# 1335 search results for "regression"

## Regression with multiple predictors

February 18, 2014
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(This article was first published on Digithead's Lab Notebook, and kindly contributed to R-bloggers) Now that I'm ridiculously behind in the Stanford Online Statistical Learning class, I thought it would be fun to try to reproduce the figure on page 36 of the slides from chapter 3 or page 81 of the book. The result is a curvaceous surface...

## Better living through zero-one inflated beta regression

February 6, 2014
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Dealing with proportion data on the interval  is tricky. I realized this while trying to explain variation in vegetation cover. Unfortunately this is a true proportion, and can’t be made into a binary response. Further, true 0’s and 1’s rule out beta regression. You could arcsine square root transform the data (but shouldn’t; Warton and Hui 2011)....

## Compare Regression Results to a Specific Factor Level in R

February 6, 2014
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Including a series of dummy variables in a regression in R is very simple. For example,ols <- lm(weight ~ Time + Diet, data = ChickWeight)summary(ols) The above regression automatically includes a dummy variable for all but the first level of the factor of the Diet variable.Call:lm(formula = weight ~ Time...

## Princeton’s guide to linear modeling and logistic regression with R

January 31, 2014
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If you're new to the R language but keen to get started with linear modeling or logistic regression in the language, take a look at this "Introduction to R" PDF, by Princeton's Germán Rodríguez. (There's also a browsable HTML version.) In a crisp 35 pages it begins by taking you through the basics of R: simple objects, importing data,...

## Twelve Days 2013: LASSO Regression

December 19, 2013
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Day Eight: LASSO RegressionTL/DRLASSO regression (least absolute shrinkage and selection operator) is a modified form of least squares regression that penalizes model complexity via a regularization parameter. It does so by including a term proportional to $||\beta||_{l_1}$ in the objective function which shrinks coefficients towards zero, and can even eliminate them entirely. In that light, LASSO is a...

## Logistic Regression with R: step by step implementation part-2

December 8, 2013
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Welcome to the second part of series blog posts! In previous part, we discussed on the concept of the logistic regression and its mathematical formulation. Now, we will apply that learning here and try to implement step by step in R. (If you know concept of logistic regression then move ahead in this part, otherwise The post Logistic...

## Using R to replicate common SPSS multiple regression output

December 4, 2013
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(This article was first published on Jeromy Anglim's Blog: Psychology and Statistics, and kindly contributed to R-bloggers) The following post replicates some of the standard output you might get from a multiple regression analysis in SPSS. A copy of the code in RMarkdown format is available on github. The post was motivated by this previous post that discussed using...

## Logistic Regression with R: step by step implementation part-1

November 30, 2013
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Very warm welcome to first part of my series blog posts. In previous blog post, we discussed about concept of the linear regression and its mathematical model representation.  We also tried to implement linear regression in R step by step. In this post I will discuss about the logistic regression  and how to implement the The post Logistic...

## Binomial regression model

November 18, 2013
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$Y_i\sim\mathcal{B}(p(\boldsymbol{X_i}))$

Most of the time, when we introduce binomial models, such as the logistic or probit models, we discuss only Bernoulli variables, . This year (actually also the year before), I discuss extensions to multinomial regressions, where  is a function on some simplex. The multinomial logistic model was mention here. The idea is to consider, for instance with three possible classes the following...