# Planet R

## May 23, 2015

### Alstatr

#### Parametric Inference: Likelihood Ratio Test Problem 2

More on Likelihood Ratio Test, the following problem is originally from Casella and Berger (2001), exercise 8.12.

### Problem

For samples of size $n=1,4,16,64,100$ from a normal population with mean $\mu$ and known variance $\sigma^2$, plot the power function of the following LRTs (Likelihood Ratio Tests). Take $\alpha = .05$.
1. $H_0:\mu\leq 0$ versus $H_1:\mu>0$
2. $H_0:\mu=0$ versus $H_1:\mu\neq 0$

### Solution

1. The LRT statistic is given by $$\lambda(\mathbf{x})=\frac{\displaystyle\sup_{\mu\leq 0}\mathcal{L}(\mu|\mathbf{x})}{\displaystyle\sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})}, \;\text{since }\sigma^2\text{ is known}.$$ The denominator can be expanded as follows: \begin{aligned} \sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})&=\sup_{-\infty<\mu<\infty}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\sup_{-\infty<\mu<\infty}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu)^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 }\mu.\\ &=\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} while the numerator is evaluated as follows: \begin{aligned} \sup_{\mu\leq 0}\mathcal{L}(\mu|\mathbf{x})&=\sup_{\mu\leq 0}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\sup_{\mu\leq 0}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu)^2}{2\sigma^2}\right]. \end{aligned} Above expression will attain its maximum if the value inside the exponential function is small. And for negative values of $\mu\in(-\infty,0)$ the quantity $(x_i-\mu)^2$ would be large, implies that the exponential term would become small. Therefore, the only value that will give us the supremum likelihood is $\mu=\mu_0=0$. Hence, \begin{aligned} \sup_{\mu\leq 0}\mathcal{L}(\mu|\mathbf{x})&=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu_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}-\mu_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}-\mu_0)+(\bar{x}-\mu_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}-\mu_0)^2}{2\sigma^2}\right], \\ &\text{since the middle term is 0.}\\ =\frac{1}{(2\pi\sigma^2)^{1/n}}&\exp\left[-\frac{(n-1)s^2+n\bar{x}^2}{2\sigma^2}\right], \text{since }\mu_0=0.\\ \end{aligned} So that \label{eq:lrtre} \begin{aligned} \lambda(\mathbf{x})&=\frac{\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2+n\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]}\\ &=\exp\left[-\frac{n\bar{x}^2}{2\sigma^2}\right].\\ \end{aligned} And we reject the null hypothesis if $\lambda(\mathbf{x})\leq c$, that is \begin{aligned} \exp\left[-\frac{n\bar{x}^2}{2\sigma^2}\right]&\leq c\\ -\frac{n\bar{x}^2}{2\sigma^2}&\leq \log c\\ \frac{\lvert\bar{x}\rvert}{\sigma/\sqrt{n}}&\geq\sqrt{-2\log c}=c'. \end{aligned}
 Figure 1: Plot of Likelihood Ratio Test Statistic for $n = 4,\sigma = 1$.

Hence, rejecting the null hypothesis if $\lambda(\mathbf{x})\leq c$, is equivalent to rejecting $H_0$ if $\frac{\bar{x}}{\sigma/\sqrt{n}}\geq c'\in[0,\infty)$. Figure 1 depicts the plot of the LRT, the shaded region is on the positive side because that's where the alternative region is, $H_1:\mu>0$, in a sense that if the LRT is small enough to reject $H_0$, then it simply tells us that the plausibility of the parameter in the alternative in explaining the sample is higher compared to the null hypothesis. And if that's the case, we expect the sample to come from the model proposed by $H_1$, so that the sample mean $\bar{x}$, being an unbiased estimator of the population mean $\mu$, a function of the LRT statistic, should fall on the side (shaded region) of the alternative.

So that the power function, that is the probability of rejecting the null hypothesis given that it is true (the probability of Type I error) is, \begin{aligned} \beta(\mu)&=\mathrm{P}\left[\frac{\bar{x}-\mu_0}{\sigma/\sqrt{n}}\geq c'\right],\quad\mu_0=0\\ &=1-\mathrm{P}\left[\frac{\bar{x}+\mu-\mu-\mu_0}{\sigma/\sqrt{n}}< c'\right]\\ &=1-\mathrm{P}\left[\frac{\bar{x}-\mu}{\sigma/\sqrt{n}} + \frac{\mu-\mu_0}{\sigma/\sqrt{n}}< c'\right]\\ &=1-\mathrm{P}\left[\frac{\bar{x}-\mu}{\sigma/\sqrt{n}}< c'+ \frac{\mu_0-\mu}{\sigma/\sqrt{n}}\right]\\ &=1-\Phi\left[c'+ \frac{\mu_0-\mu}{\sigma/\sqrt{n}}\right]. \end{aligned} Values taken by $\Phi$ are negative and so it decreases, but since we subtracted it to 1, then $\beta(\mu)$ is an increasing function. So that for $\alpha=.05$, \begin{aligned} \alpha&=\sup_{\mu\leq \mu_0}\beta(\mu)\\ .05&=\beta(\mu_0)\Rightarrow\beta(\mu_0)=1-\Phi(c')\\ .95&=\Phi(c')\Rightarrow c'=1.645. \end{aligned} Since, \begin{aligned} \Phi(1.645)=\int_{-\infty}^{1.645}\frac{1}{\sqrt{2\pi}}\exp\left[-\frac{x^2}{2}\right]\operatorname{d}x=.9500151. \end{aligned} Therefore for $c'=1.645,\mu_0=0,\sigma=1$, the plot of the power function as a function of $\mu$ for different sample size, $n$, is shown in Figure 2. For example, for $n=1$ we compute for the function \label{eq:powcomp} \begin{aligned} \beta(\mu)&=1-\Phi\left[c'+ \frac{\mu_0-\mu}{\sigma/\sqrt{n}}\right]\\ &=1-\Phi\left[1.645+ \frac{0-\mu}{1/\sqrt{1}}\right]\\ &=1-\int_{-\infty}^{\left(1.645+ \frac{0-\mu}{1/\sqrt{1}}\right)}\frac{1}{\sqrt{2\pi}}\exp\left[-\frac{x^2}{2}\right]\operatorname{d}x. \end{aligned} The obtained values would be the $y$. For $n = 64$ \begin{aligned} \beta(\mu)&=1-\Phi\left[c'+ \frac{\mu_0-\mu}{\sigma/\sqrt{n}}\right]\\ &=1-\Phi\left[1.645+ \frac{0-\mu}{1/\sqrt{64}}\right]\\ &=1-\int_{-\infty}^{\left(1.645+ \frac{0-\mu}{1/\sqrt{64}}\right)}\frac{1}{\sqrt{2\pi}}\exp\left[-\frac{x^2}{2}\right]\operatorname{d}x, \end{aligned} and so on.
 Figure 2: Power Function for Different Values of $n$.

2. The LRT statistic is given by $$\lambda(\mathbf{x})=\frac{\displaystyle\sup_{\mu= 0}\mathcal{L}(\mu|\mathbf{x})}{\displaystyle\sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})}, \;\text{since }\sigma^2\text{ is known}.$$ The denominator can be expanded as follows: \begin{aligned} \sup_{-\infty<\mu<\infty}\mathcal{L}(\mu|\mathbf{x})&=\sup_{-\infty<\mu<\infty}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\sup_{-\infty<\mu<\infty}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu)^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 }\mu.\\ &=\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} and the numerator is evaluated as follows: \begin{aligned} \sup_{\mu=0}\mathcal{L}(\mu|\mathbf{x})&=\sup_{\mu=0}\prod_{i=1}^{n}\frac{1}{\sqrt{2\pi}\sigma}\exp\left[-\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\sup_{\mu=0}\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-\mu)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\displaystyle\sum_{i=1}^{n}\frac{(x_i-0)^2}{2\sigma^2}\right]\\ &=\frac{1}{(2\pi\sigma^2)^{1/n}}\exp\left[-\frac{(n-1)s^2+n\bar{x}^2}{2\sigma^2}\right], \end{aligned} we skip some lines in the above simplification since we've done this already in part (a). And by Equation (1), $\lambda(\mathbf{x})=\exp\left[-\frac{n\bar{x}^2}{2\sigma^2}\right]$. So that $\lambda(\mathbf{x})\leq c$ would be \begin{aligned} \exp\left[-\frac{n\bar{x}^2}{2\sigma^2}\right]&\leq c\\ -\frac{n\bar{x}^2}{2\sigma^2}&\leq \log c\\ \frac{\lvert\bar{x}-\mu_0\rvert}{\sigma/\sqrt{n}}&\geq\sqrt{-2\log c}=c',\quad \mu_0=0. \end{aligned} So rejecting the null hypothesis if $\lambda(\mathbf{x})\leq c'$ is equivalent to rejecting $H_0$ if $\frac{\lvert\bar{x}\rvert}{\sigma/\sqrt{n}}\geq c'$. And since $H_1$ is two-sided, then we reject $H_0$ if $\frac{\bar{x}}{\sigma/\sqrt{n}}\geq c'$ or $\frac{\bar{x}}{\sigma/\sqrt{n}}\leq -c'$. To illustrate this, consider Figure 3 where the two shaded regions are the lower and upper rejection regions.
 Figure 3: Plot of Likelihood Ratio Test Statistic for $n = 4,\sigma = 1$.

So that the power function is, \begin{aligned} \beta(\mu)&=\mathrm{P}\left[\frac{\lvert\bar{x}\rvert}{\sigma/\sqrt{n}}\geq c'\right]\\ &=1 - \mathrm{P}\left[\frac{\lvert\bar{x}\rvert}{\sigma/\sqrt{n}}< c'\right]\\ &=1 - \mathrm{P}\left[-c'<\frac{\bar{x}}{\sigma/\sqrt{n}}< c'\right]\\ &=1 - \left\{\mathrm{P}\left[\frac{\bar{x}}{\sigma/\sqrt{n}}< c'\right]-\mathrm{P}\left[\frac{\bar{x}}{\sigma/\sqrt{n}}< -c'\right]\right\}\\ &=1 - \left\{\mathrm{P}\left[\frac{\bar{x}+\mu-\mu}{\sigma/\sqrt{n}}< c'\right]-\mathrm{P}\left[\frac{\bar{x}+\mu-\mu}{\sigma/\sqrt{n}}< -c'\right]\right\}\\ &=1 - \mathrm{P}\left[\frac{\bar{x}-\mu}{\sigma/\sqrt{n}}< c'-\frac{\mu}{\sigma/\sqrt{n}}\right]+\mathrm{P}\left[\frac{\bar{x}-\mu}{\sigma/\sqrt{n}}< -c'-\frac{\mu}{\sigma/\sqrt{n}}\right]\\ &=\underbrace{1 - \Phi\left[c'-\frac{\mu}{\sigma/\sqrt{n}}\right]}_{\Phi_1}+\underbrace{\Phi\left[-c'-\frac{\mu}{\sigma/\sqrt{n}}\right]}_{\Phi_2}. \end{aligned} Notice that $\Phi_1$ is an increasing function, while $\Phi_2$ is decreasing as a function of $\mu$. We expect this since the alternative hypothesis is a two-sided one, so does the power. To see this, consider Figure 4 for different values of $n$.
 Figure 4: Two-Sided Power Function for Different $n$.

The points in the plot are computed by substituting values of $\mu=0,\sigma=1$ and $n$ to the power function just like we did in Equation (2).

### CRANberries

#### New package restimizeapi with initial version 1.0.0

Package: restimizeapi
Type: Package
Title: Functions for Working with the 'www.estimize.com' Web Services
Version: 1.0.0
Date: 2015-05-23
Author: Thomas P. Fuller <thomas.fuller@coherentlogic.com>
Maintainer: Thomas P. Fuller <thomas.fuller@coherentlogic.com>
Description: Provides the user with functions to develop their trading strategy, uncover actionable trading ideas, and monitor consensus shifts with crowdsourced earnings and economic estimate data directly from <www.estimize.com>. Further information regarding the web services this package invokes can be found at <www.estimize.com/api>.
Depends: R (>= 3.1.3), RCurl, RJSONIO
Suggests: testthat
SystemRequirements:
URL: http://www.r-project.org, http://coherentlogic.com/middleware-development/r-package-for-the-estimize-com-api?source=cran, https://www.estimize.com?source=cran
Repository: CRAN
Archs: i386, x64
Packaged: 2015-05-22 18:57:15 UTC; thospfuller
NeedsCompilation: no
Date/Publication: 2015-05-23 08:55:19

#### New package mitml with initial version 0.2-2

Package: mitml
Type: Package
Title: Tools for Multiple Imputation in Multilevel Modeling
Version: 0.2-2
Date: 2015-05-23
Author: Simon Grund [aut,cre], Alexander Robitzsch [aut], Oliver Luedtke [aut]
Maintainer: Simon Grund <grund@ipn.uni-kiel.de>
Imports: pan, haven
Suggests: lme4, nlme, mice
LazyData: true
Description: Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the 'pan' package, and several functions for visualization, data management and the analysis of multiply imputed data sets.
Packaged: 2015-05-22 22:08:44 UTC; simon
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-05-23 01:34:51

## May 22, 2015

### CRANberries

#### New package Gotoh with initial version 0.9

Package: Gotoh
Type: Package
Title: C++ Wrapper for R which Does Alignments using Gotoh Algorithm
Version: 0.9
Date: 2015-05-18
Author: Rafael Nozal Canyadas <rca025@uib.no> , Eivind Valen <eivind.valen@gmail.com>
Description: Implement a Gotoh approach to sequence alignment. The Gotoh algorithm was first published by Osamu Gotoh in 1982; for more information please refer to: <doi:10.1016/0022-2836(82)90398-9> We are using 'Rcpp', which decrease greatly the CPU time with respect other alignment implementations. This was done in an effort to speed up the calculations for the project AmpliCan. The improvement we got compared with 'DECIPHER' is in between 300x and 500x many fold for sequences between 100 to 200 nucleotides, and between 100x and 200x for sequences of between 200 to 300 nucleotides. And up to 1.5x for sequences of size 10.000 nucleotides and beyond. This package contains the minimum files require for it to work. Please download the repository at: https://rafanozal@bitbucket.org/rafanozal/amplican.git to get the a more verbose documentation and script benchmarks.
Depends: R (>= 3.1.1)
Imports: Rcpp (>= 0.11.6)
Packaged: 2015-05-22 19:21:41 UTC; rafaelc
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-05-22 22:39:20

### Bioconductor Project Working Papers

#### Predicting Future Years of Life, Health, and Functional Ability: A Healthy Life Calculator for Older Adults

Abstract

Introduction

Planning for the future would be easier if we knew how long we will live and, more importantly, how many years we will be healthy and able to enjoy it. There are few well-documented aids for predicting our future health. We attempted to meet this need for persons 65 years of age and older.

Methods

Data came from the Cardiovascular Health Study, a large longitudinal study of older adults that began in 1990. Years of life (YOL) were defined by measuring time to death. Years of healthy life (YHL) were defined by an annual question about self-rated health, and years of able life (YABL) by questions about activities of daily living. Years of healthy and able life (YHABL) were the number of years the person was both Healthy and Able. We created prediction equations for YOL, YHL, YABL, and YHABL based on the demographic and health characteristics that best predicted outcomes. Internal and external validity were assessed. The resulting CHS Healthy Life Calculator (CHSHLC) was created and underwent three waves of beta testing.

Findings

A regression equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. As an example, a very healthy 70-year-old woman might expect an additional 20 YOL, 16.8 YHL, 16.5 YABL, and 14.2 YHABL. The CHSHLC also provides the percent in the sample who differed by more than 5 years from the estimate, to remind the user of variability.

Discussion

The CHSHLC is currently the only available calculator for YHL, YABL, and YHABL. It may have limitations if today’s users have better prospects for health than persons in 1990. But the external validity results were encouraging. The remaining variability is substantial, but this is one of the few calculators that describes the possible accuracy of the estimates.

Conclusion

The CHSHLC, currently at http://diehr.com/paula/healthspan, meets the need for a straightforward and well-documented estimate of future years of healthy and able life that older adults can use in planning for the future.

### CRANberries

#### New package dpmr with initial version 0.1.7-1

Package: dpmr
Title: Data Package Manager for R
Description: Create, install, and summarise data packages that follow the Open Knowledge Foundation's Data Package Protocol.
Version: 0.1.7-1
Date: 2015-05-22
Authors@R: c( person("Christopher", "Gandrud", email = "christopher.gandrud@gmail.com", role = c("aut", "cre")) )
URL: http://christophergandrud.github.io/dpmr/
BugReports: https://github.com/christophergandrud/dpmr/issues
Imports: digest, httr, jsonlite, magrittr, rio
Suggests: devtools, testthat
NeedsCompilation: no
Packaged: 2015-05-22 10:22:53 UTC; christophergandrud
Author: Christopher Gandrud [aut, cre]
Maintainer: Christopher Gandrud <christopher.gandrud@gmail.com>
Repository: CRAN
Date/Publication: 2015-05-22 13:22:08

### Alstatr

#### Parametric Inference: Likelihood Ratio Test Problem 1

Another post for mathematical statistics, the problem below is originally from Casella and Berger (2001) (see Reference 1), exercise 8.6.

### Problem

1. Suppose that we have two independent random samples $X_1,\cdots, X_n$ are exponential($\theta$), and $Y_1,\cdots, Y_m$ are exponential($\mu$).
1. Find the LRT (Likelihood Ratio Test) of $H_0:\theta=\mu$ versus $H_1:\theta\neq\mu$.
2. Show that the test in part (a) can be based on the statistic
3. $$T=\frac{\sum X_i}{\sum X_i+\sum Y_i}.$$
4. Find the distribution of $T$ when $H_0$ is true.

### Solution

1. The Likelihood Ratio Test is given by $$\lambda(\mathbf{x},\mathbf{y}) = \frac{\displaystyle\sup_{\theta = \mu,\mu>0}\mathrm{P}(\mathbf{x},\mathbf{y}|\theta,\mu)}{\displaystyle\sup_{\theta > 0,\mu>0}\mathrm{P}(\mathbf{x}, \mathbf{y}|\theta,\mu)},$$ where the denominator is evaluated as follows: $$\sup_{\theta > 0,\mu>0}\mathrm{P}(\mathbf{x}, \mathbf{y}|\theta,\mu)= \sup_{\theta > 0}\mathrm{P}(\mathbf{x}|\theta)\sup_{\mu > 0}\mathrm{P}(\mathbf{y}|\mu),\quad\text{by independence.}$$ So that, \begin{aligned} \sup_{\theta > 0}\mathrm{P}(\mathbf{x}|\theta)&=\sup_{\theta>0}\prod_{i=1}^{n}\frac{1}{\theta}\exp\left[-\frac{x_i}{\theta}\right]=\sup_{\theta>0}\frac{1}{\theta^n}\exp\left[-\frac{\sum_{i=1}^{n}x_i}{\theta}\right]\\ &=\frac{1}{\bar{x}^n}\exp\left[-\frac{\sum_{i=1}^{n}x_i}{\bar{x}}\right]=\frac{1}{\bar{x}^n}\exp[-n], \end{aligned} since $\bar{x}$, or the sample mean is the MLE of $\theta$. Also, \begin{aligned} \sup_{\mu > 0}\mathrm{P}(\mathbf{y}|\mu)&=\sup_{\mu>0}\prod_{j=1}^{m}\frac{1}{\mu}\exp\left[-\frac{y_j}{\mu}\right]=\sup_{\mu>0}\frac{1}{\mu^m}\exp\left[-\frac{\sum_{j=1}^{m}y_j}{\mu}\right]\\ &=\frac{1}{\bar{y}^m}\exp\left[-\frac{\sum_{j=1}^{m}y_j}{\bar{y}}\right]=\frac{1}{\bar{y}^m}\exp[-m]. \end{aligned} Now the numerator is evaluated as follows, \begin{aligned} \sup_{\theta = \mu,\mu>0}\mathrm{P}(\mathbf{x},\mathbf{y}|\theta,\mu)&=\sup_{\theta=\mu,\mu>0}\mathrm{P}(\mathbf{x}|\theta)\mathrm{P}(\mathbf{y}|\mu),\quad\text{by independence.}\\ &=\sup_{\theta=\mu,\mu>0}\prod_{i=1}^{n}\frac{1}{\theta}\exp\left[-\frac{x_i}{\theta}\right]\prod_{j=1}^{m}\frac{1}{\mu}\exp\left[-\frac{y_j}{\mu}\right]\\ &=\sup_{\theta=\mu,\mu>0}\frac{1}{\theta^n}\exp\left[-\frac{\sum_{i=1}^nx_i}{\theta}\right]\frac{1}{\mu^m}\exp\left[-\frac{\sum_{j=1}^m y_j}{\mu}\right]\\ &=\sup_{\mu>0}\frac{1}{\mu^n}\exp\left[-\frac{\sum_{i=1}^nx_i}{\mu}\right]\frac{1}{\mu^m}\exp\left[-\frac{\sum_{j=1}^m y_j}{\mu}\right]\\ &=\sup_{\mu>0}\frac{1}{\mu^{n+m}}\exp\left\{-\frac{1}{\mu}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]\right\} \end{aligned} Note that $\mu$ is a nuisance parameter, and so we will also maximize this over its domain. And to do that we take the log-likeihood function first, \begin{aligned} \ell(\mu|\mathbf{x},\mathbf{y})&=-\log(\mu^{n+m})-\frac{1}{\mu}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]\\ &=-(n+m)\log(\mu)-\frac{1}{\mu}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]. \end{aligned} Taking the derivative with respect to $\mu$, gives us $$\frac{\operatorname{d}}{\operatorname{d}\mu}\ell(\mu|\mathbf{x},\mathbf{y})=-(n+m)\frac{1}{\mu}+\frac{1}{\mu^2}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right],$$ equate this to zero to obtain the stationary point, \begin{aligned} -(n+m)\frac{1}{\mu}+\frac{1}{\mu^2}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]&=0\\ -(n+m)\mu+\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]&=0\\ \mu&=\frac{1}{n+m}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]. \end{aligned} To verify if this is the MLE, we take the second derivative test for the log-likelihood function, $$\frac{\operatorname{d}^2}{\operatorname{d}\mu^2}\ell(\mu|\mathbf{x},\mathbf{y})=(n+m)\frac{1}{\mu^2}-\frac{2}{\mu^3}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]<0,$$ since $\frac{1}{\mu^2}<\frac{2}{\mu^3}$, implying $\hat{\mu}=\displaystyle\frac{1}{n+m}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]$ is the MLE of $\mu$. Thus the LRT, $\lambda(\mathbf{x},\mathbf{y})$ would be, \begin{aligned} \lambda(\mathbf{x},\mathbf{y})&=\frac{\sup_{\mu>0}\displaystyle\frac{1}{\mu^{n+m}}\exp\left\{-\frac{1}{\mu}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]\right\}}{\displaystyle\frac{1}{\bar{x}^n}\frac{1}{\bar{y}^m}\exp[-(n+m)]}\\ &=\left(\frac{1}{\frac{1}{{(n+m)}^{n+m}}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]^{n+m}}\times\right.\\ &\qquad\left.\exp\left\{-\frac{1}{\frac{1}{n+m}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]\right\}\right)\bigg/\\ &\qquad\qquad\qquad\displaystyle\frac{1}{\bar{x}^n}\frac{1}{\bar{y}^m}\exp[-(n+m)] \end{aligned}  \begin{aligned} &=\frac{\displaystyle\frac{1}{\displaystyle\frac{1}{{(n+m)}^{n+m}}\left[\displaystyle\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]^{n+m}}\times\exp[-(n+m)]}{\displaystyle\frac{1}{\bar{x}^n}\frac{1}{\bar{y}^m}\exp[-(n+m)]}\.3cm] &=\frac{\displaystyle \bar{x}^n \bar{y}^m}{\displaystyle\frac{1}{{(n+m)}^{n+m}}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]^{n+m}}. \end{aligned}  And we say that H_0 is rejected if \lambda(\mathbf{x},\mathbf{y})\leq c. 2. If we do some algebra on the LRT in part (a), we obtain the following:  \begin{aligned} \lambda(\mathbf{x},\mathbf{y})&=\frac{\displaystyle \bar{x}^n \bar{y}^m}{\displaystyle\frac{1}{{(n+m)}^{n+m}}\left[\sum_{i=1}^nx_i+\sum_{j=1}^m y_j\right]^{n+m}}\\ &=\frac{\displaystyle\frac{1}{n^n}\left(\sum_{i=1}^{n}x_i\right)^{n}\frac{1}{m^{m}}\left(\sum_{j=1}^{m}y_j\right)^{m}}{\displaystyle\frac{1}{(n+m)^{n+m}}\left[\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j\right]^{n+m}}\\ &=\frac{\displaystyle (n+m)^{n+m}\left(\sum_{i=1}^{n}x_i\right)^{n}\left(\sum_{j=1}^{m}y_j\right)^{m}}{\displaystyle n^{n}m^{m}\left[\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j\right]^{n+m}}\\ &=\frac{(n+m)^{n+m}}{n^nm^{m}}\left[\frac{\displaystyle \sum_{j=1}^{m}y_j}{\displaystyle\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j}\right]^{m}\left[\frac{\displaystyle \sum_{i=1}^{n}x_i}{\displaystyle\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j}\right]^{n}\\ &=\frac{(n+m)^{n+m}}{n^nm^{m}}\left[1-\frac{\displaystyle \sum_{i=1}^{n}x_i}{\displaystyle\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j}\right]^{m}\left[\frac{\displaystyle \sum_{i=1}^{n}x_i}{\displaystyle\sum_{i=1}^{n}x_i+\sum_{j=1}^{m}y_j}\right]^{n}\\ &=\frac{(n+m)^{n+m}}{n^nm^{m}}\left[1-T\right]^{m}\left[T\right]^{n}. \end{aligned}  Hence, the LRT can be based on the statistic T. 3. The distribution of \sum X_i is obtain using the MGF (Moment Generating Function) technique, that is  \begin{aligned} \mathrm{M}_{\Sigma X_i}(t)&=\mathrm{E}\exp[t\Sigma X_i]=\mathrm{E}\exp[tX_1 +\cdots + tX_n]\\ &=\mathrm{E}\exp[tX_1]\times\cdots\times\mathrm{E}\exp[tX_n],\quad\text{by independence.}\\ &=\frac{1}{1-\theta t}\times\cdots\times\frac{1}{1-\theta t}\\ &=\left(\frac{1}{1-\theta t}\right)^{n}=\text{MGF of gamma}(n,\theta). \end{aligned}  Now, when H_0 is true then \sum X_i is gamma(m,\theta). For brevity, let X=\sum_{i=1}^{n} X_i and Y=\sum_{j=1}^{m}Y_j. The joint distribution of X and Y is given below,  f_{XY}(x, y)=\frac{1}{\Gamma (n)\theta^{n}}x^{n-1}\exp[-x/\theta]\times\frac{1}{\Gamma (m)\theta^{m}}y^{m-1}\exp[-y/\theta].  Let U=\frac{X}{X+Y} and V=X+Y, then the support of (X,Y) is \mathcal{A}=\left\{(x,y)\in \mathbb{R}^{+}\times \mathbb{R}^{+}\right\}. Since the transformations U and V is one-to-one and onto, then \mathcal{B}=\left\{(u,v)\in [0,1]\times \mathbb{R}^{+}\right\}. Consider the following transformations  u=g_{1}(x,y)=\frac{x}{x+y}\quad\text{and}\quad v=g_{2}(x,y)=x+y.  Then, $$\label{eq:bvt1} u=\frac{x}{x+y}\Rightarrow x=\frac{uy}{1-u}$$ and $$\label{eq:bvt2} v=x+y\Rightarrow y = v-x.$$ Substitute Equation (\ref{eq:bvt2}) to Equation (\ref{eq:bvt1}), then  \begin{aligned} x=\frac{u(v-x)}{1-u}&\Rightarrow x(1-u)=u(v-x)\\ x-ux=uv-ux&\Rightarrow x=uv=h_{1}(u,v). \end{aligned}  Substitute x above to Equation (\ref{eq:bvt2}) to obtain, y=v(1-u)=h_2(u,v). And the Jacobian matrix is,  \mathbf{J}=\bigg| \begin{array}{cc} v&u\\[.2cm] -v&1-u \end{array} \bigg|=v(1-u)+uv=v.  So that,  \begin{aligned} f_{UV}(u,v)&=f_{XY}(h_1(u,v),h_2(u,v))\lvert \mathbf{J}\rvert=f_{XY}(uv,v(1-u))\lvert v\rvert\\ &=\frac{1}{\Gamma (n)\theta^{n}}(uv)^{n-1}\exp[-uv/\theta]\times\\ &\quad\;\frac{1}{\Gamma (m)\theta^{m}}(v(1-u))^{m-1}\exp[-v(1-u)/\theta]v\\ &=\frac{1}{\Gamma (n)\theta^{n}}(uv)^{n-1}\exp[-uv/\theta]\times\\ &\quad\;\frac{1}{\Gamma (m)\theta^{m}}(v(1-u))^{m-1}\exp[-v/\theta]\exp[uv/\theta]v\\ &=\frac{1}{\Gamma (n)\theta^{n}}u^{n-1}v^{n-1}\times\frac{1}{\Gamma (m)\theta^{m}}v^{m-1}(1-u)^{m-1}\exp[-v/\theta]v\\ &=\frac{1}{\Gamma (n)}\underbrace{u^{n-1}(1-u)^{m-1}}_{\text{Beta}(n,m)\text{ kernel}}\frac{1}{\Gamma (m)\theta^{m+n}}v^{m-1}v^{n-1}\exp[-v/\theta]v\\ &=\frac{\Gamma(m)\Gamma(m+n)}{\Gamma(m)\Gamma(m+n)}\frac{u^{n-1}(1-u)^{m-1}}{\Gamma (n)}\times\\ &\quad\;\frac{1}{\Gamma (m)\theta^{m+n}}v^{m-1}v^{n}\exp[-v/\theta]\\ &=\underbrace{\frac{\Gamma(m+n)}{\Gamma (n)\Gamma(m)}u^{n-1}(1-u)^{m-1}}_{\text{Beta}(n,m)}\times\\ &\quad\;\underbrace{\frac{1}{\Gamma(m+n)\theta^{m+n}}v^{m+n-1}\exp[-v/\theta]}_{\text{Gamma}(m+n,\theta)}. \end{aligned}  So that the marginal density of U=\displaystyle\frac{\sum X_i}{\sum X_i +\sum Y_i} is Beta(n,m). ### Reference 1. Casella, G. and Berger, R.L. (2001). Statistical Inference. Thomson Learning, Inc. ## May 21, 2015 ### Dirk Eddelbuettel #### BH release 1.58.0-1 A new released of BH is now on CRAN. BH provides a large part of the Boost C++ libraries as a set of template headers for use by R and Rcpp. This release both upgrades the version of Boost to the current release, and adds a new library: Boost MultiPrecision . A brief summary of changes from the NEWS file is below. #### Changes in version 1.58.0-1 (2015-05-21) • Upgraded to Boost 1.58 installed directly from upstream source • Added Boost MultiPrecision as requested in GH ticket #12 based on rcpp-devel request by Jordi Molins Coronado Courtesy of CRANberries, there is also a diffstat report for the most recent release. Comments and suggestions are welcome via the mailing list or the issue tracker at the GitHubGitHub 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. #### RInside 0.2.13 A new release 0.2.13 of RInside is now on CRAN. RInside provides a set of convenience classes which facilitate embedding of R inside of C++ applications and programs, using the classes and functions provided by Rcpp. This release works around a bug in R 3.2.0, and addressed in R 3.2.0-patched. The NEWS extract below has more details. #### Changes in RInside version 0.2.13 (2015-05-20) • Added workaround for a bug in R 3.2.0: by including the file RInterface.h only once we do not getting linker errors due to multiple definitions of R_running_as_main_program (which is now addressed in R-patched as well). • Small improvements to the Travis CI script. CRANberries also provides a short report with changes from the previous release. More information is on the RInside page. Questions, comments etc should go to the rcpp-devel mailing list off the Rcpp 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. ## May 20, 2015 ### Bioconductor Project Working Papers #### Some models and methods for the analysis of observational data This article provides a short, concise and essentially self-contained exposition of some of the most important models and methods for the analysis of observational data, and a substantial number of illustrations of their application. Although for the most part our presentation follows P. Rosenbaum’s book, “Observational Studies”, and naturally draws on related literature, it contains original elements and simplifies and generalizes some basic results. The illustrations, based on simulated data, show the methods at work in some detail, highlighting pitfalls and emphasizing certain subjective aspects of the statistical analyses. ## May 18, 2015 ### Dirk Eddelbuettel #### random 0.2.4 A new release of our random package for truly (hardware-based) random numbers as provided by random.org is now on CRAN. The R 3.2.0 release brought the change to use an internal method="libcurl" which we are using if available; else the curl::curl() method added in release 0.2.3 is used. We are also a little more explicit about closing connections, and added really basic regression tests -- as it is hard to test hardware-based RNGs draws. Courtesy of CRANberries comes a diffstat report for this release. Current and previous releases are available here as well as on CRAN. This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings. ## May 15, 2015 ### Alstatr #### 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, TruthDecision Table 1: Two Types of Errors in Hypothesis Testing. Accept H_0Reject H_0 H_0Correct DecisionType I Error H_1Type 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, 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, 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, And for different values of n, consider the following power functions 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. ### Reference ## May 14, 2015 ### Bioconductor Project Working Papers #### Adaptive Pre-specification in Randomized Trials With and Without Pair-Matching In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is unclear a priori which baseline covariates (if any) should be included in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate targeted maximum likelihood estimator (TMLE) that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. Our procedure is tailored to the scientific question (sample vs. population treatment effect) and study design (pair-matched or not) and alleviates many of the common concerns. ## May 12, 2015 ### Bioconductor Project Working Papers #### Double Robust Estimation of Encouragement-design Intervention Effects Transported Across Sites We develop double robust targeted maximum likelihood estimators (TMLE) for transporting intervention effects from one population to another. Specifically, we develop TMLE estimators for three transported estimands: intent-to-treat average treatment effect (ATE) and complier ATE, which are relevant for encouragement-design interventions and instrumental variable analyses, and the ATE of the exposure on the outcome, which is applicable to any randomized or observational study. We demonstrate finite sample performance of these TMLE estimators using simulation, including in the presence of practical violations of the positivity assumption. We then apply these methods to the Moving to Opportunity trial, a multi-site, encouragement-design intervention in which families in public housing were randomized to receive housing vouchers and logistical support to move to low-poverty neighborhoods. This application sheds light on whether effect differences across sites can be explained by differences in population composition. ### 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. ## May 05, 2015 ### Dirk Eddelbuettel #### RcppAnnoy 0.0.6 A few days ago, Erik released a new version of his Annoy library -- a small, fast, and lightweight C++ template header library for approximate nearest neighbours -- which now no longer requires Boost. While I don't mind Boost (actually, quite the opposite), it appears to have been a blocker in getting the Python part of Annoy over to the world of python3. And with a new Annoy out, I updated RcppAnnoy to it. In the process I got another pull request into Annoy (use R's RNG rather than rand() which R CMD check really dislikes). This new package is now on CRAN. Courtesy of CRANberries, there is also a diffstat report for this release. More detailed information is on the RcppAnnoy page 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. ## April 27, 2015 ### Alstatr #### 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 $$\label{eq:lrt} \lambda(\mathbf{x})=\frac{\displaystyle\sup_{\theta\in\Theta_0}L(\theta|\mathbf{x})}{\displaystyle\sup_{\theta\in\Theta}L(\theta|\mathbf{x})}.$$ 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. Solution: 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, 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, 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: ### Reference ## April 25, 2015 ### RCpp Gallery #### Stochastic SIR Epidemiological Compartment Model ### Introduction 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 }) }) set.seed(paramsseed) ## run the model once result.df <- tauleapCpp(params) library(plyr) 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) { set.seed(.nn) ## run the model result <- tauleapCpp(params) ## this wastes space, but is very simple and aids plotting resultnsim <- .nn return(result) }) ### 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! library(lattice) ## lattice plot of results plot( xyplot(I ~ time | sprintf("Simulation %02d",nsim), data=result.rep, type=c('l','g'), as.table=T, ylab='Infected', xlab='Year', scales=list(y=list(alternating=F)) ) ) ## April 14, 2015 ### R you ready? #### Beautiful plots while simulating loss in two-part procrustes problem Today 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(xs, xrss, 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. Cheers! ## April 08, 2015 ### Journal of Statistical Software #### SAVE: An R Package for the Statistical Analysis of Computer Models Vol. 64, Issue 13, Apr 2015 Abstract: This paper introduces the R package SAVE which implements statistical methodology for the analysis of computer models. Namely, the package includes routines that perform emulation, calibration and validation of this type of models. The methodology is Bayesian and is essentially that of Bayarri, Berger, Paulo, Sacks, Cafeo, Cavendish, Lin, and Tu (2007). The package is available through the Comprehensive R Archive Network. We illustrate its use with a real data example and in the context of a simulated example. #### GPfit: An R Package for Fitting a Gaussian Process Model to Deterministic Simulator Outputs Vol. 64, Issue 12, Apr 2015 Abstract: Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the input space are close together. Ranjan, Haynes, and Karsten (2011) proposed a computationally stable approach for fitting GP models to deterministic computer simulators. They used a genetic algorithm based approach that is robust but computationally intensive for maximizing the likelihood. This paper implements a slightly modified version ofthe model proposed by Ranjan et al. (2011 ) in the R package GPfit. A novel parameterization of the spatial correlation function and a clustering based multi-start gradient based optimization algorithm yield robust optimization that is typically faster than the genetic algorithm based approach. We present two examples with R codes to illustrate the usage of the main functions in GPfit . Several test functions are used for performance comparison with the popular R package mlegp . We also use GPfit for a real application, i.e., for emulating the tidal kinetic energy model for the Bay of Fundy, Nova Scotia, Canada. GPfit is free software and distributed under the General Public License and available from the Comprehensive R Archive Network. ## April 02, 2015 ### RCpp Gallery #### Call matplotlib from R ## Motivation 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 Makevars.win in Rpyplot-package to see how to set the flags for Windows. ## Calling Python from R ### Initializing 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> #endif using namespace Rcpp; //Run Python commands from R //[[Rcpp::export]] void pyrun(std::string command) { PyRun_SimpleString(command.c_str()); } //You need to call this first //[[Rcpp::export]] void initialize_python() { #ifndef WIN32 dlopen("libpython2.7.so", RTLD_LAZY |RTLD_GLOBAL); //Required to import matplotlib #endif Py_Initialize(); pyrun("import matplotlib"); //pyrun("matplotlib.use('Qt4Agg')"); pyrun("import matplotlib.pyplot as plt"); } //Call after you're done //[[Rcpp::export]] void finalize_python() { Py_Finalize(); } ### 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 } return(xpy); } //Copy a numeric vector from R to Python //[[Rcpp::export]] 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 } ### Plotting 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 plt.show() 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) initialize_python() #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("plt.savefig('http://gallery.rcpp.org/articles/matplotlib-from-R/../figure/2015-04-02-pyplot.png')") #pyrun("plt.show()") #Uncomment this line to show the plot And here is the generated plot: ## March 26, 2015 ### Statistical Modelling #### Guest Editorial: Special Issue: Compositional Data Modelling #### Regression with compositional response having unobserved components or below detection limit values The typical way to deal with zeros and missing values in compositional data sets is to impute them with a reasonable value, and then the desired statistical model is estimated with the imputed data set, e.g., a regression model. This contribution aims at presenting alternative approaches to this problem within the framework of Bayesian regression with a compositional response. In the first step, a compositional data set with missing data is considered to follow a normal distribution on the simplex, which mean value is given as an Aitchison affine linear combination of some fully observed explanatory variables. Both the coefficients of this linear combination and the missing values can be estimated with standard Gibbs sampling techniques. In the second step, a normally distributed additive error is considered superimposed on the compositional response, and values are taken as ‘below the detection limit’ (BDL) if they are ‘too small’ in comparison with the additive standard deviation of each variable. Within this framework, the regression parameters and all missing values (including BDL) can be estimated with a Metropolis-Hastings algorithm. Both methods estimate the regression coefficients without need of any preliminary imputation step, and adequately propagate the uncertainty derived from the fact that the missing values and BDL are not actually observed, something imputation methods cannot achieve. #### Tools for compositional data with a total Compositional data analysis usually deals with relative information between parts where the total (abundances, mass, amount, etc.) is unknown or uninformative. This article addresses the question of what to do when the total is known and is of interest. Tools used in this case are reviewed and analysed, in particular the relationship between the positive orthant of D-dimensional real space, the product space of the real line times the D-part simplex, and their Euclidean space structures. The first alternative corresponds to data analysis taking logarithms on each component, and the second one to treat a log-transformed total jointly with a composition describing the distribution of component amounts. Real data about total abundances of phytoplankton in an Australian river motivated the present study and are used for illustration. #### Sparse principal balances Compositional data analysis deals with situations where the relevant information is contained only in the ratios between the measured variables, and not in the reported values. This article focuses on high-dimensional compositional data (in the sense of hundreds or even thousands of variables), as they appear in chemometrics (e.g., mass spectral data), proteomics or genomics. The goal of this contribution is to perform a dimension reduction of such data, where the new directions should allow for interpretability. An approach named principal balances turned out to be successful for low dimensions. Here, the concept of sparse principal component analysis is proposed for constructing principal directions, the so-called sparse principal balances. They are sparse (contain many zeros), build an orthonormal basis in the sample space of the compositional data, are efficient for dimension reduction and are applicable to high-dimensional data. ## March 22, 2015 ### RCpp Gallery #### Parsing Dates and Times ## Motivation 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). ## Implementation 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.imbue(formats[i]); 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 } } ## Illustration 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 20, 2015 ### Journal of Statistical Software #### BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments Vol. 64, Issue 11, Mar 2015 Abstract: Empirical analysis of statistical algorithms often demands time-consuming experiments. We present two R packages which greatly simplify working in batch computing environments. The package BatchJobs implements the basic objects and procedures to control any batch cluster from within R. It is structured around cluster versions of the well-known higher order functions Map, Reduce and Filter from functional programming. Computations are performed asynchronously and all job states are persistently stored in a database, which can be queried at any point in time. The second package, BatchExperiments, is tailored for the still very general scenario of analyzing arbitrary algorithms on problem instances. It extends package BatchJobs by letting the user define an array of jobs of the kind “apply algorithm A to problem instance P and store results”. It is possible to associate statistical designs with parameters of problems and algorithms and therefore to systematically study their influence on the results. The packages’ main features are: (a) Convenient usage: All relevant batch system operations are either handled internally or mapped to simple R functions. (b) Portability: Both packages use a clear and well-defined interface to the batch system which makes them applicable in most high-performance computing environments. (c) Reproducibility: Every computational part has an associated seed to ensure reproducibility even when the underlying batch system changes. (d) Abstraction and good software design: The code layers for algorithms, experiment definitions and execution are cleanly separated and enable the writing of readable and maintainable code. #### gems: An R Package for Simulating from Disease Progression Models Vol. 64, Issue 10, Mar 2015 Abstract: Mathematical models of disease progression predict disease outcomes and are useful epidemiological tools for planners and evaluators of health interventions. The R package gems is a tool that simulates disease progression in patients and predicts the effect of different interventions on patient outcome. Disease progression is represented by a series of events (e.g., diagnosis, treatment and death), displayed in a directed acyclic graph. The vertices correspond to disease states and the directed edges represent events. The package gems allows simulations based on a generalized multistate model that can be described by a directed acyclic graph with continuous transition-specific hazard functions. The user can specify an arbitrary hazard function and its parameters. The model includes parameter uncertainty, does not need to be a Markov model, and may take the history of previous events into account. Applications are not limited to the medical field and extend to other areas where multistate simulation is of interest. We provide a technical explanation of the multistate models used by gems, explain the functions of gems and their arguments, and show a sample application. ## March 09, 2015 ### Journal of the Royal Statistical Society: Series C #### Bayesian modelling of rainfall data by using non‐homogeneous hidden Markov models and latent Gaussian variables #### Analysis of multivariate failure times in the presence of selection bias with application to breast cancer #### Bayesian optimal interval designs for phase I clinical trials #### Spatially weighted functional clustering of river network data ## 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. ## January 16, 2015 ### Modern Toolmaking #### caretEnsemble 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) ## 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 https://github.com/gregorgorjanc/cpumemlog. Read more about this useful utility at the GitHub site. ## January 01, 2015 ### Journal of the Royal Statistical Society: Series B #### Contents of volume 76, 2014 #### Index of authors, volume 76, 2014 #### Split sample methods for constructing confidence intervals for binomial and Poisson parameters #### Transformations and invariance in the sensitivity analysis of computer experiments ### Journal of the Royal Statistical Society: Series A #### Understanding Advanced Statistical Methods P. Westfall and K. S. S. Henning, 2013 Boca Raton, Chapman and Hall–CRC 570 pp., £44.99 ISBN 978‐1‐466‐51210‐8 #### Statistics Topics S. Mehta, 2014 CreateSpace Independent Publishing Platform 160 pp., £4.50 ISBN 978‐1‐499‐27353‐3 #### Bayesian Data Analysis 3rd edn A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari and D. B. Rubin, 2013 Boca Raton, Chapman and Hall–CRC 676 pp., £44.99 ISBN 1‐439‐84095‐4 #### Book Reviews ## December 27, 2014 ### RCpp Gallery #### Create an R-tree data structure using Rcpp and Boost::Geometry ### Introduction The purpose of this post is to show how to use Boost::Geometry library which was introduced recently in Rcpp. Especially, we focus on R-tree data structure for searching objects in space because only one spatial index is implemented - R-tree Currently in this library. Boost.Geometry which is part of the Boost C++ Libraries gives us algorithms for solving geometry problems. In this library, the Boost.Geometry.Index which is one of components is intended to gather data structures called spatial indexes which are often used to searching for objects in space quickly. Generally speaking, spatial indexes stores geometric objects’ representations and allows searching for objects occupying some space or close to some point in space. ### R-tree R-tree is a tree data structure used for spatial searching, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. R-tree was proposed by Antonin Guttman in 1984 as an expansion of B-tree for multi-dimensional data and plays significant role in both theoretical and applied contexts. It is only one spatial index implemented in Boost::Geometry. As a real application of this, It is often used to store spatial objects such as restaurant locations or the polygons which typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc in order to perform a spatial query like “Find all stations within 1 km of my current location”, “Let me know all road segments in 2 km of my location” or “find the nearest gas station” which we often ask google seach by your voice recenlty. In this way, the R-tree can be used (nearest neighbor) search for some places. You can find more explanations about R-tree in Wikipedia. ### Write a wrapper class of rtree in Boost::Geometry using Rcpp Now, we write a simple C++ wrapper class of rtree class in Boost::Geometry::Index that we can use in R. The most important feature to mention here is the use of Rcpp module to expose your own class to R. Although almost all classes in Boost library have a lot of functions, , you do not use all in many cases. In that case, you should write your wrapper class for making your code simple. ### Rcpp code // [[Rcpp::depends(BH)]] // Enable C++11 via this plugin to suppress 'long long' errors // [[Rcpp::plugins("cpp11")]] #include <vector> #include <Rcpp.h> #include <boost/geometry.hpp> #include <boost/geometry/geometries/point.hpp> #include <boost/geometry/geometries/box.hpp> #include <boost/geometry/index/rtree.hpp> using namespace Rcpp; // Mnemonics namespace bg = boost::geometry; namespace bgi = boost::geometry::index; typedef bg::model::point<float, 2, bg::cs::cartesian> point_t; typedef bg::model::box<point_t> box; typedef std::pair<box, unsigned int> value_t; class RTreeCpp { public: // Constructor. // You have to give spatial data as a data frame. RTreeCpp(DataFrame df) { int size = df.nrows(); NumericVector id = df[0]; NumericVector bl_x = df[1]; NumericVector bl_y = df[2]; NumericVector ur_x = df[3]; NumericVector ur_y = df[4]; for(int i = 0; i < size; ++i) { // create a box box b(point_t(bl_x[i], bl_y[i]), point_t(ur_x[i], ur_y[i])); // insert new value rtree_.insert(std::make_pair(b, static_cast<unsigned int>(id[i]))); } } // This method(query) is k-nearest neighbor search. // It returns some number of values nearest to some point(point argument) in space. std::vector<int> knn(NumericVector point, unsigned int n) { std::vector<value_t> result_n; rtree_.query(bgi::nearest(point_t(point[0], point[1]), n), std::back_inserter(result_n)); std::vector<int> indexes; std::vector<value_t>::iterator itr; for ( itr = result_n.begin(); itr != result_n.end(); ++itr ) { value_t value = *itr; indexes.push_back( value.second ); } return indexes; } private: // R-tree can be created using various algorithm and parameters // You can change the algorithm by template parameter. // In this example we use quadratic algorithm. // Maximum number of elements in nodes in R-tree is set to 16. bgi::rtree<value_t, bgi::quadratic<16> > rtree_; }; // [[Rcpp::export]] std::vector<int> showKNN(Rcpp::DataFrame df, NumericVector point, unsigned int n) { RTreeCpp tree(df); // recreate tree each time return tree.knn(point, n); } ### R code using RTreeCpp First, we create a sample data set of spatial data. # Sample spatial data(boxes) points <- data.frame( id=0:2, bl_x=c(0, 2, 4), bl_y=c(0, 2, 4), ur_x=c(1, 3, 5), ur_y=c(1, 3, 5)) /* * To visually the data, we use the following code: */ size <- nrow(points) #colors for rectangle area colors <- rainbow(size) #Plot these points plot(c(0, 5), c(0, 5), type= "n", xlab="", ylab="") for(i in 1:size){ rect(points[i, "bl_x"], points[i, "bl_y"], points[i, "ur_x"], points[i, "ur_y"], col=colors[i]) } legend(4, 2, legend=pointsid, fill=colors) One can use the RTreeCpp class as follows: # new RTreeCpp object # Search nearest neighbor points(return value : id of points data) showKNN(points, c(0, 0), 1) [1] 0  showKNN(points, c(0, 0), 2) [1] 1 0  showKNN(points, c(0, 0), 3) [1] 2 1 0  Note the re-creation of the RTreeCpp object is of course inefficient, but the Rcpp Gallery imposes some constraints on how we present code. For actual application a stateful and persistent object would be created. This could be done via Rcpp Modules as well a number of different ways. Here, however, we need to recreate the object for each call as knitr (which is used behind the scenes) cannot persist objects between code chunks. This is simply a technical limitation of the Rcpp Gallery—but not of Rcpp itself. ## 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: set.seed(0) x <- sample(0:9, 100, rep=T)  SPSS R 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. library(devtools) install_github("markheckmann/ryouready") # install from github repo library(ryouready) # load package library(ggplot2) 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  # Literature 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. ## 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: https://gist.github.com/zachmayer/e591cf868b3a381a01d6#file-openblas-sh To use veclib with R, follow these intructions: https://gist.github.com/zachmayer/e591cf868b3a381a01d6#file-veclib-sh ## OLD POST: 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. ## 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. ## 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). library(png) img <- readPNG(system.file("img", "Rlogo.png", package="png")) class(img)  ## [1] "array"  dim(img)  ## [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("http://i.imgur.com/A14ntCt.png", 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 set.seed(1) 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] img } 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. library(EBImage) 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("http://i.imgur.com/A14ntCt.png", 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 as.raster(img) } r <- nrow(img) / ncol(img) # get image ratio s <- 1 # size # create random data set.seed(1) x <- data.frame(x=rnorm(16, c(2,2,4,4)), y=rnorm(16, c(1,3)), colors=1:4) # 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. ### Literature 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 https://www.utdallas.edu/~herve/Abdi-MFA2007-pretty.pdf ## 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.” ## 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). ## February 17, 2014 ### Seth Falcon #### Have Your SHA and Bcrypt Too ## Fear 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: userid salt hashed_pass  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: userid salt hashed_pass hash_type salt2  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. ## 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://github.com/seth/rebar_lock_deps_plugin.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 ], digraph_utils:topsort(G). %% 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) -> de_dup(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 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> /* 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; close(f); return *r1 & *r2; } int main(void) { char r1; char r2; int ret; ret = get_random_chars(&r1, &r2); if (ret < 0) fprintf(stderr, "error"); else 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 error  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. ## Conclusions 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 https://github.com/lpenz/lpenz.org. ## 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: ## Filefile <- "myfile.txt" ## Create connectioncon <- file(description=file, open="r") ## Hopefully you know the number of lines from some other source orcom <- paste("wc -l ", file, " | awk '{ print$1 }'", sep="")n <- system(command=com, intern=TRUE) ## Loop over a file connectionfor(i in 1:n) {  tmp <- scan(file=con, nlines=1, quiet=TRUE)  ## do something on a line of data }
Created by Pretty R at inside-R.org

## November 20, 2013

#### Sending data from client to server and back using shiny

After 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
verbatimTextOutput("results"),

# javascript code to send data to shiny server
tags$script(' document.getElementById("mydiv").onclick = function() { var number = Math.random(); Shiny.onInputChange("mydata", number); }; ') ))  Now, on the server side, we can simply access the data that was sent by addressing it the usual way via the input object (i.e. input$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$results = renderPrint({ input$mydata
})

})


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.

library(shiny)
runGist("https://gist.github.com/markheckmann/7554422")


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
tags$script(' Shiny.addCustomMessageHandler("myCallbackHandler", function(color) { document.getElementById("mydiv").style.backgroundColor = color; }); ')  Let’s move to the server side. I want the server to send the data to the handler function whenever the div is clicked, i.e. when the value of input$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'
observe({
input$mydata color = rgb(runif(1), runif(1), runif(1)) session$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.

runGist("https://gist.github.com/markheckmann/7554458")


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

library(shiny)

shinyUI( bootstrapPage(

# include the js code
includeScript("mycode.js"),

# a div named mydiv

## 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):

#!/usr/bin/Rscript

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 argumentsargs <- commandArgs(TRUE) ## Default setting when no arguments passedif(length(args) < 1) {  args <- c("--help")} ## Help sectionif("--help" %in% args) {  cat("      The R Script       Arguments:      --arg1=someValue   - numeric, blah blah      --arg2=someValue   - character, blah blah      --arg3=someValue   - logical, blah blah      --help              - print this text       Example:      ./test.R --arg1=1 --arg2="output.txt" --arg3=TRUE \n\n")   q(save="no")} ## Parse arguments (we expect the form --arg=value)parseArgs <- function(x) strsplit(sub("^--", "", x), "=")argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))argsL <- as.list(as.character(argsDF$V2))names(argsL) <- argsDF$V1 ## Arg1 defaultif(is.null(args$arg1)) { ## do something} ## Arg2 defaultif(is.null(args$arg2)) {  ## do something} ## Arg3 defaultif(is.null(args\$arg3)) {  ## do something}

## ... your code here ...
Created by Pretty R at inside-R.org

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.

## March 24, 2013

### Romain Francois

#### Moving

This blog is moving to blog.r-enthusiasts.com. The new one is powered by wordpress and gets a subdomain of r-enthusiasts.com.

See you there

## 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.