ttests2s.mv {smsets} | R Documentation |
Multiple two-sample t-tests for multivariate data
Description
Performs multiple two-sample t-tests on more than one response vector with
corrected significance levels using any of the adjustment methods for
multiple comparisons offered by p.adjust
. Effects sizes are also
computed.
Usage
ttests2s.mv(
x,
group,
level1,
alternative = "two.sided",
var.equal = FALSE,
P.adjust = "none",
unit = "units"
)
Arguments
x |
A data frame with one two-level factor and p response variables. |
group |
Two-level factor defining groups. It must be one of the columns
in |
level1 |
A character string identifying Sample 1. The string must be one
of the factor levels in |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
var.equal |
a logical variable indicating whether to treat the two
variances as being equal. If |
P.adjust |
p-value correction method, a character string. Can be abbreviated. |
unit |
A character string in cases in which all response variables are
measured using the same physical units. Useful to fully characterize raw
effect sizes. The default value is the character string |
Details
This function extends the univariate t.test
for the comparison of mean
values for two samples, when more than one variable is involved in the data
analysis, so that type one error rates ("false significances") in a series of
univariate t-tests are adjusted according to the number of response
variables analyzed. The pairwise comparisons between the two levels in
group
with corrections for multiple testing are made over more than
one response vector thus, the function is a variation of
pairwise.t.test
.
The methods implemented are the same as those contained in the
p.adjust.methods
for p.adjust
: "bonferroni"
,
"holm"
, "hochberg"
, "hommel"
, "BH"
(Benjamini-Hochberg) or its alias "fdr"
(False Discovery Rate), and
"BY"
(Benjamini & Yekutieli). The default pass-through option
("none"
) is also included.
Value
Returns an object of class "ttests2s.mv"
, a list containing
the following components:
name | A character string describing the function |
t.list | A list containing p vectors of length 5, each vector
having the computed t-statistic, the degrees of freedom for the
t-statistic, the adjusted p-value for the test, the raw effect size
estimator: \bar{x}_1 - \bar{x}_2 , and the post hoc effect size
estimator recommended by Hedges (1981), analogous to Cohen's d, given
by |\bar{x}_1 - \bar{x}_2| / \hat{\sigma} . Here \hat{\sigma}
= \sqrt{MSE} where MSE is mean squared error, the estimator
of the variance for the difference of means \bar{x}_1 - \bar{x}_2 .
|
alternative | A character string specifying the alternative hypothesis chosen. |
var.equal | A logical variable indicating whether the two
variances were treated as being equal TRUE or not FALSE .
|
P.adjust | A character string indicating the correction method chosen |
raw.ES | The raw effect size (scalar) expressed in the
pre-specified unit s |
unit | A character string indicating the unit s chosen |
Hedges.d | The post hoc effect size Hedges' estimator (scalar) |
group | A character string specifying the name of the two-level factor defining groups. |
levels.group | A vector of length two showing the two levels in
factor group . |
data.name | A character string giving the name of the data. |
data | the data frame analyzed. |
The extractor function print.ttests2s.mv
returns an
annotated output of each t-test and effect size estimation.
Author(s)
Jorge Navarro Alberto, ganava4@gmail.com
References
Hedges, L. V. 1981. Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics 6(2): 107–128.
Examples
data(sparrows)
ttests.sparrows <- ttests2s.mv(sparrows, group = Survivorship, level1 = "S",
var.equal = TRUE, P.adjust = "bonferroni",
unit = "mm")
ttests.sparrows