Compute the prediction error for the simultaneous envelope estimator using m-fold cross validation.
Usage
cv.stenv(X, Y, q, u, m, nperm)
Arguments
X
Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables.
Y
Responses. An n by r matrix, r is the number of responses. The response can be univariate or multivariate and must be continuous variable.
q
Dimension of the X-envelope. An integer between 0 and p.
u
Dimension of the Y-envelope. An integer between 0 and r.
m
A positive integer that is used to indicate m-fold cross validation.
nperm
A positive integer indicating number of permutations of the observations, m-fold cross validation is run on each permutation.
Value
The output is a real nonnegative number.
cvPE
The prediction error estimated by m-fold cross validation.
Details
This function computes prediction errors using m-fold cross validation. For a fixed dimension (q, u), the data is randomly partitioned into m parts, each part is in turn used for testing for the prediction performance while the rest m-1 parts are used for training. This process is repeated for nperm times, and average prediction error is reported. If Y is multivariate, the identity inner product is used for computing the prediction errors.
# NOT RUN {data(fiberpaper)
X <- fiberpaper[, 5:7]
Y <- fiberpaper[, 1:4]
m <- 5
nperm <- 50
# }# NOT RUN {cvPE <- cv.stenv(X, Y, 2, 3, m, nperm)
# }# NOT RUN {cvPE
# }