2158 search results for "regression"

Principal Components Regression, Pt. 2: Y-Aware Methods

May 23, 2016
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Principal Components Regression, Pt. 2: Y-Aware Methods

In our previous note, we discussed some problems that can arise when using standard principal components analysis (specifically, principal components regression) to model the relationship between independent (x) and dependent (y) variables. In this note, we present some dimensionality reduction techniques that alleviate some of those problems, in particular what we call Y-Aware Principal Components … Continue reading...

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Visual contrast of two robust regression methods

Visual contrast of two robust regression methods

Robust regression For training purposes, I was looking for a way to illustrate some of the different properties of two different robust estimation methods for linear regression models. The two methods I’m looking at are: least trimmed squares, implemented as the default option in lqs() a Huber M-estimator, implemented as the default option in rlm() Both functions...

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Principal Components Regression in R, an operational tutorial

May 17, 2016
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Principal Components Regression in R, an operational tutorial

John Mount Ph. D. Data Scientist at Win-Vector LLC Win-Vector LLC's Dr. Nina Zumel has just started a two part series on Principal Components Regression that we think is well worth your time. You can read her article here. Principal Components Regression (PCR) is the use of Principal Components Analysis (PCA) as a dimension reduction step prior to linear...

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Principal Components Regression, Pt.1: The Standard Method

May 16, 2016
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In this note, we discuss principal components regression and some of the issues with it: The need for scaling. The need for pruning. The lack of “y-awareness” of the standard dimensionality reduction step. The purpose of this article is to set the stage for presenting dimensionality reduction techniques appropriate for predictive modeling, such as y-aware … Continue reading...

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Manipulate(d) Regression!

May 5, 2016
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Manipulate(d) Regression!

The R package ‘manipulate’ can be used to create interactive plots in RStudio. Though not as versatile as the ‘shiny’ package, ‘manipulate’ can be used to quickly add interactive elements to standard R plots. This can prove useful for demonstrating statistical concepts, especially to a non-statistician audience. The R code at the end of this

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How long could it take to run a regression

April 6, 2016
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How long could it take to run a regression

This afternoon, while I was discussing with Montserrat (aka @mguillen_estany) we were wondering how long it might take to run a regression model. More specifically, how long it might take if we use a Bayesian approach. My guess was that the time should probably be linear in , the number of observations. But I thought I would be good to check. Let...

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Assessing significance of slopes in regression models with interaction

March 17, 2016
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Assessing significance of slopes in regression models with interaction

This is a pretty short post on an issue that popped at some point in the past, at that time I found a way around it but as it arose again recently I decided to go through it. The issue I had was that when modeling an interaction between a continuous (say temperature) and a Related Post

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How to export Regression results from R to MS Word

March 15, 2016
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How to export Regression results from R to MS Word

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare their manuscript for publication Related Post

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First steps with Non-Linear Regression in R

February 25, 2016
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First steps with Non-Linear Regression in R

Drawing a line through a cloud of point (ie doing a linear regression) is the most basic analysis one may do. It is sometime fitting well to the data, but in some (many) situations, the relationships between variables are not linear. In this case one may follow three different ways: (i) try to linearize the

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Multiple regression lines in ggpairs

February 16, 2016
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Multiple regression lines in ggpairs

Abstract Plots including multiple regression lines are added to a matrix of plots generated with the GGally package in R.1 Background Built upon ggplot2, GGally provides templates for combining plots into a matrix through the ggpairs function. Such...

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