predict.SMME | R Documentation |
Given new covariate data this function computes the linear predictors
based on the estimated model coefficients in an object produced by the function
softmaximin
. Note that the data can be supplied in three different
formats: i) for general models as a n' \times p matrix (p is the
number of model coefficients and n' is the number of new data points),
ii) for array models with custom design as a list of one, two or three Kronecker component
matrices each of size n_i' \times p_i, i = 1, 2, 3
(n_i' is the number of new marginal data points in the ith dimension),
iii) for wavelet based models a string indicating the wavelet used to produce
the model object.
## S3 method for class 'SMME' predict(object, x, ...)
object |
An object of class SMME, produced with |
x |
An object that should be like the input to the |
... |
ignored. |
A list of length length(zeta)
. If x
is a n' \times p
matrix each list item is a n'\times m_ζ matrix containing the linear
predictors computed for each lambda
. If x
is a string or a list of
tensor component matrices and fit$dim = d
, each list item is a d + 1
array containing predictions computed for each lambda
.
Adam Lund
##size of example n1 <- 65; n2 <- 26; n3 <- 13; p1 <- 13; p2 <- 5; p3 <- 4 ##marginal design matrices (Kronecker components) X1 <- matrix(rnorm(n1 * p1, 0, 0.5), n1, p1) X2 <- matrix(rnorm(n2 * p2, 0, 0.5), n2, p2) X3 <- matrix(rnorm(n3 * p3, 0, 0.5), n3, p3) X <- list(X1, X2, X3) component <- rbinom(p1 * p2 * p3, 1, 0.1) Beta1 <- array(rnorm(p1 * p2 * p3, 0, 0.1) + component, c(p1 , p2, p3)) Beta2 <- array(rnorm(p1 * p2 * p3, 0, 0.1) + component, c(p1 , p2, p3)) mu1 <- RH(X3, RH(X2, RH(X1, Beta1))) mu2 <- RH(X3, RH(X2, RH(X1, Beta2))) Y1 <- array(rnorm(n1 * n2 * n3, mu1), dim = c(n1, n2, n3)) Y2 <- array(rnorm(n1 * n2 * n3, mu2), dim = c(n1, n2, n3)) Y <- array(NA, c(dim(Y1), 2)) Y[,,, 1] <- Y1; Y[,,, 2] <- Y2; fit <- softmaximin(X, Y, zeta = c(1, 10), penalty = "lasso", alg = "npg") ##new data in tensor component form X1 <- matrix(rnorm(2 * p1), nrow = 2) X2 <- matrix(rnorm(3 * p2), nrow = 3) X3 <- matrix(rnorm(4 * p3), nrow = 4) Yhat <- predict(fit, x = list(X1, X2, X3))
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