1524 search results for "Regression"

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

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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

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Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

March 6, 2013
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Veterinary Epidemiologic Research: Linear Regression Part 2 – Checking assumptions

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

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Veterinary Epidemiologic Research: Linear Regression

February 14, 2013
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Veterinary Epidemiologic Research: Linear Regression

This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If

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Regression on categorical variables

January 30, 2013
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Regression on categorical variables

This morning, Stéphane asked me tricky question about extracting coefficients from a regression with categorical explanatory variates. More precisely, he asked me if it was possible to store the coefficients in a nice table, with information on the variable and the modality (those two information being in two different columns). Here is some code I did to produce the...

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Evolution of a logistic regression

January 28, 2013
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Evolution of a logistic regression

In my last post I showed how one can easily summarize the outcome of a logistic regression. Here I want to show how this really depends on the data-points that are used to estimate the model. Taking a cue from the evolution of a correlation I have plotted the estimated Odds Ratios (ORs) depending on

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Regression tree using Gini’s index

January 27, 2013
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Regression tree using Gini’s index

In order to illustrate the construction of regression tree (using the CART methodology), consider the following simulated dataset, > set.seed(1) > n=200 > X1=runif(n) > X2=runif(n) > P=.8*(X1<.3)*(X2<.5)+ + .2*(X1<.3)*(X2>.5)+ + .8*(X1>.3)*(X1<.85)*(X2<.3)+ + .2*(X1>.3)*(X1<.85)*(X2>.3)+ + .8*(X1>.85)*(X2<.7)+ + .2*(X1>.85)*(X2>.7) > Y=rbinom(n,size=1,P) > B=data.frame(Y,X1,X2) with one dichotomos varible (the variable of interest, ), and two continuous ones (the explanatory ones  and ). > tail(B) Y...

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Regressions 101: “Significance”

January 25, 2013
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Regressions 101: “Significance”

SETUP (CAN BE SKIPPED) We start with data (how was it collected?) and the hope that Read more »

texreg: A package for beautiful and easily customizable LaTeX regression tables from R

January 20, 2013
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texreg: A package for beautiful and easily customizable LaTeX regression tables from R

There was a very informative post last week showing how the R package stargazer is used to generate nice LaTeX tables from a number of R objects. This package looks very useful. However, I would like to extol the virtues of another R package that converts model objects in R into LaTeX code: texreg. For

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Binary Classification – A Comparison of “Titanic” Proportions Between Logistic Regression, Random Forests, and Conditional Trees

December 23, 2012
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Binary Classification – A Comparison of “Titanic” Proportions Between Logistic Regression, Random Forests, and Conditional Trees

Now that I’m on my winter break, I’ve been taking a little bit of time to read up on some modelling techniques that I’ve never used before. Two such techniques are Random Forests and Conditional Trees.  Since both can be used … Continue reading →

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