SimTestDiff {SimComp} | R Documentation |
Simultaneous tests for general contrasts (linear functions) of normal means (e.g., "Dunnett", "Tukey", "Williams" ect.) when there is more than one primary response variable (endpoint). The procedure of Hasler (2009) is applied for differences of means of normally distributed data. The covariance matrices (containing the covariances between the endpoints) may be assumed to be equal or possibly unequal for the different groups.
SimTestDiff(data, grp, resp = NULL, type = "Dunnett", base = 1, ContrastMat = NULL, alternative = "two.sided", Margin = NULL, covar.equal = FALSE)
data |
a data frame containing a grouping variable and the endpoints as columns |
grp |
a character string with the name of the grouping variable |
resp |
a vector of character strings with the names of the endpoints; if
resp=NULL (default), all column names of the data frame
without the grouping variable are chosen automatically |
type |
a character string, defining the type of contrast, with the following
options:
type is ignored if ContrastMat is specified
by the user (see below) |
base |
a single integer specifying the control group for Dunnett contrasts, ignored otherwise |
ContrastMat |
a contrast matrix, where columns correspond to groups and rows correspond to contrasts |
alternative |
a character string specifying the alternative hypothesis,
must be one of "two.sided" (default), "greater"
or "less" |
Margin |
a single numeric value, or a numeric vector corresponding to endpoints, or a matrix where columns correspond to endpoints and rows correspond to contrasts, default is 0 |
covar.equal |
a logical variable indicating whether to treat the covariance
matrices (containing the covariances between the endpoints)
for the different groups as being equal;
if TRUE then the pooled covariance matrix is used,
otherwise the Satterthwaite approximation to the degrees of
freedom is used according to Hasler and Hothorn (2008) |
The interest is in simultaneous tests for several linear combinations (contrasts) of
treatment means in a one-way ANOVA model, and simultaneously for multiple endpoints.
For example, the all-pair comparison of Tukey (1953) and the many-to-one comparison
of Dunnett (1955) are implemented, but allowing for multiple endpoints. Also, the
user is free to create other interesting problem-specific contrasts. An approximate
multivariate t-distribution is used to calculate (adjusted) p-values
(see Hasler, 2009). This approach controls the familywise error rate in the strong
sense. The covariance matrices of the treatment groups (containing the covariances
between the endpoints) can be assumed to be equal (covar.equal=TRUE
) or
unequal (covar.equal=FALSE
). If being equal, the pooled covariance matrix is
used, otherwise the Satterthwaite approximation to the degrees of freedom is used
according to Hasler and Hothorn (2008). Unequal covariance matrices occure if either
variances or correlations of some endpoints differ depending on the treatment groups.
An object of class SimTest containing:
estimate |
a matrix of estimated differences |
statistic |
a matrix of the calculated test statistics |
p.val.raw |
a matrix of raw p-values |
p.val.adj |
a matrix of p-values adjusted for multiplicity |
CorrMatDat |
either the estimated common correlation matrix of the data
(covar.equal=TRUE ) or the list of the different (one for
each treatment) estimated correlation matrices of the data
(covar.equal=FALSE ) |
CorrMatComp |
the estimated correlation matrix to be used for the multivariate t-distribution |
degr.fr |
either a single degree of freedom (covar.equal=TRUE ) or a
matrix of degrees of freedom (covar.equal=FALSE ) |
All measurement objects of each treatment group must have values for each endpoint.
If there are missing values then the procedure stops. If covar.equal=TRUE
,
then the number of endpoints must not be greater than the total sample size minus
the number of treatment groups. If covar.equal=FALSE
, the number of endpoints
must not be greater than the minimal sample size minus 1. Otherwise the procedure
stops.
All hypotheses are tested with the same test direction for all comparisons and
endpoints (alternative="..."
). In case of doubts, use "two.sided"
.
If Margin
is a single numeric value or a numeric vector, then the same
value(s) are used for the remaining comparisons or endpoints. If Margin
is
not specified, the default is 0.
Mario Hasler
Hasler, M. (2009): Extensions of Multiple Contrast Tests. PhD Thesis, Gottfried-Wilhelm-Leibniz-Universitaet Hannover.
Hasler, M. and Hothorn, L.A. (submitted): A Dunnett-type Procedure for Multiple Endpoints
Hasler, M. and Hothorn, L.A. (2008): Multiple contrast tests in the presence of heteroscedasticity. Biometrical Journal 50, 793-800.
SimTestRat
, SimCiDiff
,
SimCiRat
,
data(iris) comp <- SimTestDiff(data=iris, grp="Species", alternative="greater", Margin=1) summary(comp)