Description Usage Arguments Details Value Note Author(s) References See Also Examples
Create a table of estimated group means based on a fit by the cg package.
1 | grpSummaryTable(fit, mcadjust = FALSE, alpha = 0.05, display = "print", ...)
|
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
|
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.
A method-specific grpSummaryTable
object is returned.
See the specific methods for discussion of return values.
Contact cg@billpikounis.net for bug reports, questions, concerns, and comments.
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
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.
grpSummaryTable.cgOneFactorFit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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)
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