cg (version 0.9.0)

fit.cgOneFactorData: Fit models to a cgOneFactorData object

Description

Fits a one-factor model based on the cgOneFactorData object. The created object is designed for one-factor / one-way / unpaired samples collected data, and is of class cgOneFactorFit.

Usage

## S3 method for class 'cgOneFactorData':
fit(data, type="rr", \dots)

Arguments

data

A data object of class cgOneFactorData.

type

Type of model to fit, represented by a character value. The default value is "rr". The four current possibilities are: [object Object],[object Object],[object Object],[object Object]

...

Additional arguments, both optional, that are allowed to be specified dependent on the choice of the type argument. Otherwise they have no effect on the fit: [object Object],[object Object]

Value

• Creates an object of class cgOneFactorFit, with the following slots: [object Object],[object Object],[object Object],[object Object],[object Object]

concept

• resistant
• robust
• least squares
• censored
• accelerated failure time
• unequal variance

Details

In the current version of the cg package, most default settings for rlm are kept for the fit.cgOneFactorData method wrapper call when type="rr", with no capability to choose another value for an arguments such as psi, scale.est, and k2. The method argument is set to "MM". Analogously most survreg default settings are kept for the fit.cgOneFactorData method wrapper call when type="aft", with no capability to modify the arguments. Most notably the dist argument is set to "lognormal" or "gaussian", depending on whether a log scale analysis request is evident in the cgOneFactorData object or not, respectively.

References

Huber, P.J (1967), "The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions", Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1, 221-233. Venables, W. N. and Ripley, B. D. (2002), Modern Applied Statistics with S. Fourth edition. Springer.

Examples

data(canine)
canine.data <- prepareCGOneFactorData(canine, format="groupcolumns",
                                      analysisname="Canine",
                                      endptname="Prostate Volume",
                                      endptunits=expression(plain(cm)^3),
                                      digits=1, logscale=TRUE, refgrp="CC")
									  
canine.fit <- fit(data=canine.data, type="rr")


data(gmcsfcens)
gmcsfcens.data <- prepareCGOneFactorData(gmcsfcens, format="groupcolumns",
                                         analysisname="cytokine",
                                         endptname="GM-CSF (pg/ml)",
                                         logscale=TRUE)

gmcsfcens.fit <- fit(gmcsfcens.data, type="aft")