1507 search results for "regression"

Compare Regression Results to a Specific Factor Level in R

February 6, 2014
By

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...

Read more »

Solutions for Multicollinearity in Regression(1)

February 3, 2014
By
Solutions for Multicollinearity in Regression(1)

In multiple regression analysis, multicollinearity is a common phenomenon, in which two or more predictor variables are highly correlated. If there is an exact linear relationship (perfect multicollinearity) among the independent variables, the rank of X is less than k+1(assume the number of predictor variables is k), and the matrix will not be invertible. So the strong correlations … Continue reading...

Read more »

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

January 31, 2014
By
Princeton’s guide to linear modeling and logistic regression with R

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,...

Read more »

Spurious Regression of Time Series

December 30, 2013
By
Spurious Regression of Time Series

spu.ri.ousadjective : not...

Read more »

Twelve Days 2013: LASSO Regression

December 19, 2013
By

Day Eight: LASSO Regression TL/DR LASSO 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...

Read more »

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

December 8, 2013
By
Logistic Regression with R: step by step implementation part-2

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...

Read more »

Using R to replicate common SPSS multiple regression output

December 4, 2013
By

(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...

Read more »

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

November 30, 2013
By
Logistic Regression with R: step by step implementation part-1

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...

Read more »

Bayesian Linear Regression Analysis (with non-informative priors but without Monte Carlo) In R

November 24, 2013
By
Bayesian Linear Regression Analysis (with non-informative priors but without Monte Carlo) In R

Continuing the previous post concerning linear regression analysis with non-informative priors in R, I will show how to derive numerical summaries for the regression parameters without Monte Carlo integration. The theoretical background for this post is contained in Chapter 14 of Bayesian Data Analysis which should be consulted for more information. The Residual Standard Deviation The

Read more »

Binomial regression model

November 18, 2013
By
Binomial regression model

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...

Read more »