cg (version 1.0-2)

comparisons: Create a table of comparisons amongst groups

Description

Create a table of comparisons based on group estimates and variance-covariance matrix. Pairwise or custom specified contrasts are estimated and tested.

Usage

comparisons(estimates, varcovmatrix, errordf = Inf, endptscale,
            mcadjust = FALSE, alpha = 0.05, type = "pairwisereflect",
            contrastmatrix = NULL, n, offset = NULL, cnames = "derive",
            analysisname = "", endptname = "", digits = NULL, addpct = FALSE,
            display = "print")

Arguments

estimates

A named vector of estimates. Each estimate element is a measure of the center of the group. The name of each group must be present in the names attribute of the vector.

varcovmatrix

The estimated variance-covariance matrix associated with the estimates. Must have the same number of columns and rows as the length of the estimates vector.

errordf

Can be one of three types of values:

[object Object],[object Object],[object Object]

endptscale

Must be specified as "log" or "original". If "log" then the estimates vector is assumed to be in the log scale, and calculations will transform it to the original scale for the generated tab

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

alpha

Significance level, by default set to 0.05 to correspond to 95% confidence.

type

Can be one of four values:

[object Object],[object Object],[object Object],[object Object]

contrastmatrix

Only relevant if type="custom" is specified. In that case, a numeric matrix with the number of rows equal to the number of comparisons of interest is needed. The number of columns must be equal to the number of groups in the <

n

Needs to be specified only when errordf="approx". In this case it needs to be a vector of group sample sizes, the same in length as the estimates vector.

offset

Optional, If for example a numeric constant was added to all response values before calculation of the estimates as means in the "log" scale, this could be used to adjust the estimates and comparisons ap

cnames

If the default value of "derive" is used, row names are derived for the table that reflect the A vs. B type of comparison items in each row, using the names attrributes of the estimates vector and the middle term of <

analysisname

Optional, a character text that will be printed along with the results table. The default value is the empty "".

endptname

Optional, a character text that will be printed along with the results table. The default value is the empty "".

digits

Optional, For output display purposes, values will be rounded to this numeric value. Only the integers of 0, 1, 2, 3, and 4 are accepted. No rounding is done during any calculations. The default value is NULL, which w

addpct

Only relevant if endptscale=="original". An column of percent differences is added for the comparisons, as a descriptive supplement to the original scale differences that are formally estimated.

display

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

Value

• Creates a return data frame object that specifies the comparison of the form A vs. B in each row, and with these columns:

  [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

  An additional column addpct of percent differences is added if endptscale=="original" and addpct=TRUE, as a descriptive supplement to the original scale differences that are formally estimated.

Warning

This function was created for internal use in the cg package as its use can be seen in the comparisonsTable methods code. Therefore any direct use of it needs to be done cautiously.

concept

• comparisons
• multiplicity

Details

Only two-sided Wald-type of confidence intervals are possible with this function. 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.

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.

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)


## Easier way: notice the comparisonsTable call

comparisonsTable(canine.fit, model="olsonly")

## Manual way
## Instead of comparisonsTable(canine.fit, model="olsonly")

comparisons(estimates=canine.fit@olsfit$coef,
            varcovmatrix=vcov(canine.fit@olsfit),
            errordf=canine.fit@olsfit$df.residual,
            endptscale="log",
            analysisname="Canine",
            digits=1,
            endptname="Prostate Volume")