can be collected in the script by the commandArgs() function. Then we need to parse the arguments and conditional on them do something. I came with a rather general way of parsing these arguments using simply these few lines:

## Collect arguments
args <- commandArgs(TRUE)
## Default setting when no arguments passed
if(length(args) < 1) {
args <- c("--help")
## Help section
if("--help" %in% args) {
The R Script
--arg1=someValue - numeric, blah blah
--arg2=someValue - character, blah blah
--arg3=someValue - logical, blah blah
--help - print this text
./test.R --arg1=1 --arg2="
output.txt" --arg3=TRUE \n\n")
## Parse arguments (we expect the form --arg=value)
parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
argsDF <-"rbind", parseArgs(args)))
argsL <- as.list(as.character(argsDF$V2))
names(argsL) <- argsDF$V1
## Arg1 default
if(is.null(args$arg1)) {
## do something
## Arg2 default
if(is.null(args$arg2)) {
## do something
## Arg3 default
if(is.null(args$arg3)) {
## do something
## ... your code here ...
Created by Pretty R at

It is some work, but I find it pretty neat and use it for quite a while now. I do wonder what others have come up for this task. I hope I did not miss some very general solution.

by Gregor Gorjanc ( at July 02, 2013 04:55 PM

March 24, 2013

Romain Francois



This blog is moving to The new one is powered by wordpress and gets a subdomain of

See you there

by romain francois at March 24, 2013 03:52 PM

March 17, 2013

Modern Toolmaking

caretEnsemble Classification example

Here's a quick demo of how to fit a binary classification model with caretEnsemble.  Please note that I haven't spent as much time debugging caretEnsemble for classification models, so there's probably more bugs than my last post.  Also note that multi class models are not yet supported.

Right now, this code fails for me if I try a model like a nnet or an SVM for stacking, so there's clearly bugs to fix.

The greedy model relies 100% on the gbm, which makes sense as the gbm has an AUC of 1 on the training set.  The linear model uses all of the models, and achieves an AUC of .5.  This is a little weird, as the gbm, rf, SVN, and knn all achieve an AUC of close to 1.0 on the training set, and I would have expected the linear model to focus on these predictions. I'm not sure if this is a bug, or a failure of my stacking model.

by Zachary Deane-Mayer ( at March 17, 2013 04:04 AM