npmc {npmc} | R Documentation |
npmc implements the nonparametrical multiple testprocedures (Behrens-Fisher- and Steel-type for the all-pairs and many-to-one situations) described in the paper "A unified approach to Simultaneous Rank Test Procedures in the Unbalanced One-way Layout" written by Ullrich Munzel and Ludwig Hothorn.
npmc(dataset, control=NULL, df=2, alpha=0.05) ## S3 method for class 'npmc': summary(object, type="both", info=TRUE, short=TRUE, corr=FALSE, ...)
dataset |
a data-frame with variables 'var' (the response-variable)and 'class' (containing the class-level). The data may be unsorted and unbalanced. The 'summary'-function supports 'name' and 'description' attributes |
control |
level of control-group for the many-to-one situation or NULL for the all-pairs situation (default) |
df |
determines the BF-teststatistics' asymptotic distribution function (0 = standard-normal approximation, 1 = simple t-approximation, 2 = Satterthwaite t-approximation (default)) |
alpha |
Level for the (1-alpha) confidence-intervals |
object |
An object of type 'npmc' |
type |
You may select either "BF" or "Steel". All other values extract information for both types |
info |
Prints info about the procedure and the data |
short |
Only prints the most relevant items of the test-results |
corr |
Prints the correlation-matrices |
... |
further arguments to be passed to or from methods. |
npmc performs nonparametrical multiple testprocedures (Behrens-Fisher- and Steel-type for the all-pairs and many-to-one situations) and computes the simultaneous (1-alpha) confidence limits for the relative effects.
The one-sided tests reject if group with smaller index has larger values due to the calculation of the relative effect-estimators.
A list of several other values and structures with subvalues
info |
group.index: an integer number to identify the group
class.level: the class-level of this group nobs: the number of observations in this group |
corr |
BF: The Behrens-Fisher-type correlation-matrix
Steel: The Steel-type correlation-matrix The matrices have attributes 'adjusted' which indicate if negative eigenvalues were changed to zero (see 'Notes'-section). |
test |
BF / Steel: Two lists containing the test-results and some other
characteristics of the multiple nonparametric Behrens-Fisher- and
Steel-type testprocedures
cmp: names the compared groups ('a-b') gn: the sum of both sample-sizes effect: the relative effect-estimator variance: the variance-estimator std: the standard-deviation statistic: the test-statistic p-value 1s: the 1-sided p-value p-value 2s: the 1-sided p-value zero: TRUE if zero variances occured and were substituted by 0.0000001 |
control |
The factor-level of the control group (NULL for allpairs-comparison) |
df.method |
The original df-paramter |
df |
The estimated degrees of freedom for the multivariate t-distribution or 0 if the standard normal distribution was used for approximating the teststatistics' distribution (depending on the df-parameter). |
This function requires the 'mvtnorm' package to calculate the p-values for the test-statistics. If this package is not available on your system, the result will contain NA's as p-values.
The functions in the 'mvtnorm' package seem to use randomized values for integral calculations, so the results from npmc concerning p-values and confidence-intervals differ from call to call and can be recognized only as an approximative solution.
If a correlation-matrix has negative eigenvalues, they are replaced by 0 as recommended by the paper. The matrix's 'adjusted' attribute indicates whether or not the matrix was forced to be positive semidefinite.
Joerg Helms
"A unified approach to Simultaneous Rank Test Procedures in the Unbalanced One-way Layout" written by Ullrich Munzel and Ludwig Hothorn.
data(brain) summary(npmc(brain), type="BF")