One of the most commonly used uses of Statistical Software is building models, and that too logistic regression models for propensity in marketing of goods and services.
If building a model is what you do-here is a brief easy essay on how to build a model in R.
1) Packages to be used-
For smaller datasets
- CAR Package http://cran.r-project.org/web/packages/car/index.html
- GVLMA Package http://cran.r-project.org/web/packages/gvlma/index.html
- ROCR Package http://rocr.bioinf.mpi-sb.mpg.de/
- Relaimpo Package
- DAAG package
- MASS package
- Bootstrap package
- Leaps package
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.
- outp=lm(y~x1+x2+xn,data=dataset) Model Eq
- summary(outp) Model Summary
- par(mfrow=c(2,2)) + plot(outp) Model Graphs
- vif(outp) MultiCollinearity
- gvlma(outp) Heteroscedasticity using GVLMA package
- outlierTest (outp) for Outliers
- predicted(outp) Scoring dataset with scores
- > predict(lm.result,data.frame(conc = newconc), level = 0.9, interval = “confidence”)
For a Reference Card -Cheat Sheet see
3) Also read-