testcoef.senv: Hypothesis test of the coefficients of the scaled envelope model
Description
This function tests the null hypothesis L * beta * R = A versus the alternative hypothesis L * beta * R ~= A, where beta is estimated under the scaled envelope model.
Usage
testcoef.senv(m, L, R, A)
Arguments
m
A list containing estimators and other statistics inherited from scale.env.
L
The matrix multiplied to beta on the left. It is a d1 by r matrix, while d1 is less than or equal to r.
R
The matrix multiplied to beta on the right. It is a p by d2 matrix, while d2 is less than or equal to p.
A
The matrix on the right hand side of the equation. It is a d1 by d2 matrix.
Value
The output is a list that contains following components.
chisqStatistic
The test statistic.
dof
The degrees of freedom of the reference chi-squared distribution.
pValue
p-value of the test.
covMatrix
The covariance matrix of vec(L beta R).
Details
This function tests for hypothesis H0: L beta R = A, versus Ha: L beta R != A. The beta is estimated by the scaled envelope model. If L = Ir, R = Ip and A = 0, then the test is equivalent to the standard F test on if beta = 0. The test statistic used is vec(L beta R - A) hatSigma^-1 vec(L beta R - A)^T, where beta is the envelope estimator and hatSigma is the estimated asymptotic covariance of vec(L beta R - A). The reference distribution is chi-squared distribution with degrees of freedom d1 * d2.
# NOT RUN {data(sales)
X <- sales[, 1:3]
Y <- sales[, 4:7]
m <- senv(X, Y, 2)
L <- diag(4)
R <- as.matrix(c(1, 0, 0))
A <- matrix(0, 4, 1)
test.res <- testcoef.senv(m, L, R, A)
test.res
# }