sme: Compute Simple Main Effects for a 2-way Factorial Design

In DeducerANOVA: Factorial ANOVA for Deducer

Description

sme computes t-tests between all possible pairs of levels of one factor at each level of a second factor

Usage

sme(data, dv, test.var, is.within = FALSE, at.var = NULL, var.equal=FALSE,p.adjust.method = "holm")

Arguments

data	The data.frame to be analyzed.
dv	A dot object such as .(helping). The dependent (response) variable.
test.var	A dot object such as .(time.pressure). This is the factor whose levels will be arranged into all possible pairs and compared using a t-test.
is.within	Logical value indicating whether test.var is a within-subjects factor, in which case dependent t-tests will be used.
at.var	A dot object such as .(messag). The second factor in the design. The t-tests on levels of test.var will be conducted AT each level of at.var.
var.equal	Logical value indicating whether the variances within the cells of the factorial design are assumed to be equal. This argument is passed to the t.test function.
p.adjust.method	A method for computing adjusted p-values. The default is 'holm'. See help(p.adjust) for the available options.

Value

A data.frame consisting of 7 columns. The first column provides the levels of the at.var. The second and third columns show the two levels of test.var that will be compared. The fourth column reports the t statistic. The fifth column reports the degrees of freedom. The sixth column reports the unadjusted p-value of the t statistic. The seventh column reports the p-value adjusted for multiple comparisons. The name of this column reflects the adjustment method. The data.frame is also assigned attributes that name the data, test.var, at.var, and p.adjust.method.

Author(s)

Bill Altermatt

See Also

p.adjust, t.test

Examples

data(Adler)
ad1 <- sme(Adler,.(rating),.(instruction),at.var=.(expectation))
ad1
ad2 <- sme(Adler,.(rating),.(expectation),at.var=.(instruction))
ad2
attributes(ad2)

DeducerANOVA documentation built on May 2, 2019, 6:11 p.m.