Output objects from package relaimpo have classes relimplm (output from calc.relimp), relimplmboot (output from boot.relimp), relimplmbooteval (output from booteval.relimp) or relimplmbootMI. For classes relimplm, relimplmbooteval and relimplmbootMI, there are methods for plotting and printing, usage of which is described below. For class relimplmbootMI, there is in addition a summary-method, which produces a less detailed output than the show / print - method. For classes relimplm, relimplmbooteval and relimplmbootMI, there is in addition a method for extracting slots of the class with $.
calc.relimp
(Package: relaimpo) :
Function to calculate relative importance metrics for linear models
calc.relimp calculates several relative importance metrics for the linear model. The recommended metrics are lmg (R^2 partitioned by averaging over orders, like in Lindemann, Merenda and Gold (1980, p.119ff)) and pmvd (a newly proposed metric by Feldman (2005) that is provided in the non-US version of the package only). For completeness and comparison purposes, several other metrics are also on offer (cf. e.g. Darlington (1968)).
relaimpo-package
(Package: relaimpo) :
Package to calculate relative importance metrics for linear models
relaimpo calculates several relative importance metrics for the linear model. The recommended metrics are lmg (R^2 partitioned by averaging over orders, like in Lindemann, Merenda and Gold (1980, p.119ff)) and pmvd (a newly proposed metric by Feldman (2005), non-US version only). For completeness, several other metrics are also on offer. Other packages with related topics: hier.part, relimp.
These functions provide bootstrap confidence intervals for relative importances. boot.relimp uses the R package boot to do the actual bootstrapping of requested metrics (which may take quite a while), while booteval.relimp evaluates the results and provides confidence intervals. Output from booteval.relimp is printed with a tailored print method, and a plot method produces bar plots with confidence indication of the relative importance metrics.
mianalyze.relimp
(Package: relaimpo) :
Function to do relative importance calculations based on multiply imputed datasets
The function mianalyze.relimp takes a list of imputed data frames (or matrices), calculates relative importance metrics for each of these and combines these metrics into overall estimates with estimated variances according to the method by Rubin (1987). The output object can be summarized, printed and plotted.
providing 
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