cg (version 1.0-2)

grpSummaryTable: Create a table of estimated group means and variability

Description

Create a table of estimated group means based on a fit by the cg package.

Usage

grpSummaryTable(fit, mcadjust = FALSE, alpha = 0.05, display = "print",  ...)

Arguments

fit

An fit object created with a fit method from the cg package. The only class of object currently available is cgOneFactorFit, wh

mcadjust

Do a multiple comparisons adjustment, based on the simultaneous inference capabilities of the multcomp package. See Details below. The default value is FALSE. If mcadjust=TRUE is specified, there will be

alpha

Significance level, by default set to 0.05.

display

One of three valid values: [object Object],[object Object],[object Object]

...

Additional arguments, depending on the specific method written for the object. Currently, there is only one such specific method; see grpSummaryTable.cgOneFactorFit for any

Value

• A method-specific grpSummaryTable object is returned. See the specific methods for discussion of return values.

concept

group summary

Details

When mcadjust=TRUE, a status message of "Some time may be needed as the critical point" "from the multcomp::summary.glht function call is calculated" is displayed at the console. This computed critical point is used for all subsequent p-value and confidence interval calculations.

References

Hothorn, T., Bretz, F., Westfall, P., Heiberger, R.M., and Schuetzenmeister, A. (2010). The multcomp package. Hothorn, T., Bretz, F., and Westfall, P. (2008). "Simultaneous Inference in General Parametric Models", Biometrical Journal, 50, 3, 346-363.

See Also

grpSummaryTable.cgOneFactorFit

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(canine.data)

canine.grpsumm <- grpSummaryTable(canine.fit)


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

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

gmcsfcens.grpsumm <- grpSummaryTable(gmcsfcens.fit)