# 2292 search results for "regression"

## Regression tree using Gini’s index

January 27, 2013
By
$Y$

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...

## Regressions 101: “Significance”

January 25, 2013
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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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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

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

December 23, 2012
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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 →

## 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 ...

## Generalized Boosted Regression with A Monotonic Marginal Effect for Each Predictor

December 18, 2012
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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

## Shootout 2012 : first PLS regressions

November 23, 2012
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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 ...

## VIDEO: Looking to the regression coefficients in R

November 16, 2012
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(This article was first published on NIR-Quimiometria, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on their blog: NIR-Quimiometria. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL,...

## Influential Data in Multilevel Regression: What are your strategies?

November 13, 2012
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The application of multilevel regression models has become common practice in the field of social sciences. Multilevel regression models take into account that observations on individual respondents are nested within higher-level groups such as schools, classrooms, states, and countries. In ...

## On Box-Cox transform in regression models

November 13, 2012
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$Y_i=\beta_0+\beta_1 X_i+\varepsilon_i$

A few days ago, a former student of mine, David, contacted me about Box-Cox tests in linear models. It made me look more carefully at the test, and I do not understand what is computed, to be honest. Let us start with something simple, like a linea...