1595 search results for "regression"

SAS PROC MCMC example in R: Logistic Regression Random-Effects Model

January 18, 2015
By

In this post I will run SAS example Logistic Regression Random-Effects Model in four R based solutions; Jags, STAN, MCMCpack and LaplacesDemon. To quote the SAS manual: 'The data are taken from Crowder (1978). The Seeds data set is a 2 x 2 fa...

Read more »

Introducing: Orthogonal Nonlinear Least-Squares Regression in R

January 17, 2015
By
Introducing: Orthogonal Nonlinear Least-Squares Regression in R

With this post I want to introduce my newly bred ‘onls’ package which conducts Orthogonal Nonlinear Least-Squares Regression (ONLS): http://cran.r-project.org/web/packages/onls/index.html. Orthogonal nonlinear least squares (ONLS) is a not so frequently applied and maybe overlooked regression technique that comes into question when one encounters an “error in variables” problem. While classical nonlinear least squares (NLS) aims

Read more »

Regression Solutions Available

January 8, 2015
By
Regression Solutions Available

The github page for the APM exercises has been updated with three new files for Chapters 6-8 (the section on regression). The classifications section is in-progress. Here's one of our fancy-pants graphs:

Read more »

SAS PROC MCMC in R: Nonlinear Poisson Regression Models

December 6, 2014
By
SAS PROC MCMC in R: Nonlinear Poisson Regression Models

In exercise 61.1 the problem is that the model has bad mixing. In the SAS manual the mixing is demonstrated after which a modified distribution is used to fix the model.In this post the same problem is tackled in R; MCMCpack, RJags, RStan and LaplaceDemon. MCMCpack has quite some mixing problems, RStan seems to do best.DataTo quote the SAS...

Read more »

Performing Logistic Regression in R and SAS

Performing Logistic Regression in R and SAS

Introduction My statistics education focused a lot on normal linear least-squares regression, and I was even told by a professor in an introductory statistics class that 95% of statistical consulting can be done with knowledge learned up to and including a course in linear regression.  Unfortunately, that advice has turned out to vastly underestimate the

Read more »

Interpreting regression coefficient in R

November 23, 2014
By
Interpreting regression coefficient in R

Linear models are a very simple statistical techniques and is often (if not always) a useful start for more complex analysis. It is however not so straightforward to understand what the regression coefficient means even in the most simple case when there are no interactions in the model. If we are not only fishing for

Read more »

SAS PROC MCMC example in R; Poisson Regression

November 16, 2014
By

In this post I will try to copy the calculations of SAS's PROC MCMC example 61.5 (Poisson Regression) into the various R solutions. In this post Jags, RStan, MCMCpack, LaplacesDemon solutions are shown. Compared to the first post in this series, rcppbugs and mcmc are not used. Rcppbugs has no poisson distribution and while I know how to...

Read more »

Estimating a Beta Regression with The Variable Dispersion in R

October 19, 2014
By
Estimating a Beta Regression with The Variable Dispersion in R

Read more »

Structured simulation of regression models – simReg package.

September 30, 2014
By

I'd like to introduce a package that simulates regression models. This includes both single level and multilevel (i.e. hierarchical or linear mixed) models up to two levels of nesting. The package produces a unified framework to simulate all types of c...

Read more »

Implementing an EM Algorithm for Probit Regressions

September 30, 2014
By
Implementing an EM Algorithm for Probit Regressions

Users new to the Rcpp family of functionality are often impressed with the performance gains that can be realized, but struggle to see how to approach their own computational problems. Many of the most impressive performance gains are demonstrated with seemingly advanced statistical methods, advanced C++–related constructs, or both. Even when users are able to understand how various demonstrated features operate in isolation, examples...

Read more »