# 1209 search results for "latex"

## Veterinary Epidemiologic Research: GLM – Logistic Regression

March 14, 2013
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$Veterinary Epidemiologic Research: GLM – Logistic Regression$

We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary epidemiology, often the outcome is dichotomous (yes/no), representing the presence or absence of disease or mortality. We code 1 for the presence of the outcome and 0

## reports 0.1.2 Released

March 12, 2013
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I’m very pleased to announce the release of reports : An R package to assist in the workflow of writing academic articles and other reports. This is the first CRAN release of reports: http://cran.r-project.org/web/packages/reports/index.html The reports package assists in writing … Continue reading →

## High Resolution Figures in R

March 12, 2013
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As I was recently preparing a manuscript for PLOS ONE, I realized the default resolution of R and RStudio images are insufficient for publication. PLOS ONE requires 300 ppi images in TIFF or EPS (encapsulated postscript) format. In R plots … Continue reading →

## Simulating Random Multivariate Correlated Data (Categorical Variables)

March 11, 2013
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This is a repost of the second part of an example that I posted last year but at the time I only had the PDF document (written in ). This is the second example to generate multivariate random associated data. This example shows how to generate ordinal, categorical, data. It is a little more complex than generating continuous

## Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and Matrix Representation

March 11, 2013
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$Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and Matrix Representation$

In the previous post, I forgot to show an example of Box-Cox transformation when there’s a lack of normality. The Box-Cox procedure computes values of which best “normalises” the errors. value Transformed value of Y 2 1 0.5 0 -0.5 -1 -2 For example: The plot indicates a log transformation. Matrix Representation We can use

## Simulating Random Multivariate Correlated Data (Continuous Variables)

March 11, 2013
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This is a repost of an example that I posted last year but at the time I only had the PDF document (written in ).  I’m reposting it directly into WordPress and I’m including the graphs. From time-to-time a researcher needs to develop a script or an application to collect and analyze data. They may also need

## Analyse Quandl data with R – even from the cloud

March 10, 2013
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I have read two thrilling news about the really promising time-series data provider called Quandl recently: Quandl: A Wikipedia for Time Series DataQuandl package released to CRANWith the help of the Quandl R package* (development version...

## Comparing quantiles for two samples

March 8, 2013
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Recently, for a research paper, I some samples, and I wanted to compare them. Not to compare they means (by construction, all of them were centered) but there dispersion. And not they variance, but more their quantiles. Consider the following boxplot type function, where everything here is quantile related (which is not the case for standard boxplot, see http://freakonometrics.hypotheses.org/4138,...

## Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

March 6, 2013
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We continue on the linear regression chapter the book Veterinary Epidemiologic Research. Using same data as last post and running example 14.12: Now we can create some plots to assess the major assumptions of linear regression. First, let’s have a look at homoscedasticity, or constant variance of residuals. You can run a statistical test, the

## Stan 1.2.0 and RStan 1.2.0

March 6, 2013
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$Stan 1.2.0 and RStan 1.2.0$

Stan 1.2.0 and RStan 1.2.0 are now available for download. See: http://mc-stan.org/ Here are the highlights. Full Mass Matrix Estimation during Warmup Yuanjun Gao, a first-year grad student here at Columbia (!), built a regularized mass-matrix estimator. This helps for posteriors with high correlation among parameters and varying scales. We’re still testing this ourselves, so The post Stan...