Tests the null hypothesis that an arbitrary subset of the \(\beta _{ij}\)'s is zero, based on the least squares estimates, using the \(\chi^2\) test as in Section 7.1. The null and alternative are specified by pattern matrices \(P_0\) and \(P_A\), respectively. If the \(P_A\) is omitted, then the alternative will be taken to be the unrestricted model.
bothsidesmodel.chisquare(
x,
y,
z,
pattern0,
patternA = matrix(1, nrow = ncol(x), ncol = ncol(z))
)
An \(N \times P\) design matrix.
The \(N \times Q\) matrix of observations.
A \(Q \times L\) design matrix.
An \(N \times P\) matrix of 0's and 1's specifying the null hypothesis.
An optional \(N \times P\) matrix of 0's and 1's specifying the alternative hypothesis.
A `list` with the following components:
The vector of estimated parameters of interest.
The estimated covariance matrix of the estimated parameter vector.
The degrees of freedom in the test.
\(T^2\) statistic in (7.4).
The p-value for the test.
bothsidesmodel
, bothsidesmodel.df
,
bothsidesmodel.hotelling
, bothsidesmodel.lrt
,
and bothsidesmodel.mle
.
# NOT RUN {
# TBA - Submit a PR!
# }
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