Planet R

November 27, 2015


New package pch with initial version 1.0

Package: pch
Type: Package
Title: Piecewise Constant Hazards Models for Censored and Truncated Data
Version: 1.0
Date: 2015-11-26
Author: Paolo Frumento
Maintainer: Paolo Frumento <>
Description: Using piecewise constant hazards models is a very flexible approach for the analysis of survival data. The time line is divided into sub-intervals; for each interval, a different hazard is estimated using Poisson regression.
Depends: survival
Imports: stats
License: GPL-2
RoxygenNote: 5.0.0
Packaged: 2015-11-27 11:21:27 UTC; paofru
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-11-27 14:59:46

More information about pch at CRAN

November 27, 2015 01:13 PM

November 26, 2015


New package forestmodel with initial version 0.3.3

Package: forestmodel
Type: Package
Title: Forest Plots from Regression Models
Version: 0.3.3
Date: 2015-11-25
Author: Nick Kennedy <>
Maintainer: Nick Kennedy <>
Description: Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph().
License: GPL-2
LazyData: TRUE
Depends: R (>= 3.2.0), ggplot2 (>= 1.0.1)
Imports: dplyr (>= 0.4.2), broom (>= 0.3.7), lazyeval (>= 0.1.10)
Suggests: survival, metafor
NeedsCompilation: no
Packaged: 2015-11-26 14:10:02 UTC; Nick
Repository: CRAN
Date/Publication: 2015-11-26 17:03:11

More information about forestmodel at CRAN

November 26, 2015 03:13 PM

New package NEpiC with initial version 1.0

Package: NEpiC
Type: Package
Title: Network Assisted Algorithm for Epigenetic Studies Using Mean and Variance Combined Signals
Version: 1.0
Date: 2015-11-26
Author: Peifeng Ruan
Depends: igraph
Maintainer: Peifeng Ruan <>
Description: Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
License: GPL-2
Packaged: 2015-11-26 09:35:56 UTC; win7
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-11-26 14:15:18

More information about NEpiC at CRAN

November 26, 2015 01:13 PM

New package GroupTest with initial version 1.0.1

Package: GroupTest
Type: Package
Title: Multiple Testing Procedure for Grouped Hypotheses
Version: 1.0.1
Date: 2015-11-20
Author: Zhigen Zhao
Maintainer: Zhigen Zhao <>
Description: Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
License: GPL-3
Packaged: 2015-11-26 12:38:17 UTC; zhaozhg
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-11-26 14:25:49

More information about GroupTest at CRAN

November 26, 2015 01:13 PM

November 24, 2015

Journal of Statistical Software

November 17, 2015

Dirk Eddelbuettel

RcppAnnoy 0.0.7

A new version of RcppAnnoy, our Rcpp-based R integration of the nifty Annoy library by Erik, is now on CRAN. Annoy is a small, fast, and lightweight C++ template header library for approximate nearest neighbours.

This release mostly just catches up with the Annoy release 1.6.2 of last Friday. No new features were added on our side.

Courtesy of CRANberries, there is also a diffstat report for this release.

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

November 17, 2015 02:54 AM

November 16, 2015

Dirk Eddelbuettel

Rcpp 0.12.2: More refinements

The second update in the 0.12.* series of Rcpp is now on the CRAN network for GNU R. As usual, I will also push a Debian package. This follows the 0.12.0 release from late July which started to add some serious new features, and builds upon the 0.12.1 release in September. It also marks the sixth release this year where we managed to keep a steady bi-montly release frequency.

Rcpp has become the most popular way of enhancing GNU R with C or C++ code. As of today, 512 packages on CRAN depend on Rcpp for making analytical code go faster and further. That is up by more than fifty package from the last release in September (and we recently blogged about crossing 500 dependents).

This release once again features pull requests from two new contributors with Nathan Russell and Tianqi Chen joining in. As shown below, other recent contributors (such as such as Dan) are keeping at it too. Keep'em coming! Luke Tierney also email about a code smell he spotted and which we took care of. A big Thank You! to everybody helping with code, bug reports or documentation. See below for a detailed list of changes extracted from the NEWS file.

Changes in Rcpp version 0.12.2 (2015-11-14)

  • Changes in Rcpp API:

    • Correct return type in product of matrix dimensions (PR #374 by Florian)

    • Before creating a single String object from a SEXP, ensure that it is from a vector of length one (PR #376 by Dirk, fixing #375).

    • No longer use STRING_ELT as a left-hand side, thanks to a heads-up by Luke Tierney (PR #378 by Dirk, fixing #377).

    • Rcpp Module objects are now checked more carefully (PR #381 by Tianqi, fixing #380)

    • An overflow in Matrix column indexing was corrected (PR #390 by Qiang, fixing a bug reported by Allessandro on the list)

    • Nullable types can now be assigned R_NilValue in function signatures. (PR #395 by Dan, fixing issue #394)

    • operator<<() now always shows decimal points (PR #396 by Dan)

    • Matrix classes now have a transpose() function (PR #397 by Dirk fixing #383)

    • operator<<() for complex types was added (PRs #398 by Qiang and #399 by Dirk, fixing #187)

  • Changes in Rcpp Attributes:

    • Enable export of C++ interface for functions that return void.

  • Changes in Rcpp Sugar:

    • Added new Sugar function cummin(), cummax(), cumprod() (PR #389 by Nathan Russell fixing #388)

    • Enabled sugar math operations for subsets; e.g. x[y] + x[z]. (PR #393 by Kevin and Qiang, implementing #392)

  • Changes in Rcpp Documentation:

    • The NEWS file now links to GitHub issue tickets and pull requests.

    • The Rcpp.bib file with bibliographic references was updated.

Thanks to CRANberries, you can also look at a diff to the previous release As always, even fuller details are on the Rcpp Changelog page and the Rcpp page which also leads to the downloads page, the browseable doxygen docs and zip files of doxygen output for the standard formats. A local directory has source and documentation too. 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.

November 16, 2015 03:43 AM

November 13, 2015

Bioconductor Project Working Papers

Meta-analysis of genome-wide association studies with correlated individuals: application to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Investigators often meta-analyze multiple genome-wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta-analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta-analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community. We propose a novel mixed-effect model for meta-analysis, “MetaCor", which accounts for correlation between stratum-specific effect estimates. Simulations show that MetaCor controls inflation better than alternatives such as ignoring the correlation between the strata or analyzing all strata together in a “pooled" GWAS, especially with different minor allele frequencies (MAF) between strata. We illustrate the benefits of MetaCor on two GWASs in the HCHS/SOL. Analysis of dental caries (tooth decay) stratified by ancestry group detected a genome-wide significant SNP (rs7791001, p-value=3.66x10-8, compared to 4.67x10-7 in pooled), with different MAF between strata. Stratified analysis of BMI by ancestry group and sex reduced over-all inflation from λGC=1.050 (pooled) to λGC=1.028 (MetaCor). Furthermore, even after removing close relatives to obtain nearly uncorrelated strata, a naïve stratified analysis resulted in λGC=1.058 compare to λGC=1.027 for MetaCor.

by Tamar Sofer et al. at November 13, 2015 11:53 PM

November 09, 2015

Journal of the Royal Statistical Society: Series A

November 07, 2015

Journal of the Royal Statistical Society: Series C

RCpp Gallery

Serialize and Deserialize a C++ Object in Rcpp

This post shows how to serialize a C++ object into a R raw vector object—the base type used by the internal R serialization—and how to deserialize it.

The following example shows a toy C++ class and the related serialize/deserialize functions. It shows how one can use the cereal header-only C++ library and Boost Iostreams in Rcpp. It relies on the corresponding Rcereal and BH packages which need to be installed. It also requires compilation with support for the C++11 standard, which we enable via the corresponding Rcpp plugin.

// Enable C++11 via this plugin 
// [[Rcpp::plugins("cpp11")]]

// [[Rcpp::depends(Rcereal)]]
// [[Rcpp::depends(BH)]]

#include <boost/iostreams/stream.hpp>
#include <boost/iostreams/device/array.hpp>
#include <cereal/archives/binary.hpp>
#include <Rcpp.h>

struct MyClass {
    int x, y, z;

    // This method lets cereal know which data members to serialize
    template<class Archive>
    void serialize(Archive & archive) {
        archive( x, y, z ); // serialize things by passing them to the archive

using namespace Rcpp;

RawVector serialize_myclass(int x = 1, int y = 2, int z = 3) {
    MyClass my_instance;
    my_instance.x = x;
    my_instance.y = y;
    my_instance.z = z;
    RawVector retval(100);
    boost::iostreams::stream_buffer<boost::iostreams::array_sink> buf((char*) &retval[0], retval.size());
    std::ostream ss(&buf); {
        cereal::BinaryOutputArchive oarchive(ss);
    return retval;

void deserialize_myclass(RawVector src) {
    boost::iostreams::stream<boost::iostreams::array_source> ss((char*) &src[0], src.size());
    MyClass my_instance; {
        cereal::BinaryInputArchive iarchive(ss);
    Rcout << my_instance.x << "," << my_instance.y << "," << my_instance.z << std::endl;

Thanks to how Rcpp and R interact, the compiler will automatically find the header file according to // [[Rcpp::depends(Rcereal)]] and // [[Rcpp::depends(BH)]]. The member function void serialize(Archive & archive) tells cereal how to serialize and deserialize the C++ class MyClass. Data will be saved and loaded if they are passed to the argument archive.

The cereal::BinaryOutputArchive oarchive(ss); defines an instance of bineary archive. The oarchive(my_instance); archives the instance of MyClass to the output stream and writes the data to the RawVector.

Similarly, cereal::BinaryInputArchive iarchive(ss); defines an instace of bineary archive. The iarchive(my_instance); reads the data from the RawVector and restores the content to the instance of MyClass.

Let’s try these two functions in R:

v <- serialize_myclass(1, 2, 4)
[1] 01 00 00 00 02 00

As you can see, the instance of MyClass is successfully saved to v and loaded from v.

The API of cereal is similar to the one of Boost Serialization. The main difference is that the cereal prefers using () to send data to archives. Please visit the transition from Boost page for more details about the difference between cereal and Boost Serialization.

The most important advantage may be that cereal is header-only, which makes it easier to write portable package with cereal compared to Boost Serialization.

November 07, 2015 12:00 AM

November 05, 2015

Bioconductor Project Working Papers

C-learning: a New Classification Framework to Estimate Optimal Dynamic Treatment Regimes

Personalizing treatment to accommodate patient heterogeneity and the evolving nature of a disease over time has received considerable attention lately. A dynamic treatment regime is a set of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential classification problem and is equivalent to sequentially minimizing a weighted expected misclassification error. This general classification perspective targets the exact goal of optimally individualizing treatments and is new and fundamentally different from existing methods. Based on this fresh classification perspective, we propose a novel, powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage till the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient’s characteristics and treatment history to dramatically improves performance, hence enjoying the advantages of both the traditional outcome regression based methods (Q-and A- learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies.

by Baqun Zhang et al. at November 05, 2015 08:52 PM

October 31, 2015

Dirk Eddelbuettel


armadillo image

Yet another monthly upstream Armadillo update gets us the first changes to the new the 6.* series. This was preceded by two uploads of test released to GitHub-only. These two were tested both against all reverse-dependencies as usual. A matching upload to Debian will follow 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.

This release is fairly straightforward with few changes:

Changes in RcppArmadillo version (2015-10-31)

  • Upgraded to Armadillo 6.200.0 ("Midnight Blue Deluxe")

    • expanded diagmat() to handle non-square matrices and arbitrary diagonals

    • expanded trace() to handle non-square matrices

Courtesy of CRANberries, there is also a diffstat report for the most recent CRAN release. As always, 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 31, 2015 10:26 PM

October 30, 2015

Dirk Eddelbuettel

littler 0.3.0 -- on CRAN !!

max-heap image

A new major release of littler is now available. And for the first time in the nine years since 2006 when Jeff started the effort (which I joined not long after) we are now a CRAN package.

This required a rewrite of the build system, foregoing the calls to aclocal, autoheader, automake and leaving just a simpler autoconf layer creating a configure script and a simple src/ Not only does R provide such a robust and well-understood build system (which I got to understand reasonably well given all my R packages, but being on CRAN and leveraging its mechanism for installation and upgrades is clearly worth the change.

There may be a moment or two of transition. While we can create a binary in an R package, we cannot (generally) copy to /usr/bin or /usr/local/bin as part of the build process (for lack of write-rights to those directories). So if you do not have r in the $PATH and load the package, it makes a suggestion (which needs a linebreak which I added here):

R> library(littler)
The littler package provides 'r' as a binary.
You could link to the 'r' binary installed in
'/usr/local/lib/R/site-library/littler/bin/r' from
'/usr/local/bin' in order to use 'r' for scripting.

Similarly, you could copy (or softlink) r to ~/bin if that is in your $PATH.

The Debian (and Ubuntu) packages will continue to provide /usr/bin/r as before. Note thah these packages will now be called r-cran-littler to match all other CRAN packages.

The NEWS file entry is below.

Changes in littler version 0.3.0 (2015-10-29)

  • Changes in build system

    • First CRAN Release as R package following nine years of source releases

    • Script configure, src/ and remainder of build system rewritten to take advantage of the R package build infrastructure

    • Reproducible builds are better supported as the (changing) compilation timestamps etc are only inserted for 'verbose builds' directly off the git repo, but not for Debian (or CRAN) builds off the release tarballs

  • Changes in littler functionality

    • Also source $R_HOME/etc/ and ~/.Rprofile if present

  • Changes in littler documentation

    • Added new vignette with examples

Full details for the littler release are provided as usual at the ChangeLog page.

The code is available via the GitHub repo, from tarballs off my littler page and the local directory here -- and now of course all from its CRAN page and via install.packages("littler"). A fresh package has gone to the incoming queue at Debian where it will a few days as the binary packages was renamed from littler to r-cran-littler matching all other CRAN packages. Michael Rutter will probably have new Ubuntu binaries at CRAN once the source package gets into Debian proper.

Comments and suggestions are welcome 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 30, 2015 01:50 AM

October 28, 2015

Bioconductor Project Working Papers

Removing inter-subject technical variability in magnetic resonance imaging studies

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect and other technical artifacts is still present after standard intensity normalization in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, we decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer’s disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We compare RAVEL to intensity-normalization-only methods, histogram matching, and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects by using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is generalizable to many imaging modalities, and shows promise for longitudinal studies. Additionally, because the choice of the control region is left to the user, RAVEL can be applied in studies of many brain disorders.

by Jean-Philippe Fortin et al. at October 28, 2015 09:16 PM

October 16, 2015

Bioconductor Project Working Papers

An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions

We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which represent a generalization of the maximum mean discrepancy tests described in Gretton et al. [2006], using recent developments from the higher-order pathwise differentiability literature. Despite their complex derivation, the associated test statistics can be expressed rather simply as U-statistics. We study the asymptotic behavior of the proposed tests under the null hypothesis and under both fixed and local alternatives. We provide examples to which our tests can be applied and show that they perform well in a simulation study. As an important special case, our proposed tests can be used to determine whether an unknown function, such as the conditional average treatment effect, is equal to zero almost surely.

by Alexander R. Luedtke et al. at October 16, 2015 05:15 PM

September 30, 2015

Statistical Modelling

The Altham-Poisson distribution

The multiplicative binomial model was introduced as a generalization of the binomial distribution for modelling correlated binomial data. This distribution has not been extensively explored and is revisited in the present study. Some properties of the multiplicative binomial distribution, such as, expressions for the factorial moments and the information matrix, are investigated. The distribution is also extended to accommodate data arising from a Wadley's problem setting which is frequently encountered in dose–mortality studies and is one in which the number of organisms initially treated with a drug is unobserved. The Altham–Poisson distribution is introduced by modelling the unobserved initial number of organisms, as specified by n in the multiplicative binomial model, with a Poisson distribution and its suitability for overdispersed data from a Wadley's problem setting is explored.

by Leask, K. L., Haines, L. M. at September 30, 2015 10:42 AM

Modelling volatility using a non-homogeneous martingale model for processes with constant mean on count data

In this article a non-homogeneous martingale model is proposed to model volatility in a stochastic time series of count data with constant mean. The approach is derived from a general non-homogeneous birth-and-death process, in which the mean and the variance of population size can vary as a function of time. This model can be important in modelling early warning signals that there is going to be a change of state in a complex system. The net reproduction ratio obtained from fitting a non-homogeneous birth–death model can be used as an additional tool to compare this model with a model where there is no change in the mean over the observation period. These models and procedures are illustrated with quarterly Methicillin resistant staphylococcus aureus prevalence data registered since 2001 from three Acute Trusts of hospitals of the National Health Service in Great Britain.

by van den Broek, J. at September 30, 2015 10:42 AM

Expectile and quantile regression--David and Goliath?

Recent interest in modern regression modelling has focused on extending available (mean) regression models by describing more general properties of the response distribution. An alternative approach is quantile regression where regression effects on the conditional quantile function of the response are assumed. While quantile regression can be seen as a generalization of median regression, expectiles as alternative are a generalized form of mean regression.

Generally, quantiles provide a natural interpretation even beyond the 0.5 quantile, the median. A comparable simple interpretation is not available for expectiles beyond the 0.5 expectile, the mean. Nonetheless, expectiles have some interesting properties, some of which are discussed in this article. We contrast the two approaches and show how to get quantiles from a fine grid of expectiles. We compare such quantiles from expectiles with direct quantile estimates regarding efficiency. We also look at regression problems where both quantile and expectile curves have the undesirable property that neighbouring curves may cross each other. We propose a modified method to estimate non-crossing expectile curves based on splines. In an application, we look at the expected shortfall, a risk measure used in finance, which requires both expectiles and quantiles for estimation and which can be calculated easily with the proposed methods in the article.

by Waltrup, L. S., Sobotka, F., Kneib, T., Kauermann, G. at September 30, 2015 10:42 AM

Analyzing bivariate ordinal data with CUB margins

Statistical modelling for ordinal data has received a considerable attention in the literature, and a consolidated theory relying on Generalized Linear Model approach has been developed. In this article, we present an innovative technique for modelling bivariate ordinal data. In particular, we consider the method introduced by Plackett for constructing a one-parameter bivariate distribution from given margins, and we apply it in order to represent correlated ordinal variables which individually follows a CUB model. This is a univariate mixture distribution defined by the convex Combination of a Uniform and a shifted Binomial distribution whose parameters may be related to rater's covariates. The article shows how the bivariate distribution can be defined and how its characterizing parameter, which describes the association between the component random variables, can be related to the subject's covariates. The proposed approach is applied to the study of two key drivers of extra virgin olive oil consumption in Italy. The technique allows a representation of the data whose meaning can be easily interpreted providing useful information for management support.

by Corduas, M. at September 30, 2015 10:42 AM

August 24, 2015


R, Python, and SAS: Getting Started with Linear Regression

Consider the linear regression model, $$ y_i=f_i(\boldsymbol{x}|\boldsymbol{\beta})+\varepsilon_i, $$ where $y_i$ is the response or the dependent variable at the $i$th case, $i=1,\cdots, N$ and the predictor or the independent variable is the $\boldsymbol{x}$ term defined in the mean function $f_i(\boldsymbol{x}|\boldsymbol{\beta})$. For simplicity, consider the following simple linear regression (SLR) model, $$ y_i=\beta_0+\beta_1x_i+\varepsilon_i. $$ To obtain the (best) estimate of $\beta_0$ and $\beta_1$, we solve for the least residual sum of squares (RSS) given by, $$ S=\sum_{i=1}^{n}\varepsilon_i^2=\sum_{i=1}^{n}(y_i-\beta_0-\beta_1x_i)^2. $$ Now suppose we want to fit the model to the following data, Average Heights and Weights for American Women, where weight is the response and height is the predictor. The data is available in R by default.

The following is the plot of the residual sum of squares of the data base on the SLR model over $\beta_0$ and $\beta_1$, note that we standardized the variables first before plotting it,
Error Surface
If you are interested on the codes of the above figure, please click here. To minimize this elliptic paraboloid, differentiation has to be done with respect to the parameters, and then equate this to zero to obtain the stationary point, and finally solve for $\beta_0$ and $\beta_1$. For more on derivation of the estimates of the parameters see reference 1.

Simple Linear Regression in R

In R, we can fit the model using the lm function, which stands for linear models, i.e.

Formula, defined above as {response ~ predictor}, is a handy method for fitting model to the data in R. Mathematically, our model is $$ weight = \beta_0 + \beta_1 (height) + \varepsilon. $$ The summary of it is obtain by running model %>% summary or for non-magrittr user summary(model), given the model object defined in the previous code,

The Coefficients section above returns the estimated coefficients of the model, and these are $\beta_0 = -87.51667$ and $\beta_1=3.45000$ (it should be clear that we used the unstandardized variables for obtaining these estimates). The estimates are both significant base on the p-value under .05 and even in .01 level of the test. Using the estimated coefficients along with the residual standard error we can now construct the fitted line and it's confidence interval as shown below.
Fig 1. Plot of the Data and the Predicted Values in R.

Simple Linear Regression in Python

In Python, there are two modules that have implementation of linear regression modelling, one is in scikit-learn (sklearn) and the other is in Statsmodels (statsmodels). For example we can model the above data using sklearn as follows:

Above output is the estimate of the parameters, to obtain the predicted values and plot these along with the data points like what we did in R, we can wrapped the functions above into a class called linear_regression say, that requires Seaborn package for neat plotting, see the codes below:

Using this class and its methods, fitting the model to the data is coded as follows:

The predicted values of the data points is obtain using the predict method,

And Figure 2 below shows the plot of the predicted values along with its confidence interval and data points.
Fig 2. Plot of the Data and the Predicted Values in Python.
If one is only interested on the estimates of the model, then LinearRegression of scikit-learn is sufficient, but if the interest on other statistics such as that returned in R model summary is necessary, the said module can also do the job but might need to program other necessary routine. statsmodels, on the other hand, returns complete summary of the fitted model as compared to the R output above, which is useful for studies with particular interest on these information. So that modelling the data using simple linear regression is done as follows:

Clearly, we could spare time with statsmodels, especially in diagnostic checking involving test statistics such as Durbin-Watson and Jarque-Bera tests. We can of course add some plotting for diagnostic, but I prefer to discuss that on a separate entry.

Simple Linear Regression in SAS

Now let's consider running the data in SAS, I am using SAS Studio and in order to import the data, I saved it as a CSV file first with columns height and weight. Uploaded it to SAS Studio, in which follows are the codes below to import the data.

Next we fit the model to the data using the REG procedure,

Number of Observations Read15
Number of Observations Used15
Analysis of Variance
SourceDFSum of
F ValuePr > F
Corrected Total143362.93333   
Root MSE1.52501R-Square0.9910
Dependent Mean136.73333Adj R-Sq0.9903
Coeff Var1.11531  
Parameter Estimates
t ValuePr > |t|
Now that's a lot of output, probably the complete one. But like I said, I am not going to discuss each of these values and plots as some of it are used for diagnostic checking (you can read more on that in reference 1, and in other applied linear regression books). For now, let's just confirm the coefficients obtained -- both the estimates are the same with that in R and Python.

Multiple Linear Regression (MLR)

To extend SLR to MLR, we'll demonstrate this by simulation. Using the formula-based lm function of R, assuming we have $x_1$ and $x_2$ as our predictors, then following is how we do MLR in R:

Although we did not use intercept in simulating the data, but the obtained estimates for $\beta_1$ and $\beta_2$ are close to the true parameters (.35 and .56). The intercept, however, will help us capture the noise term we added in simulation.

Next we'll try MLR in Python using statsmodels, consider the following:

It should be noted that, the estimates in R and in Python should not (necessarily) be the same since these are simulated values from different software. Finally, we can perform MLR in SAS as follows:

Number of Observations Read100
Number of Observations Used100
Analysis of Variance
SourceDFSum of
F ValuePr > F
Corrected Total99708.36056   
Root MSE1.00255R-Square0.8624
Dependent Mean244.07327Adj R-Sq0.8595
Coeff Var0.41076  
Parameter Estimates
t ValuePr > |t|


In conclusion, SAS saves a lot of work, since it returns complete summary of the model, no doubt why companies prefer to use this, besides from their active customer support. R and Python, on the other hand, despite the fact that it is open-source, it can well compete with the former, although it requires programming skills to achieved all of the SAS outputs, but I think that's the exciting part of it -- it makes you think, and manage time. The achievement in R and Python is of course fulfilling. Hope you've learned something, feel free to share your thoughts on the comment below.


  1. Draper, N. R. and Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons, Inc. United States of America.
  2. Scikit-learn Documentation
  3. Statsmodels Documentation
  4. SAS Documentation
  5. Delwiche, Lora D., and Susan J. Slaughter. 2012. The Little SAS® Book: A Primer, Fifth Edition. Cary, NC: SAS Institute Inc.
  6. Regression with SAS. Institute for Digital Research and Education. UCLA. Retrieved August 13, 2015.
  7. Python Plotly Documentation

by Al-Ahmadgaid Asaad ( at August 24, 2015 09:29 AM

August 17, 2015


Python and R: Basic Sampling Problem

In this post, I would like to share a simple problem about sampling analysis. And I will demonstrate how to solve this using Python and R. The first two problems are originally from Sampling: Design and Analysis book by Sharon Lohr.


  1. Let $N=6$ and $n=3$. For purposes of studying sampling distributions, assume that all population values are known.

    $y_1 = 98$$y_2 = 102$$y_3=154$
    $y_4 = 133$$y_5 = 190$$y_6=175$

    We are interested in $\bar{y}_U$, the population mean. Consider eight possible samples chosen.

    Sample No.Sample, $\mathcal{S}$$P(\mathcal{S})$

    1. What is the value of $\bar{y}_U$?
    2. Let $\bar{y}$ be the mean of the sample values. For each sampling plan, find
      1. $\mathrm{E}\bar{y}$;
      2. $\mathrm{Var}\bar{y}$;
      3. $\mathrm{Bias}(\bar{y})$;
      4. $\mathrm{MSE}(\bar{y})$;
  2. Mayr et al. (1994) took an SRS of 240 children who visisted their pediatric outpatient clinic. They found the following frequency distribution for the age (in months) of free (unassisted) walking among the children:

    Age (months)91011121314151617181920
    Number of Children133544693624732511

    Find the mean and SE of the age for onset of free walking.
  3. Table 1 gives the cultivated area in acres in 1981 for 40 villages in a region. (Theory and Method of Survey) Using the arrangement (random) of data in the table, draw systematic sample of size 8. Use r ((random start) = 2,



In order to appreciate the codes, I will share some theoretical part of the solution. But our main focus here is to solve this problem computationally using Python and R.
    1. The value of $\bar{y}_U$ is coded as follows:

      Python Code R Code
    2. To obtain the sample using the sample index given in the table in the above question, we do a combination of population index of three elements, ${6\choose 3}$, first. Where the first two combinations are the samples, $\{1,2,3\}$ and $\{1,2,4\}$, and so on. Then from this list of all possible combinations of three elements, we draw those that are listed in the above table as our samples, with first sample index $\{1,3,5\}$, having population units, $\{98, 154, 190\}$. So that the following is the code of this sampling design:

      Python Code R Code
      1. Now to obtain the expected value of the average of the sample data, we compute it using $\mathrm{E}\bar{y}=\sum_{k}\bar{y}_k\mathrm{P}(\bar{y}_k)=\sum_{k}\bar{y_k}\mathrm{P}(\mathcal{S}_k)$, $\forall k\in\{1,\cdots,8\}$. So for $k = 1$, $$ \begin{aligned} \bar{y}_1\mathrm{P}(\mathcal{S}_1)&=\frac{98+154+190}{3}\mathrm{P}(\mathcal{S}_1)\\ &=\frac{98+154+190}{3}\left(\frac{1}{8}\right)=18.41667. \end{aligned} $$ Applying this to the remaining $n-1$ $k$s, and summing up the terms gives us the answer to $\mathrm{E}\bar{y}$. So that the following is the equivalent of this:

        Python Code R Code From the above code, the output tells us that $\mathrm{E}\bar{y}=140$.
      2. Next is to compute for the variance of $\bar{y}$, which is $\mathrm{Var}\bar{y}=\mathrm{E}\bar{y}^{2}-(\mathrm{E}\bar{y})^2$. So we need a function for $\mathrm{E}\bar{y}^2$, where the first term of this, $k=1$, is $\bar{y}_1^2\mathrm{P}(\mathcal{S}_1)=\left(\frac{98+154+190}{3}\right)^2\mathrm{P}(\mathcal{S}_1)=\left(\frac{98+154+190}{3}\right)^2(\frac{1}{8})=2713.3889$. Applying this to other terms and summing them up, we have following code:

        Python Code R Code So that using the above output, 20182.94, and subtracting $(\mathrm{E}\bar{y})^2$ to it, will give us the variance. And hence the succeeding code:

        Python Code: R Code: So the variance of the $\bar{y}$ is $18.9444$.
    3. The $\mathrm{Bias}$ is just the difference between the estimate and the true value. And since the estimate is unbiased ($\mathrm{E}\bar{y}=142$), so $\mathrm{Bias}=142-142=0$.
    4. $\mathrm{MSE}=\mathrm{Var}\bar{y}-(\mathrm{Bias}\bar{y})^2$, and since the $\mathrm{Bias}\bar{y}=0$. So $\mathrm{MSE}=\mathrm{Var}\bar{y}$.
  1. First we need to obtain the probability of each Age, that is by dividing the Number of Children with the total sum of it. That is why, we have p_s function defined below. After obtaining the probabilities, we can then compute the expected value using the expectation function we defined earlier.

    Python Code R Code It should be clear in the data that the average age is about 12 months old, where the plot of it is shown below, For the code of the above plot please click here. Next is to compute the standard error which is just the square root of the variance of the sample,

    Python Code R Code So the standard variability of the Age is 1.920824.
  2. Let me give you a brief discussion on the systematic sampling to help you understand the code. The idea in systematic sampling is that, given the population units numbered from 1 to $N$, we compute for the sampling interval, given by $k = \frac{N}{n}$, where $n$ is the number of units needed for the sample. After that, we choose for the random start, number between $1$ and $k$. This random start will be the first sample, and then the second unit in the sample is obtained by adding the sampling interval to the random start, and so on. There are two types of systematic sampling namely, Linear and Circular Systematic Samplings. Circular systematic sampling treats the population units numbered from $1$ to $N$ in circular form, so that if the increment step is more than the number of $N$ units, say $N+2$, the sample unit is the $2^{nd}$ element in the population, and so on. The code that I will be sharing can be used both for linear and circular, but for this particular problem only. Since there are rules in linear that are not satisfied in the function, one of which is if $k$ is not a whole number, despite that, however, you can always extend it to a more general function.

    Python Code R Code You may notice in the output above, that the index returned in Python is not the same with the index returned in R. This is because Python index starts with 0, while that in R starts with 1. So that's why we have the same population units sampled between the two language despite the differences between the index returned.


  1. Lohr, Sharon (2009). Sampling: Design and Analysis. Cengage Learning.

by Al-Ahmadgaid Asaad ( at August 17, 2015 02:47 AM

Parametric Inference: Likelihood Ratio Test by Example

Hypothesis testing have been extensively used on different discipline of science. And in this post, I will attempt on discussing the basic theory behind this, the Likelihood Ratio Test (LRT) defined below from Casella and Berger (2001), see reference 1.
Definition. The likelihood ratio test statistic for testing $H_0:\theta\in\Theta_0$ versus $H_1:\theta\in\Theta_0^c$ is \begin{equation} \label{eq:lrt} \lambda(\mathbf{x})=\frac{\displaystyle\sup_{\theta\in\Theta_0}L(\theta|\mathbf{x})}{\displaystyle\sup_{\theta\in\Theta}L(\theta|\mathbf{x})}. \end{equation} A likelihood ratio test (LRT) is any test that has a rejection region of the form $\{\mathbf{x}:\lambda(\mathbf{x})\leq c\}$, where $c$ is any number satisfying $0\leq c \leq 1$.
The numerator of equation (\ref{eq:lrt}) gives us the supremum probability of the parameter, $\theta$, over the restricted domain (null hypothesis, $\Theta_0$) of the parameter space $\Theta$, that maximizes the joint probability of the sample, $\mathbf{x}$. While the denominator of the LRT gives us the supremum probability of the parameter, $\theta$, over the unrestricted domain, $\Theta$, that maximizes the joint probability of the sample, $\mathbf{x}$. Therefore, if the value of $\lambda(\mathbf{x})$ is small such that $\lambda(\mathbf{x})\leq c$, for some $c\in [0, 1]$, then the true value of the parameter that is plausible in explaining the sample is likely to be in the alternative hypothesis, $\Theta_0^c$.

Example 1. Let $X_1,X_2,\cdots,X_n\overset{r.s.}{\sim}f(x|\theta)=\frac{1}{\theta}\exp\left[-\frac{x}{\theta}\right],x>0,\theta>0$. From this sample, consider testing $H_0:\theta = \theta_0$ vs $H_1:\theta<\theta_0$.

The parameter space $\Theta$ is the set $(0,\Theta_0]$, where $\Theta_0=\{\theta_0\}$. Hence, using the likelihood ratio test, we have $$ \lambda(\mathbf{x})=\frac{\displaystyle\sup_{\theta=\theta_0}L(\theta|\mathbf{x})}{\displaystyle\sup_{\theta\leq\theta_0}L(\theta|\mathbf{x})}, $$ where, $$ \begin{aligned} \sup_{\theta=\theta_0}L(\theta|\mathbf{x})&=\sup_{\theta=\theta_0}\prod_{i=1}^{n}\frac{1}{\theta}\exp\left[-\frac{x_i}{\theta}\right]\\ &=\sup_{\theta=\theta_0}\left(\frac{1}{\theta}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta}\right]\\ &=\left(\frac{1}{\theta_0}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta_0}\right], \end{aligned} $$ and $$ \begin{aligned} \sup_{\theta\leq\theta_0}L(\theta|\mathbf{x})&=\sup_{\theta\leq\theta_0}\prod_{i=1}^{n}\frac{1}{\theta}\exp\left[-\frac{x_i}{\theta}\right]\\ &=\sup_{\theta\leq\theta_0}\left(\frac{1}{\theta}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta}\right]=\sup_{\theta\leq\theta_0}f(\mathbf{x}|\theta). \end{aligned} $$ Now the supremum of $f(\mathbf{x}|\theta)$ over all values of $\theta\leq\theta_0$ is the MLE (maximum likelihood estimator) of $f(x|\theta)$, which is $\bar{x}$, provided that $\bar{x}\leq \theta_0$.

So that, $$ \begin{aligned} \lambda(\mathbf{x})&=\frac{\left(\frac{1}{\theta_0}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta_0}\right]} {\left(\frac{1}{\bar{x}}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\bar{x}}\right]},\quad\text{provided that}\;\bar{x}\leq \theta_0\\ &=\left(\frac{\bar{x}}{\theta_0}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta_0}\right]\exp[n]. \end{aligned} $$ And we say that, if $\lambda(\mathbf{x})\leq c$, $H_0$ is rejected. That is, $$ \begin{aligned} \left(\frac{\bar{x}}{\theta_0}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta_0}\right]\exp[n]&\leq c\\ \left(\frac{\bar{x}}{\theta_0}\right)^n\exp\left[-\displaystyle\frac{\sum_{i=1}^{n}x_i}{\theta_0}\right]&\leq c',\quad\text{where}\;c'=\frac{c}{\exp[n]}\\ n\log\left(\frac{\bar{x}}{\theta_0}\right)-\frac{n}{\theta_0}\bar{x}&\leq \log c'\\ \log\left(\frac{\bar{x}}{\theta_0}\right)-\frac{\bar{x}}{\theta_0}&\leq \frac{1}{n}\log c'\\ \log\left(\frac{\bar{x}}{\theta_0}\right)-\frac{\bar{x}}{\theta_0}&\leq \frac{1}{n}\log c-1. \end{aligned} $$ Now let $h(x)=\log x - x$, then $h'(x)=\frac{1}{x}-1$. So the critical point of $h'(x)$ is $x=1$. And to test if this is maximum or minimum, we apply second derivative test. That is, $$ h''(x)=-\frac{1}{x^2}<0,\forall x. $$ Thus, $x=1$ is a maximum. Hence, $ \log\left(\frac{\bar{x}}{\theta_0}\right)-\frac{\bar{x}}{\theta_0} $ is maximized if $\frac{\bar{x}}{\theta_0}=1\Rightarrow\bar{x}=\theta_0$. To see this consider the following plot,
LRT and its Critical Value
Above figure is the plot of $h(\bar{x})$ function with $\theta_0=1$. Given the assumption that $\bar{x}\leq \theta_0$ then assuming $R=\frac{1}{n}\log c-1$ designates the orange line above, then we reject $H_0$ if $h(\bar{x})\leq R$, if and only if $\bar{x}\leq k$. In practice, $k$ is specified to satisfy, $$ \mathrm{P}(\bar{x}\leq k|\theta=\theta_0)\leq \alpha, $$ where $\alpha$ is called the level of the test.

It follows that $X_i|\theta = \theta_0\overset{r.s.}{\sim}\exp[\theta_0]$, then $\mathrm{E}X_i=\theta_0$ and $\mathrm{Var}X_i=\theta_0^2$. If $\bar{x}=\frac{1}{n}\sum_{i=1}^{n}X_i$ and if $G_n$ is the distribution of $\frac{(\bar{x}_n-\theta_0)}{\sqrt{\frac{\theta_0^2}{n}}}$. By CLT (central limit theorem) $\lim_{n\to\infty}G_n$ converges to standard normal distribution. That is, $\bar{x}|\theta = \theta_0\overset{r.s.}{\sim}AN\left(\theta_0,\frac{\theta_0^2}{n}\right)$. $AN$ - assymptotically normal.

Thus, $$ \mathrm{P}(\bar{x}\leq k|\theta=\theta_0)=\Phi\left(\frac{k-\theta_0}{\theta_0/\sqrt{n}}\right),\quad\text{for large }n. $$ So that, $$ \mathrm{P}(\bar{x}\leq k|\theta=\theta_0)=\Phi\left(\frac{k-\theta_0}{\theta_0/\sqrt{n}}\right)\leq \alpha. $$ Plotting this gives us,
with corresponding PDF given by,
Density Function
Implying, $$ \frac{k-\theta_0}{\theta_0/\sqrt{n}}=z_{\alpha}\Rightarrow k=\theta_0+z_{\alpha}\frac{\theta_0}{\sqrt{n}}. $$ Therefore, a level-$\alpha$ test of $H_0:\theta=\theta_0$ vs $H_1:\theta<\theta_0$ is the test that rejects $H_0$ when $\bar{x}\leq\theta_0+z_{\alpha}\frac{\theta_0}{\sqrt{n}}$.

Plot's Python Codes

In case you might ask how above plots were generated:


  1. Casella, G. and Berger, R.L. (2001). Statistical Inference. Thomson Learning, Inc.
  2. Plotly Python Library Documentation

by Al-Ahmadgaid Asaad ( at August 17, 2015 02:46 AM

Parametric Inference: The Power Function of the Test

In Statistics, we model random phenomenon and make conclusions about its population. For example, in an experiment of determining the true heights of the students in the university. Suppose we take sample from the population of the students, and consider testing the null hypothesis that the average height is 5.4 ft against an alternative hypothesis that the average height is greater than 5.4 ft. Mathematically, we can represent this as $H_0:\theta=\theta_0$ vs $H_1:\theta>\theta_0$, where $\theta$ is the true value of the parameter, and $\theta_0=5.4$ is the testing value set by the experimenter. And because we only consider subset (the sample) of the population for testing the hypotheses, then we expect for errors we commit. To understand these errors, consider if the above test results into rejecting $H_0$ given that $\theta\in\Theta_0$, where $\Theta_0$ is the parameter space of the null hypothesis, in other words we mistakenly reject $H_0$, then in this case we committed a Type I error. Another is, if the above test results into accepting $H_0$ given that $\theta\in\Theta_0^c$, where $\Theta_0^c$ is the parameter space of the alternative hypothesis, then we committed a Type II error. To summarize this consider the following table,

Table 1: Two Types of Errors in Hypothesis Testing.
Accept $H_0$Reject $H_0$
$H_0$Correct DecisionType I Error
$H_1$Type II ErrorCorrect Decision

Let's formally define the power function, from Casella and Berger (2001), see reference 1.
Definition 1. The power function of a hypothesis test with rejection region $R$ is the function of $\theta$ defined by $\beta(\theta)=\mathrm{P}_{\theta}(\mathbf{X}\in R)$.
To relate the definition to the above problem, if $R$ is the rejection region of $H_0$. Then we make mistake if the sample observed, $\mathbf{x}$, $\mathbf{x}\in R$ given that $\theta\in\Theta_0$. That is, $\beta(\theta)=\mathrm{P}_{\theta}(\mathbf{X}\in R)$ is the probability of Type I error. Let's consider an example, one that is popularly used in testing the sample mean. The example below is the combined problem of Example 8.3.3 and Exercise 8.37 (a) of reference 1.

Example 1. Let $X_1,\cdots, X_n\overset{r.s.}{\sim}N(\mu,\sigma^2)$ -- normal population where $\sigma^2$ is known. Consider testing $H_0:\theta\leq \theta_0$ vs $H_1:\theta> \theta_0$, obtain the likelihood ratio test (LRT) statistic and its power function.

Solution:The LRT statistic is given by $$ \lambda(\mathbf{x})=\frac{\displaystyle\sup_{\theta\leq\theta_0}L(\theta|\mathbf{x})}{\displaystyle\sup_{-\infty<\theta<\infty}L(\theta|\mathbf{x})}, $$ where $$ \begin{aligned} \sup_{\theta\leq\theta_0}L(\theta|\mathbf{x})&=\sup_{\theta\leq\theta_0}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\theta)^2}{2\sigma^2}\right]\\ &=\sup_{\theta\leq\theta_0}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\theta)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\theta_0)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\bar{x}+\bar{x}-\theta_0)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left\{-\displaystyle\sum_{i=1}^{n}\left[\frac{(x_i-\bar{x})^2+2(x_i-\bar{x})(\bar{x}-\theta_0)+(\bar{x}-\theta_0)^2}{2\sigma^2}\right]\right\}\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2+n(\bar{x}-\theta_0)^2}{2\sigma^2}\right], \text{since the middle term is 0.} \end{aligned} $$ And $$ \begin{aligned} \sup_{-\infty<\theta<\infty}L(\theta|\mathbf{x})&=\sup_{-\infty<\theta<\infty}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\theta)^2}{2\sigma^2}\right]\\ &=\sup_{-\infty<\theta<\infty}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\theta)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\bar{x})^2}{2\sigma^2}\right],\quad\text{since }\bar{x}\text{ is the MLE of }\theta.\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{n-1}{n-1}\displaystyle\sum_{i=1}^{n}\frac{(x_i-\bar{x})^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2}{2\sigma^2}\right],\\ \end{aligned} $$ so that $$ \begin{aligned} \lambda(\mathbf{x})&=\frac{\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2+n(\bar{x}-\theta_0)^2}{2\sigma^2}\right]}{\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2}{2\sigma^2}\right]}\\ &=\exp\left[-\frac{n(\bar{x}-\theta_0)^2}{2\sigma^2}\right].\\ \end{aligned} $$ And from my previous entry, $\lambda(\mathbf{x})$ is rejected if it is small, such that $\lambda(\mathbf{x})\leq c$ for some $c\in[0,1]$. Hence, $$ \begin{aligned} \lambda(\mathbf{x})&=\exp\left[-\frac{n(\bar{x}-\theta_0)^2}{2\sigma^2}\right]< c\\&\Rightarrow-\frac{n(\bar{x}-\theta_0)^2}{2\sigma^2}<\log c\\ &\Rightarrow\frac{\bar{x}-\theta_0}{\sigma/\sqrt{n}}>\sqrt{-2\log c}. \end{aligned} $$ So that $H_0$ is rejected if $\frac{\bar{x}-\theta_0}{\sigma/\sqrt{n}}> c'$ for some $c'=\sqrt{-2\log c}\in[0,\infty)$. Now the power function of the test, is the probability of rejecting the null hypothesis given that it is true, or the probability of the Type I error given by, $$ \begin{aligned} \beta(\theta)&=\mathrm{P}\left[\frac{\bar{x}-\theta_0}{\sigma/\sqrt{n}}> c'\right]\\ &=\mathrm{P}\left[\frac{\bar{x}-\theta+\theta-\theta_0}{\sigma/\sqrt{n}}> c'\right]\\ &=\mathrm{P}\left[\frac{\bar{x}-\theta}{\sigma/\sqrt{n}}+\frac{\theta-\theta_0}{\sigma/\sqrt{n}}> c'\right]\\ &=\mathrm{P}\left[\frac{\bar{x}-\theta}{\sigma/\sqrt{n}}> c'-\frac{\theta-\theta_0}{\sigma/\sqrt{n}}\right]\\ &=1-\mathrm{P}\left[\frac{\bar{x}-\theta}{\sigma/\sqrt{n}}\leq c'+\frac{\theta_0-\theta}{\sigma/\sqrt{n}}\right]\\ &=1-\Phi\left[c'+\frac{\theta_0-\theta}{\sigma/\sqrt{n}}\right]. \end{aligned} $$ To illustrate this, consider $\theta_0=5.4,\sigma = 1,n=30$ and $c'=1.645$. Then the plot of the power function as a function of $\theta$ is,
Power Function
Since $\beta$ is an increasing function with unit range, then $$ \alpha = \sup_{\theta\leq\theta_0}\beta(\theta)=\beta(\theta_0)=1-\Phi(c'). $$ So that using values we set for the above graph, $\alpha=0.049985\approx 0.05$, $\alpha$ here is called the size of the test since it is the supremum of the power function over $\theta\leq\theta_0$, see reference 1 for level of the test. Now let's investigate the power function above, the probability of committing Type I error, $\beta(\theta), \forall \theta\leq \theta_0$, is acceptably small. However, the probability of committing Type II error, $1-\beta(\theta), \forall \theta > \theta_0$, is too high as we can see in the following plot,
Type II Error
Therefore, it's better to investigate the error structure when considering the power of the test. From Casella and Berger (2001), the ideal power function is 0 $\forall\theta\in\Theta_0$ and 1 $\forall\theta\in\Theta_0^c$. Except in trivial situations, this ideal cannot be attained. Qualitatively, a good test has power function near 1 for most $\theta\in\Theta_0^c$ and $\theta\in\Theta_0$. Implying, one that has steeper power curve.

Now an interesting fact about power function is that it depends on the sample size $n$. Suppose in our experiment above we want the Type I error to be 0.05 and the Type II error to be 0.1 if $\theta\geq \theta_0+\sigma/2$. Since the power function is increasing, then we have $$ \beta(\theta_0)=0.05\Rightarrow c'=1.645\quad\text{and}\quad 1 - \beta(\theta_0+\sigma/2)=0.1\Rightarrow\beta(\theta_0+\sigma/2)=0.9. $$ Where $$ \begin{aligned} \beta(\theta_0+\sigma/2)&=1-\Phi\left[c' +\frac{\theta_0-\sigma/2-\theta_0}{\sigma/\sqrt{n}}\right]\\ &=1-\Phi\left[c' - \frac{\sqrt{n}}{2}\right]\\ 0.9&=1-\Phi\left[1.645 - \frac{\sqrt{n}}{2}\right]\\ 0.1&=\Phi\left[1.645 - \frac{\sqrt{n}}{2}\right].\\ \end{aligned} $$ Hence, $n$ is chosen such that it solves the above equation. That is, $$ \begin{aligned} 1.645 - \frac{\sqrt{n}}{2}&=-1.28155,\quad\text{since }\Phi(-1.28155)=0.1\\ \frac{3.29 - \sqrt{n}}{2}&=-1.28155\\ 3.29 - \sqrt{n}&=-2.5631\\ n&=(3.29+2.5631)^2=34.25878,\;\text{take }n=35. \end{aligned} $$ For purpose of illustration, we'll consider the non-rounded value of $n$. Below is the plot of this,
Power Function with Sample Size
And for different values of $n$, consider the following power functions
Effect of Sample Size on Power Function
From the above plot, the larger the sample size, $n$, the steeper the curve implying a better error structure. To see this, try hovering over the lines in the plot, and you'll witness a fast departure for values of large $n$ on the unit range, this characteristics contribute to the sensitivity of the test.

Plot's Python Codes

In case you want to reproduce the above plots, click here for the source code.


  1. Casella, G. and Berger, R.L. (2001). Statistical Inference. Thomson Learning, Inc.
  2. Plotly Python Library Documentation

by Al-Ahmadgaid Asaad ( at August 17, 2015 02:41 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 25, 2015

RCpp Gallery

Stochastic SIR Epidemiological Compartment Model


This post is a simple introduction to Rcpp for disease ecologists, epidemiologists, or dynamical systems modelers - the sorts of folks who will benefit from a simple but fully-working example. My intent is to provide a complete, self-contained introduction to modeling with Rcpp. My hope is that this model can be easily modified to run any dynamical simulation that has dependence on the previous time step (and can therefore not be vectorized).

This post uses a classic Susceptible-Infected-Recovered (SIR) epidemiological compartment model. Compartment models are simple, commonly-used dynamical systems models. Here I demonstrate the tau-leap method, where a discrete number of individuals move probabilistically between compartments at fixed intervals in time. In this model, the wait-times within class are exponentially distributed, and the number of transitions between states in a fixed time step are Poisson distributed.

This model is parameterized for the spread of measles in a closed population, where the birth rate (nu) = death rate (mu). The transmission rate (beta) describes how frequently susceptible (S) and infected (I) individuals come into contact, and the recovery rate (gamma) describes the the average time an individual spends infected before recovering.

C++ Code

Note: C++ Functions must be marked with the following comment for use in R: // [[Rcpp::export]].

When functions are exported in this way via sourceCpp(), RNG setup is automatically handled to use R’s engine. For details on random number generation with Rcpp, see the this Rcpp Gallery post.

#include <Rcpp.h>
using namespace Rcpp;

// This function will be used in R! Evaluates the number of events 
// and updates the states at each time step
// [[Rcpp::export]]
List tauleapCpp(List params) {
    // chained operations are tricky in cpp
    // pull out list w/in list into its own object
    List init = params["init"];
    // use Rcpp as() function to "cast" R vector to cpp scalar
    int nsteps = as<int>(params["nsteps"]);

    // initialize each state vector in its own vector
    // set all vals to initial vals
    // I use doubles (NumericVector) rather than 
    // ints (IntegerVector), since rpois returns double,
    // and the domain of double is a superset of int
    NumericVector SS(nsteps, init["S"]);
    NumericVector II(nsteps, init["I"]);
    NumericVector RR(nsteps, init["R"]);
    NumericVector NN(nsteps, init["pop"]);
    // fill time w/zeros
    NumericVector time(nsteps);

    // pull out params for easy reading 
    double nu = params["nu"];
    double mu = params["mu"];
    double beta = params["beta"];
    double gamma = params["gamma"];
    double tau = params["tau"];

    // Calculate the number of events for each step, update state vectors
    for (int istep = 0; istep < (nsteps-1); istep++) {
        // pull out this step's scalars for easier reading
        // and to avoid compiler headaches
        double iS = SS[istep];
        double iI = II[istep];
        double iR = RR[istep];
        double iN = NN[istep];
        // State Equations
        // R::rpois always returns a single value
        // to return multiple (e.g. Integer/NumericVector, 
        // use Rcpp::rpois(int ndraw, param) and friends
        double births = R::rpois(nu*iN*tau);
        // Prevent negative states
        double Sdeaths = std::min(iS, R::rpois(mu*iS*tau));
        double maxtrans = R::rpois(beta*(iI/iN)*iS*tau);
        double transmission = std::min(iS-Sdeaths, maxtrans);
        double Ideaths = std::min(iI, R::rpois(mu*iI*tau));
        double recovery = std::min(iI-Ideaths, R::rpois(gamma*iI*tau));
        double Rdeaths = std::min(iR, R::rpois(mu*iR*tau));
        // Calculate the change in each state variable
        double dS = births-Sdeaths-transmission;
        double dI = transmission-Ideaths-recovery;
        double dR = recovery-Rdeaths;
        // Update next timestep
        SS[istep+1] = iS + dS;
        II[istep+1] = iI + dI;
        RR[istep+1] = iR + dR;
        // Sum population
        NN[istep+1] = iS + iI + iR + dS + dI + dR;
        // time in fractional years (ie units parameters are given in)
        time[istep+1] = (istep+1)*tau;
    // Return results as data.frame
    DataFrame sim = DataFrame::create(
        Named("time") = time,
        Named("S") = SS,
        Named("I") = II,
        Named("R") = RR,
        Named("N") = NN
    return sim;

R Code

Next we need to parameterize the model. Modelers often deal with many named parameters, some of which are dependent on each other. My goal here is to specify parameters in R once (and only once), and then pass all of them together to the main cpp function.

## Specify model parameters use within() to make assignments *inside* an 
## empty (or existing) list. Yhis is a handy R trick that allows you to 
## refer to existing list elements on right hand side (RHS)
## Note the braces, <-, and and no commas here:  everything in braces is a
## regular code block, except that assignments happen *inside* the list
params <- list()
params <- within(params, {
    ## set rng state
    seed <- 0
    tau <- 0.001 # in years
    nyears <- 10
    ## total number of steps
    nsteps <- nyears/tau
    mu <- 1/70 #death rate
    gamma <- 365/10 #recovery rate
    R0 <- 10
    ## refers to R0 above
    beta <- R0*(gamma+mu) #transmission rate
    nu <- mu #birth rate
    ## initial conditions, list within list
    ## use within() to modify empty list, as above
    init <- within(list(), {
        pop <- 1e6
        S <- round(pop/R0)
        I <- round(pop*mu*(1-1/R0)/(gamma+mu))
        ## refers to S,I above
        R <- pop-S-I


## run the model once
result.df <- tauleapCpp(params)
nsim <- 12

## run many sims, combine all results into one data.frame
## plyr will combine results for us
result.rep <- ldply(1:nsim, function(.nn) {
    ## run the model
    result <- tauleapCpp(params)
    ## this wastes space, but is very simple and aids plotting
    result$nsim <- .nn

Plot Results

Note that the model contains no seasonality. Rather, the system experiences stochastic resonance, where the “noise” of stochastic state transitions stimulates a resonant frequency of the system (here, 2-3 years). For more information see here.

Sometimes epidemics die out. In fact, for this model, they will die out with probability = 1 as time goes to infinity!


## lattice plot of results
    xyplot(I ~ time | sprintf("Simulation %02d",nsim), 
        data=result.rep, type=c('l','g'), as.table=T,
        ylab='Infected', xlab='Year',

plot of chunk unnamed-chunk-4

April 25, 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

April 02, 2015

RCpp Gallery

Call matplotlib from R


I often use Python and matplotlib for exploring measurement data (from e.g. accelerometers), even if I use R for the actual analysis. The reason is that I like to be able to flexibly zoom into different parts of the plot using the mouse and this works well for me with matplotlib. So I decided to try to call matplotlib from R using Rcpp and Python/C API.

It was surprisingly simple to get it working and I put together a small R-package Rpyplot. The package seems to work well on Ubuntu and Windows 7 for my use cases.

A lot of the code is based on the informative Call Python from R through Rcpp post in Rcpp gallery. I decided not use Boost.Python to make compiling on Windows simpler.

This post explains how I implemented the package and hopefully it will also allow others to expand the package for their needs. If you do implement additional functionality for Rpyplot I’d appreaciate a pull request on Github.

Set up

You’ll need to have Python in your path and Python headers and matplotlib installed (id sudo apt-get install python-dev python-matplotlib in Ubuntu). In Windows I have used the Anaconda Python distribution.

The following sets the compiler flags for Ubuntu:

py_cflags <- system("python2.7-config --cflags", intern=TRUE)
Sys.setenv("PKG_CXXFLAGS"=sprintf("%s %s", Sys.getenv("PKG_CXXFLAGS"), py_cflags))
py_ldflags <- system("python2.7-config --ldflags", intern=TRUE)
Sys.setenv("PKG_LIBS"=sprintf("%s", py_ldflags))

You can have a look at the in Rpyplot-package to see how to set the flags for Windows.

Calling Python from R


The snippet below contains code required to initialize Python and imports pyplot from matplotlib and pyrun function that can be used to call Python from R. All code executed with pyrun (or PyRun_SimpleString in C++) runs Python source code in the scope of __main__ module.

#include <Rcpp.h>
#include <Python.h>
#include <stdlib.h>
#ifndef WIN32
#include <dlfcn.h>

using namespace Rcpp;

//Run Python commands from R
void pyrun(std::string command) {

//You need to call this first
void initialize_python() {
#ifndef WIN32
   dlopen("", RTLD_LAZY |RTLD_GLOBAL); //Required to import matplotlib
    pyrun("import matplotlib");
    pyrun("import matplotlib.pyplot as plt");

//Call after you're done
void finalize_python() {

Copying data

It is not enough to be able to just run Python commands from strings, but we also need to pass data from R to Python. The numvec_to_python function below copies a numeric vector from R to a Python list and adds it to Python’s __main__ module. It is then accessible to Python commands executed with pyrun.

#include <Rcpp.h>
#include <Python.h>
#include <stdlib.h>n
using namespace Rcpp;

//Convert NumericVector to Python List
PyObject* numvec_to_list(NumericVector x) {
    int n = x.length();
    PyObject *xpy = PyList_New(n); //Make new list
    PyObject *f;
    for (int i=0; i<n; i++) {
      f = PyFloat_FromDouble(x[i]);
      PyList_SetItem(xpy, i, f); //Fill list from NumericVector

//Copy a numeric vector from R to Python
void numvec_to_python(std::string name, NumericVector x) {
    PyObject *xpy = numvec_to_list(x);
    PyObject *m = PyImport_AddModule("__main__");
    PyObject *main = PyModule_GetDict(m); //Get the locals dictionary of __main__ module
    PyDict_SetItemString(main, name.c_str(), xpy); //Add variable to that dictionary


Using the functions defined above makes calling matplotlib simple. First you will need to copy a vector to Python and then you are able to plot it running Python commands using pyrun. You can see how different plots are created in Rpyplot by looking at Plot.R. The implemenation of pycontourf function also shows how to copy an R matrix to Python and convert it to a NumPy array.

Here is a small example on how the above functions can be used to create two line plots. You’ll need to call in order to open the plot, but when you do the program will hang until all opened figure windows are closed so make sure to only call it at the end of a script.

x <- seq(0, 2*pi, length = 100)
sx <- sin(x)
cx <- cos(x)

#Copy variables to Python
numvec_to_python("x", x) 
numvec_to_python("sx", sx)
numvec_to_python("cx", cx)
#Set plot size
pyrun("plt.rcParams.update({'figure.figsize' : (7,4)})") 
#Create plots
pyrun("plt.plot(x, sx)")
pyrun("plt.plot(x, cx, '--r', linewidth=2) ")
pyrun("plt.legend(('sin(x)', 'cos(x)'))")
#pyrun("") #Uncomment this line to show the plot

And here is the generated plot:

April 02, 2015 12:00 AM

March 22, 2015

RCpp Gallery

Parsing Dates and Times


R has excellent support for dates and times via the built-in Date and POSIXt classes. Their usage, however, is not always as straightforward as one would want. Certain conversions are more cumbersome than we would like: while as.Date("2015-03-22"), would it not be nice if as.Date("20150322") (a format often used in logfiles) also worked, or for that matter as.Date(20150322L) using an integer variable, or even as.Date("2015-Mar-22") and as.Date("2015Mar22")?

Similarly, many date and time formats suitable for POSIXct (the short form) and POSIXlt (the long form with accessible components) often require rather too much formatting, and/or defaults. Why for example does as.POSIXct(as.numeric(Sys.time()), origin="1970-01-01") require the origin argument on the conversion back (from fractional seconds since the epoch) into datetime—when it is not required when creating the double-precision floating point representation of time since the epoch?

But thanks to Boost and its excellent Boost Date_Time library—which we already mentioned in this post about the BH package— we can address parsing of dates and times. It permitted us to write a new function toPOSIXct() which now part of the RcppBDT package (albeit right now just the GitHub version but we expect this to migrate to CRAN “soon” as well).


We will now discuss the outline of this implementation. For full details, see the source file.

Headers and Constants

#include <boost/date_time.hpp>
#include <boost/lexical_cast.hpp>
#include <Rcpp.h>

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

namespace bt = boost::posix_time;

const std::locale formats[] = {    // this shows a subset only, see the source file for full list
    std::locale(std::locale::classic(), new bt::time_input_facet("%Y-%m-%d %H:%M:%S%f")),
    std::locale(std::locale::classic(), new bt::time_input_facet("%Y/%m/%d %H:%M:%S%f")),

    std::locale(std::locale::classic(), new bt::time_input_facet("%Y-%m-%d")),
    std::locale(std::locale::classic(), new bt::time_input_facet("%b/%d/%Y")),
const size_t nformats = sizeof(formats)/sizeof(formats[0]);

Note that we show only two datetime formats along with two date formats. The actual implementation has many more.

Core Converter

The actual conversion from string to a double (the underlying format in POSIXct) is done by the following function. It loops over all given formats, and returns the computed value after the first match. In case of failure, a floating point NA is returned.

double stringToTime(const std::string s) {

    bt::ptime pt, ptbase;

    // loop over formats and try them til one fits
    for (size_t i=0; pt == ptbase && i < nformats; ++i) {
        std::istringstream is(s);
        is >> pt;
    if (pt == ptbase) {
        return NAN;
    } else { 
        const bt::ptime timet_start(boost::gregorian::date(1970,1,1));
        bt::time_duration diff = pt - timet_start;

        // Define BOOST_DATE_TIME_POSIX_TIME_STD_CONFIG to use nanoseconds
        // (and then use diff.total_nanoseconds()/1.0e9;  instead)
        return diff.total_microseconds()/1.0e6;

Convenience Wrappers

We want to be able to convert from numeric as well as string formats. For this, we write a templated (and vectorised) function which invokes the actual conversion function for each argument. It also deals (somewhat heuristically) with two corner cases: we want 20150322 be converted from either integer or numeric, but need in the latter case distinguish this value and its rangue from the (much larger) value for seconds since the epoch. That creates a minir ambiguity: we will not be able to convert back for inputs from seconds since the epoch for the first few years since January 1, 1970. But as these are rare in the timestamp form we can accept the trade-off.

template <int RTYPE>
Rcpp::DatetimeVector toPOSIXct_impl(const Rcpp::Vector<RTYPE>& sv) {

    int n = sv.size();
    Rcpp::DatetimeVector pv(n);
    for (int i=0; i<n; i++) {
        std::string s = boost::lexical_cast<std::string>(sv[i]);
        //Rcpp::Rcout << sv[i] << " -- " << s << std::endl;

        // Boost Date_Time gets the 'YYYYMMDD' format wrong, even
        // when given as an explicit argument. So we need to test here.
        // While we are at it, may as well test for obviously wrong data.
        int l = s.size();
        if ((l < 8) ||          // impossibly short
            (l == 9)) {         // 8 or 10 works, 9 cannot
            Rcpp::stop("Inadmissable input: %s", s);
        } else if (l == 8) {    // turn YYYYMMDD into YYYY/MM/DD
            s = s.substr(0, 4) + "/" + s.substr(4, 2) + "/" + s.substr(6,2);
        pv[i] = stringToTime(s);
    return pv;

User-facing Function

Finally, we can look at the user-facing function. It accepts input in either integer, numeric or character vector form, and then dispatches accordingly to the templated internal function we just discussed. Other inputs are unsuitable and trigger an error.

// [[Rcpp::export]]
Rcpp::DatetimeVector toPOSIXct(SEXP x) {
    if (Rcpp::is<Rcpp::CharacterVector>(x)) {
        return toPOSIXct_impl<STRSXP>(x);
    } else if (Rcpp::is<Rcpp::IntegerVector>(x)) {
        return toPOSIXct_impl<INTSXP>(x); 
    } else if (Rcpp::is<Rcpp::NumericVector>(x)) {
        // here we have two cases: either we are an int like
        // 200150315 'mistakenly' cast to numeric, or we actually
        // are a proper large numeric (ie as.numeric(Sys.time())
        Rcpp::NumericVector v(x);
        if (v[0] < 21990101) {  // somewhat arbitrary cuttoff
            // actual integer date notation: convert to string and parse
            return toPOSIXct_impl<REALSXP>(x);
        } else {
            // we think it is a numeric time, so treat it as one
            return Rcpp::DatetimeVector(x);
    } else {
        Rcpp::stop("Unsupported Type");
        return R_NilValue;//not reached


A simply illustration follows. A fuller demonstration is part of the RcppBDT package. This already shows support for subsecond granularity and a variety of date formats.

## parsing character
s <- c("2004-03-21 12:45:33.123456",    # ISO
       "2004/03/21 12:45:33.123456",    # variant
       "20040321",                      # just dates work fine as well
       "Mar/21/2004",                   # US format, also support month abbreviation or full
       "rapunzel")                      # will produce a NA

p <- toPOSIXct(s)

options("digits.secs"=6)                # make sure we see microseconds in output
print(format(p, tz="UTC"))              # format UTC times as UTC (helps for Date types too)
[1] "2004-03-21 12:45:33.123456" "2004-03-21 12:45:33.123456"
[3] "2004-03-21 00:00:00.000000" "2004-03-21 00:00:00.000000"
[5] NA                          

We can also illustrate integer and numeric inputs:

## parsing integer types
s <- c(20150315L, 20010101L, 20141231L)
p <- toPOSIXct(s)
print(format(p, tz="UTC"))
[1] "2015-03-15" "2001-01-01" "2014-12-31"
## parsing numeric types
s <- c(20150315, 20010101, 20141231)
print(format(p, tz="UTC"))
[1] "2015-03-15" "2001-01-01" "2014-12-31"

Note that we always forced display using UTC rather local time, the R default.

March 22, 2015 12:00 AM

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

January 01, 2015

Journal of the Royal Statistical Society: Series B

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

November 20, 2013

R you ready?

Sending data from client to server and back using shiny

post-logoAfter some time of using shiny I got to the point where I needed to send some arbitrary data from the client to the server, process it with R and return some other data to the client. As a client/server programming newbie this was a challenge for me as I did not want to dive too deep into the world of web programming. I wanted to get the job done using shiny and preferably as little JS/PHP etc. scripting as possible.

It turns out that the task is quite simple as shiny comes with some currently undocumented functions under the hood that will make this task quite easy. You can find some more information on these functions here.

As mentioned above, I am a web programming newbie. So this post may be helpful for people with little web programming experience (just a few lines of JavaScript are needed) and who want to see a simple way of how to get the job done.

Sending data from client to server

Sending the data from the client to the server is accomplished by the JS function Shiny.onInputChange. This function takes a JS object and sends it to the shiny server. On the server side the object will be accessible as an R object under the name which is given as the second argument to the Shiny.onInputChange function. Let’s start by sending a random number to the server. The name of the object on the server side will be mydata.

Let’s create the shiny user interface file (ui.R). I will add a colored div, another element for verbatim text output called results and add the JavaScript code to send the data. The workhorse line is Shiny.onInputChange(“mydata”, number);. The JS code is included by passing it as a string to the tags$script function.

# ui.R

shinyUI( bootstrapPage(

  # a div named mydiv
  tags$div(id="mydiv", style="width: 50px; height :50px;
           left: 100px; top: 100px;
           background-color: gray; position: absolute"),

  # a shiny element to display unformatted text

  # javascript code to send data to shiny server
    document.getElementById("mydiv").onclick = function() {
      var number = Math.random();
      Shiny.onInputChange("mydata", number);


Now, on the server side, we can simply access the data that was sent by addressing it the usual way via the input object (i.e. input$mydata. The code below will make the verbatimTextOutput element results show the value that was initially passed to the server.

# server.R

shinyServer(function(input, output, session) {

    output$results = renderPrint({


You can copy the above files from here or run the code directly. When you run the code you will find that the random value in the upper box is updated if you click on the div.


What we have achieved so far is to pass some data to the server, access it and pass it back to a display on the client side. For the last part however, we have used a standard shiny element to send back the data to the client.

Sending data from server to client

Now let’s add a component to send custom data from the server back to the client. This task has two parts. On the client side we need to define a handler function. This is a function that will receive the data from the server and perform some task with it. In other words, the function will handle the received data. To register a handler the function Shiny.addCustomMessageHandler is used. I will name our handler function myCallbackHandler. Our handler function will use the received data and execute some JS code. In our case it will change the color of our div called mydiv according to the color value that is passed from the server to the handler. Let’s add the JS code below to the ui.R file.

# ui.R

# handler to receive data from server
        function(color) {
          document.getElementById("mydiv").style.backgroundColor = color;

Let’s move to the server side. I want the server to send the data to the handler function whenever the div is clicked, i.e. when the value of input$mydata changes. The sending of the data to the client is accomplished by an R function called sendCustomMessage which can be found in the session object. The function is passed the name of the client side handler function and the R object we want to pass to the function. Here, I create a random hex color value string that gets sent to a client handler function myCallbackHandler. The line sending the data to the client is contained in an observer. The observer includes the reactive object input$mydata, so the server will send someting to the client side handler function whenever the values of input$mydata changes. And it changes each time we click on the div. Let’s add the code below to the server.R file.

# server.R

# observes if value of mydata sent from the client changes.  if yes
# generate a new random color string and send it back to the client
# handler function called 'myCallbackHandler'
    color = rgb(runif(1), runif(1), runif(1))
    session$sendCustomMessage(type = "myCallbackHandler", color)

You can copy the above files from here or run the code directly. When you run the code you will see that the div changes color when you click on it.


That’s it. We have passed custom data from the client to the server and back. The following graphics sums up the functions that were used.


Passing more complex objects

The two functions also do a good job passing more complex JS or R objects. If you modify your code to send a JS object to shiny, it will be converted into an R list object on the server side. Let’s replace the JS object we send to the server (in ui.R) with following lines. On the server side, we will get a list.

document.getElementById("mydiv").onclick = function() {
  var obj = {one: [1,2,3,4],
             two: ["a", "b", "c"]};
  Shiny.onInputChange("mydata", obj);

Note that now however the shiny server will only execute the function once (on loading), not each time the click event is fired. The reason is, that now the input data is static, i.e. the JS object we send via onInputChange does not change. To reduce workload on the server side, the code in the observer will only be executed if the reactive value under observation (i.e. the value of input$mydata) changes. As this is not the case anymore as the value we pass is static, the observer that sends back the color information to the client to change the color of the div is not executed a second time.

The conversion also works nicely the other way round. We can pass an R list object to the sendCustomMessage function and on the client side it will appear as a JS object. So we are free to pass almost any type of data we need to.

Putting the JS code in a separate file

To keep things simple I included the JS code directly into the ui.R file using tags$script. This does not look very nice and you may want to put the JS code in a separate file instead. For this purpose I will create a JS file called mycode.js and include all the above JS code in it. Additionally, this file has another modification: All the code is wrapped into some JS/jQuery code ($(document).ready(function() { })that will make sure the JS code is run after the DOM (that is all the HTML elements) is loaded. Before, I simply placed the JS code below the HTML elements to make sure they are loaded, but I guess this is no good practice.

// mycode.js

$(document).ready(function() {

  document.getElementById("mydiv").onclick = function() {
    var number = Math.random();
    Shiny.onInputChange("mydata", number);

    function(color) {
      document.getElementById("mydiv").style.backgroundColor = color;


To include the JS file shiny offers the includeScript function to include JS files. The server.R file has not changed, the ui.R file now looks like this.

# server.R


shinyUI( bootstrapPage(

  # include the js code

  # a div named mydiv
           style="width: 50px; height :50px; left: 100px; top: 100px;
           background-color: gray; position: absolute"),

  # an element for unformatted text

You can copy the above files from here or run the gist directly from within R.


The above examples are purely artifical as it will not make much sense to let the server generate a random color value and send it back to the client. JS might just do all this on the client side without any need for client/server communiation at all. The examples are just for demonstration purposes to outline the mechanisms you may use for sending custom data to the server or client using the functions supplied by the marvellous shiny package. Winston Chang (one of the RStudio and shiny guys) has some more examples in his testapp repo. Have a look at the message-handler-inline and the message-handler-jsfile folders.


by markheckmann at November 20, 2013 06:26 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

March 13, 2013

Modern Toolmaking

New package for ensembling R models

I've written a new R package called caretEnsemble for creating ensembles of caret models in R.  It currently works well for regression models, and I've written some preliminary support for binary classification models.

At this point, I've got 2 different algorithms for combining models:

1. Greedy stepwise ensembles (returns a weight for each model)
2. Stacks of caret models

(You can also manually specify weights for a greedy ensemble)

The greedy algorithm is based on the work of Caruana et al., 2004, and inspired by the medley package here on github.  The stacking algorithm simply builds a second caret model on top of the existing models (using their predictions as input), and employs all of the flexibility of the caret package.

All the models in the ensemble must use the same training/test folds.  Both algorithms use the out-of-sample predictions to find the weights and train the stack. Here's a brief script demonstrating how to use the package:

Please feel free to submit any comments here or on github.  I'd also be happy to include any patches you feel like submitting.  In particular, I could use some help writing support for multi-class models, writing more tests, and fixing bugs.

by Zachary Deane-Mayer ( at March 13, 2013 02:36 PM

February 18, 2013

Romain Francois

Improving the graph gallery

I'm trying to make improvements to the R Graph Gallery, I'm looking for suggestions from users of the website.

I've started a question on the website's facebook page. Please take a few seconds to vote to existing improvements possibilities and perhaps offer some of your own ideas.


by romain francois at February 18, 2013 07:37 AM

February 04, 2013

Romain Francois

bibtex 0.3-5


The version 0.3-5 of the bibtex package is on CRAN. This fixes a corner case issue about empty bib files thanks to Kurt Hornik.

by romain francois at February 04, 2013 10:59 AM