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
   = “confidence”)



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

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