The functions perform \(F\) or \(t\) testing for several responses based on a matrix of hypothesis observations and a matrix of error observations.
unitests(xyObj)unitest(modelData, errorData, dfError = dim(errorData)[1])
a design-with-responses object created by xy_Obj
matrix of hypothesis observations
matrix of error observations
Degrees of freedom for error needs to be specified if
errorData is incomplete
unitest returns a list with components
\(p\)-values
The test statistics as \(t\)-statistics (when single degree of freedom) or \(F\)-statistics
unitests returns a list with components
Matrix of
\(p\)-values from unitest, one row for each term.
Matrix of test statistics from unitest, one row for each
term.
modelData and errorObs correspond to hypObs and
errorObs calculated by xy_Obj. These matrices are efficient
representations of sums of squares and cross-products (see
xy_Obj for details). This means the univariate
\(F\)-statistics can be calculated straightforwardly from these input
matrices. Furthermore, in the single-degree-of-freedom case,
\(t\)-statistics with correct sign can be obtained.
unitests is a wrapper function that calls unitest for each
term in the xyObj (see xy_Obj for details) and collects
the results.