# 1268 search results for "latex"

## Model assessment (and predictions for RuPaul’s Drag Race Season 5, Episode 9)

March 25, 2013
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Last week, Alaska took it home with her dangerous performance, while Ivy Winters was sent home after going up against Alyssa Edwards. This is sad on many fronts. First, I love me some Ivy Winters. Second, Jinkx had revealed that she had a crush on Ivy, and the relationship that may have flourished between the… Continue reading →

## Writing a MS-Word document using R (with as little overhead as possible)

March 24, 2013
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The problem: producing a Word (.docx) file of a statistical report created in R, with as little overhead as possible. The solution: combining R+knitr+rmarkdown+pander+pandoc (it is easier than it is spelled). If you get what this post is about, just …Read more »

## Estimating the Decay Rate and the Half-Life of DDT in Trout – Applying Simple Linear Regression with Logarithmic Transformation

This blog post uses a function and a script written in R that were displayed in an earlier blog post. Introduction This is the second of a series of blog posts about simple linear regression; the first was written recently on some conceptual nuances and subtleties about this model.  In this blog post, I will use

## Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

March 19, 2013
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$Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)$

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the

## One Pager Performance Report with knitr, R, and a Different Font

March 18, 2013
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Although I suffer from complete ignorance of typography, with a little help from a post from Hyndsight and post from mages' blog, I wanted to try a different font on the one-pager performance report that we created in Onepager Now with knitR. I do not think Open Sans Light is the best choice for this...

## Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)

March 17, 2013
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$Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)$

Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the

## 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