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This function is used to generate simulation data used in tensor prediction regression.
TPRsim(p, r, u, n)
The dimension of predictor, a vector in the form of
The dimension of response, a scale.
The structural dimension of envelopes at each mode, a vector with the same length as p.
The sample size.
The predictor of dimension
The response of dimension
A list of envelope subspace basis of dimension
The tensor coefficients of dimension
A lists of estimated covariance matrices at each mode for the tensor predictors, i.e.,
The input p,r,u
.
The tensor predictor regression model is of the form,
Zhang, X. and Li, L., 2017. Tensor envelope partial least-squares regression. Technometrics, 59(4), pp.426-436.
# NOT RUN {
p <- c(10, 10, 10)
u <- c(1, 1, 1)
r <- 5
n <- 200
dat <- TPRsim(p = p, r = r, u = u, n = n)
x <- dat$x
y <- dat$y
fit_std <- TPR.fit(x, y, method="standard")
# }
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