This package helps to perform efficient model building for many kinds
of regression models, including (generalized) linear, robust linear,
It also contains some useful utility functions.
The principal function
regr() allows fitting regression
models of many types by calling well-known R functions for
Ordinary linear regression,
logistic, Poisson, other generalized linear models, also for
ordinal or nominal response variable.
The output of the function is somewhat different from the output of the
base R functions
polr etc. – it should
be more informative for the task of model building.
The next most important function is the
plot method for results
plot.regr), which gives more complete
regression diagnostic plots than the specific plot methods of the
mentioned “base” R functions.
An important part of residual analysis which is often neglected is
plotting residuals versus explanatory variables, often called term
plots. This is part of
plot.regr, but can also be called
independently by calling
regr result is printed, the output is more informative than
the classical tables.
The package contains the methods for the usual extractor functions, like
predict, ..., as well as for
add1, etc, ...
The package contains a number of additional functions that should be useful for efficient data analysis. They include
plresx() to plot residuals against explanatory variables,
plres2x() to produce a plot showing interactions between two
plmatrix(), a version of
displays any rectangular scatterplot matrix and can also be used to
produce the full usual quadratic scatterplot matrix for many variables
in appropriate junks with panels that are still large enough to be useful.
The package also includes some additional utility functions, like
logst() for log transformation of variables with zeros,
showd() for printing a "representative" part of a dataset.
For documenting the steps of the data analysis, there are several documenting functions:
doc add attributes to data and results, which
should allow for tracing the history of these objects (not yet
stamp adds tracking information to plots, consisting of a project
title, a step label, and the date when the plot is produced.
Some default options are added to
options to reflect user
preferences for output, see the help entries for
Werner A. Stahel, ETH Zurich, [email protected]
More information is available at http://stat.ethz.ch/~stahel/regression
The car package.
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