1) Packages to be used- For smaller datasets
use these 1. CAR Package http://cran.r-project.org/web/packages/car/index.html 2. GVLMA Package http://cran.r-project.org/web/packages/gvlma/index.html 3. ROCR Package http://rocr.bioinf.mpi-sb.mpg.de/ 4. Relaimpo Package 5. DAAG package 6. MASS package 7. Bootstrap package 8. Leaps package Also see http://cran.r-project.org/web/packages/rms/index.html or RMS package rms works with almost any regression model, but it was especially written to work with binary or ordinal logistic regression, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression. For bigger datasets also see Biglm http://cran.r-project.org/web/packages/biglm/index.html and RevoScaleR packages. http://www.revolutionanalytics.com/products/enterprise-big-data.php 2) Syntax 1. outp=lm(y~x1+x2+xn,data=dataset) Model Eq 2. summary(outp) Model Summary 3. par(mfrow=c(2,2)) + plot(outp) Model Graphs 4. vif(outp) MultiCollinearity 5. gvlma(outp) Heteroscedasticity using GVLMA package 6. outlierTest (outp) for Outliers 7. predicted(outp) Scoring dataset with scores 8. anova(outp) 9. > predict(lm.result,data.frame(conc = newconc), level = 0.9, interval = âconfidenceâ) For a Reference Card -Cheat Sheet see http://cran.r-project.org/doc/contrib/Ricci-refcard-regression.pdf 3) Also read- http://cran.r-project.org/web/views/Econometrics.html from the blog post at- http://www.decisionstats.com/building-a-regression-model-in-r-use-rstats/ additional hint- please use google to search (packages for regression in R) before sending multiple emails on the r help list best regards ajay [[alternative HTML version deleted]]
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