1491 search results for "Regression"

Quantile Autoregression in R

February 9, 2013
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Quantile Autoregression in R

In the past, I wrote about robust regression. This is an important tool which handles outliers in the data. Roger Koenker is a substantial contributor in this area. His website is full of useful information and code so visit when … Continue reading →

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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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Influence.ME: Tools for Detecting Influential Data in Multilevel Regression Models

December 20, 2012
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Despite the increasing popularity of multilevel regression models, the development of diagnostic tools lagged behind. Typically, in the social sciences multilevel regression models are used to account for the nesting structure of the data, such as students in classes, migrants ...

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Generalized Boosted Regression with A Monotonic Marginal Effect for Each Predictor

December 18, 2012
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Generalized Boosted Regression with A Monotonic Marginal Effect for Each Predictor

In the practice of risk modeling, it is sometimes mandatory to maintain a monotonic relationship between the response and each predictor. Below is a demonstration showing how to develop a generalized boosted regression with a monotonic marginal effect for each predictor. Plot of Variable Importance Plot of Monotonic Marginal Effects

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Shootout 2012 : first PLS regressions

November 23, 2012
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Shootout 2012 : first PLS regressions

It´s time to start developing some regressions in order to find the best math treatment, the best number of terms, the best spectral regions, the best regression method,....This time I´m working with the PLS  package in R, and just to make ...

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