Compute bootstrap standard error for the reduced rank envelope estimator.
boot.rrenv(X, Y, u, d, B)The output is an r by p matrix.
The standard error for elements in beta computed by bootstrap.
Predictors. An n by p matrix, p is the number of predictors. The predictors can be univariate or multivariate, discrete or continuous.
Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.
Dimension of the envelope. An integer between 0 and r.
The rank of the coefficient matrix. An integer between 0 and u.
Number of bootstrap samples. A positive integer.
This function computes the bootstrap standard errors for the regression coefficients in the reduced rank envelope model.
data(vehicles)
X <- vehicles[, 1:11]
Y <- vehicles[, 12:15]
X <- scale(X)
Y <- scale(Y) # The scales of Y are vastly different, so scaling is reasonable here
B <- 100
if (FALSE) bootse <- boot.rrenv(X, Y, 4, 2, B)
if (FALSE) bootse
Run the code above in your browser using DataLab