grpSummaryTable {cg} | R Documentation |
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
|
mcadjust |
Do a multiple comparisons adjustment, based on the simultaneous
inference capabilities of the multcomp package. See Details
below. The default value is |
alpha |
Significance level, by default set to |
display |
One of three valid values:
|
... |
Additional arguments, depending on the specific method written for
the object. Currently, there is only one such specific method; see
|
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.
Value
A method-specific grpSummaryTable
object is returned.
See the specific methods for discussion of return values.
Note
Contact cg@billpikounis.net for bug reports, questions, concerns, and comments.
Author(s)
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
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)