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.
# S4 method for cgOneFactorFit
grpSummaryTable(fit, mcadjust=FALSE, alpha=0.05, display="print", ...)Creates an object of class cgOneFactorGrpSummaryTable, with the
following slots:
ols.grpsThe 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.grpsThe 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.grpsThe 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.grpsThe 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.
settingsA 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:
estimateThe estimated group mean. If settings$endptscale=="log" in the
fit object, this will be back-transformed to a geometric mean.
seThe estimated standard error of the group mean
estimate. If settings$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.
lowerciThe lower 100 * (1-alpha) % confidence limit of the
group mean estimate. With the default alpha=0.05,
this is 95%. If settings$endptscale=="log" in the
fit object, the confidence limit is first computed in the
logarithmic scale of analysis, and then back-transformed to the
original scale.
upperciThe upper 100 * (1-alpha) % confidence limit of the
difference estimate. With the default alpha=0.05,
this is 95%. If settings$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.
A fit object of class cgOneFactorFit.
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.
Significance level, by default set to 0.05.
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.
Bill Pikounis [aut, cre, cph], John Oleynick [aut], Eva Ye [ctb]
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.
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)
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