grpSummaryTable.cgOneFactorFit: Create a table of estimated group means and variability with...
In cg: Compare Groups, Analytically and Graphically

Description Usage Arguments Details Value Note Author(s) References Examples

Create a table of estimated group means based on the cgOneFactorFit object. Standard errors and confidence intervals are added. A cgOneFactorGrpSummaryTable class object is created.

1 2	## S4 method for signature 'cgOneFactorFit' grpSummaryTable(fit, mcadjust=FALSE, alpha=0.05, display="print", ...)

`fit`	A fit object of class `cgOneFactorFit`.
`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 a delay, usually just for a few seconds, due to computing time of the critical point in order to calculate the confidence intervals.
`alpha`	Significance level, by default set to `0.05`.
`display`	One of three valid values: `"print"` The default value; It calls a `print` method for the created `cgOneFactorGrpSummaryTable` object, which is a formatted text output of the table(s). `"none"` Supresses any printing. Useful, for example, when just assignment of the resulting object is desired. `"show"` Calls the default `showDefault` method, which will just print out the `cgOneFactorGrpSummaryTable` components.
`...`	Additional arguments. Only one is currently valid: `model` For `cgOneFactorFit` `fit` objects that have classical least squares `lm()` or resistant & robust `rlm()` fits, the following argument values are possible: `"both"` Group summary tables based on both the ordinary classical least squares and resistant & robust fits are performed. This is the default when both fits are present in the `cgOneFactorFit` object specified in the `fit` argument. If the resistant & robust fit is not available, this value is not relevant. `"olsonly"` Only a group summary table based on the ordinary classical least squares `olsfit` fit is performed. `"rronly"` Only a group summary table based on the resistant and robust `rrfit` fit is performed. For other possible `cgOneFactorFit` fit components such as accelerated failure time or unequal variance models, the `model` argument is not relevant, and the appropriate group summary table will be calculated for these model types.

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.

The multcomp package provides a unified way to calculate critical points based on the comparisons of interest in a "family". Thus a user does not need to worry about choosing amongst the myriad names of multiple comparison procedures.

Creates an object of class cgOneFactorGrpSummaryTable, with the following slots:

ols.grps: The table of group estimates based on the olsfit component of the cgOneFactorFit, unless model="rronly" is specified. In that case the slot value is NULL. Will not be appropriate in the case where a valid aftfit component is present in the cgOneFactorFit object. See below for the data frame structure of the table.
rr.grps: The table of group estimates based on the rrfit component of the cgOneFactorFit object, if a valid resistant & robust fit object is present. If rrfit is a simple character value of "No fit was selected.", or model="olsonly" was specified, then the value is NULL. See below for the data frame structure of the table.
aft.grps: The table of group estimates based on the aftfit component of the cgOneFactorFit object if a valid accelerated failure time fit object is present. If aftfit is a simple character value of "No fit was selected.", then the value is NULL. See below for the data frame structure of the table.
uv.grps: The table of group estimates based on the uvfit component of the cgOneFactorFit object if a valid unequal variances fit object is present. If uvfit is a simple character value of "No fit was selected.", then the value is NULL. See below for the data frame structure of the table.
settings: A list of settings carried from the cgOneFactorFit fit object, and the addition of some specified arguments in the method call above: alpha and mcadjust. These are used for the print.cgOneFactorGrpSummaryTable method, invoked for example when display="print".

The data frame structure of the comparisons table in a *.comprs slot consists of row.names that specify group name (factor level), and these columns:

estimate: The estimated group mean. If settings$endptscale=="log" in the fit object, this will be back-transformed to a geometric mean.
se: The estimated standard error of the group mean estimate. If settings$endptscale=="log" in the fit object, this estimate will be based on the Delta method, and will begin to be a poor approximation when the standard error in the logscale exceeds 0.50.
lowerci: The lower 100 * (1-alpha) % confidence limit of the group mean estimate. With the default alpha=0.05, this is 95%. If settings$endptscale=="log" in the fit object, the confidence limit is first computed in the logarithmic scale of analysis, and then back-transformed to the original scale.
upperci: The upper 100 * (1-alpha) % confidence limit of the difference estimate. With the default alpha=0.05, this is 95%. If settings$endptscale=="log" in the fit object, the confidence limit is first computed in the logarithmic scale of analysis, and then back-transformed to the original scale.

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.

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)

grpSummaryTable(canine.fit)

grpSummaryTable(canine.fit, mcadjust=TRUE, model="olsonly")


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

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

grpSummaryTable(gmcsfcens.fit)