Performs Welchs's t-test for multiple comparisons with one control.
welchManyOneTTest(x, ...)# S3 method for default
welchManyOneTTest(
x,
g,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = p.adjust.methods,
...
)
# S3 method for formula
welchManyOneTTest(
formula,
data,
subset,
na.action,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = p.adjust.methods,
...
)
# S3 method for aov
welchManyOneTTest(
x,
alternative = c("two.sided", "greater", "less"),
p.adjust.method = p.adjust.methods,
...
)
a numeric vector of data values, a list of numeric data vectors or a fitted model object, usually an aov fit.
further arguments to be passed to or from methods.
a vector or factor object giving the group for the
corresponding elements of "x"
.
Ignored with a warning if "x"
is a list.
the alternative hypothesis.
Defaults to two.sided
.
method for adjusting p values
(see p.adjust
).
a formula of the form response ~ group
where
response
gives the data values and group
a vector or
factor of the corresponding groups.
an optional matrix or data frame (or similar: see
model.frame
) containing the variables in the
formula formula
. By default the variables are taken from
environment(formula)
.
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NA
s. Defaults to getOption("na.action")
.
A list with class "PMCMR"
containing the following components:
a character string indicating what type of test was performed.
a character string giving the name(s) of the data.
lower-triangle matrix of the estimated quantiles of the pairwise test statistics.
lower-triangle matrix of the p-values for the pairwise tests.
a character string describing the alternative hypothesis.
a character string describing the method for p-value adjustment.
a data frame of the input data.
a string that denotes the test distribution.
For many-to-one comparisons in an one-factorial layout with normally distributed residuals and unequal variances Welch's t-test can be used. A total of \(m = k-1\) hypotheses can be tested. The null hypothesis H\(_{i}: \mu_0(x) = \mu_i(x)\) is tested in the two-tailed test against the alternative A\(_{i}: \mu_0(x) \ne \mu_i(x), ~~ 1 \le i \le k-1\).
This function is basically a wrapper function for
t.test(..., var.equal = FALSE)
. The p-values for the test
are calculated from the t distribution
and can be adusted with any method that is implemented in
p.adjust.methods
.
Welch, B. L. (1947) The generalization of "Student's" problem when several different population variances are involved, Biometrika 34, 28--35.
Welch, B. L. (1951) On the comparison of several mean values: An alternative approach, Biometrika 38, 330--336.
# NOT RUN {
set.seed(245)
mn <- rep(c(1, 2^(1:4)), each=5)
sd <- rep(1:5, each=5)
x <- mn + rnorm(25, sd = sd)
g <- factor(rep(1:5, each=5))
fit <- aov(x ~ g)
shapiro.test(residuals(fit))
bartlett.test(x ~ g)
anova(fit)
summary(welchManyOneTTest(fit, alternative = "greater", p.adjust="holm"))
# }
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