The `remfpca` class represents regularized functional principal components components.
The `remfpca` class represents regularized functional principal components ('ReMFPCs') components.
Usage
Remfpca(
mvmfd_obj,
method = "power",
ncomp,
smooth_tuning = NULL,
sparse_tuning = NULL,
centerfns = TRUE,
alpha_orth = FALSE,
smoothing_type = "basispen",
sparse_type = "soft",
K_fold = 30,
sparse_CV = TRUE,
smooth_GCV = TRUE
)Arguments
- mvmfd_obj
An `mvmfd` object representing the multivariate functional data.
- method
A character string specifying the approach to be used for MFPCA computation. Options are "power" (the default), which uses the power algorithm, or "eigen", which uses the eigen decomposition approach.
- ncomp
The number of functional principal components to retain.
- smooth_tuning
A list or vector specifying the smoothing regularization parameter(s) for each variable. If NULL, non-smoothing MFPCA is estimated.
- sparse_tuning
A list or vector specifying the sparsity regularization parameter(s) for each variable. If NULL, non-sparse MFPCA is estimated.
- centerfns
Logical indicating whether to center the functional data before analysis. Default is TRUE.
- alpha_orth
Logical indicating whether to perform orthogonalization of the regularization parameters. If `method` is "power", setting `alpha_orth = FALSE` (default) uses the sequential power approach, while setting `alpha_orth = TRUE` uses the joint power approach.
- smoothing_type
The type of smoothing penalty to be applied on the coefficients. The types "coefpen" and "basispen" is supported. Default is "coefpen".
- sparse_type
The type of sparse penalty to be applied on the coefficients. The types "soft-threshold", "hard-threshold" and "SCAD" is supported. Default is "soft-threshold".
- K_fold
An integer specifying the number of folds in the sparse cross-validation process. Default is 30.
- sparse_CV
@param sparse_CV Logical indicating whether cross-validation should be applied to select the optimal sparse tuning parameter in sequential power approach. If `sparse_CV = TRUE`, a series of tuning parameters should be provided as a vector with positive number with max equals to number of subjects. If `sparse_CV = FALSE`, specific tuning parameters are given directly to each principal components. Tuning parameters should be provided as a vector with length equal to `ncomp`. If the dimensions of input tuning parameters are incorrect, it will be converted to a list internally, and a warning will be issued.
- smooth_GCV
@param smooth_GCV Logical indicating whether generalized cross-validation should be applied to select the optimal smooth tuning parameter. If `smooth_GCV = TRUE`, a series of tuning parameters should be provided as a list with length equal to the number of variables. If a list with incorrect dimensions is provided, it will be converted to a correct list internally, and a warning will be issued. If `smooth_GCV = FALSE`, specific tuning parameters are given directly. If `method` is "power" and `alpha_orth = FALSE` (sequential power), tuning parameters should be provided as a list with length equal to the number of variables, where each element is a vector of length `ncomp`. If `method` is "power" and `alpha_orth = TRUE` (joint power), tuning parameters should be provided as a vector with length equal to the number of variables. If the dimensions of input tuning parameters are incorrect, it will be converted to a list internally, and a warning will be issued.
Active bindings
pc_mfdAn object of class `mvmfd` where the first indices (fields) represents harmonics and second indices represents variables
lsv= Left singular values vectors
values= The set of eigenvalues
smooth_tuning= The list of smoothing penalties parameters
sparse_tuning= The list of sparse penalties parameters
GCVs= Generalized cross validations scores of smoothing penalties parameters. If both smoothing and sparse tuning penalties are used in the ReMFPCA method, this represents the conditional generalized cross-validation scores, which means it is computed based on the optimal sparse tuning parameter selected via cross validation.
CVs= Cross validations scores of sparse penalties parameters
mean_mfdA multivariate functional data object giving the mean function
Methods
Method new()
Usage
remfpca$new(
mvmfd_obj,
method = "power",
ncomp,
smooth_tuning = NULL,
sparse_tuning = NULL,
centerfns = TRUE,
alpha_orth = FALSE,
smoothing_type = "coefpen",
sparse_type = "soft",
K_fold = 30,
sparse_CV,
smooth_GCV
)Examples
require(fda)
# Brownian Bridge simulation on [0,1]
M <- 110 # number of components
N <- 20 # number of instances
n <- 100 # number of grides
t0 <- seq(0, 1, len = n)
j <- 1:M
alpha1 <- list(a1 = 2^seq(0, 1, length.out = 3), a2 = 2^seq(0, 1, length.out = 3))
psi_1 <- function(t, m) sin(m * pi * t) # eigenfunction of BB
psi_2 <- function(t, m) sin((2 * m - 1) * pi / 2 * t) # eigenfunction of BM
PC_1 <- outer(t0, j, FUN = psi_1) # n by M matrix
PC_2 <- outer(t0, j, FUN = psi_2) # n by M matrix
Z <- matrix(rnorm(N * M), nr = M)
lambda <- matrix(2 / (pi * (2 * j - 1)), nr = M, nc = N)
X_1t <- PC_1 %*% (lambda * Z)
X_2t <- PC_2 %*% (lambda * Z)
noise <- rnorm(n * N, 0, 0.1)
X_1 <- X_1t + noise
X_2 <- X_2t + noise
bs <- create.bspline.basis(c(0, 1), 51)
mdbs <- Basismfd(bs)
mfd1 <- Mfd(X = X_1, mdbs = mdbs)
mfd2 <- Mfd(X = X_2, mdbs = mdbs)
mvmfd_obj <- Mvmfd(mfd1, mfd2)
k <- 2
Re0 <- Remfpca(mvmfd_obj, ncomp = k, alpha = c(0, 0))
#> Error in if (method == "power" & alpha_orth == "FALSE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != ncomp) { warning("The length of 'smooth_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = ncomp) } smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } else { warning("The length of 'smooth_tuning' did not match 'mvmfd_obj$nvar' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, ncomp))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = ncomp)] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != ncomp) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = ncomp) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (sparse_CV == FALSE & length(sparse_tuning) != ncomp & !is.null(sparse_tuning)) { warning("The length of 'sparse_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) sparse_tuning <- rep(sparse_tuning, length.out = ncomp) } result <- sequential_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, sparse_tuning = sparse_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type, sparse_tuning_type = sparse_type, K_fold = K_fold, sparse_CV, smooth_GCV)} else if (method == "eigen" || alpha_orth == "TRUE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(1:mvmfd_obj$nvar, function(i) smooth_tuning[i]) } else { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, 1))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = 1)] smooth_tuning <- as.list(smooth_tuning) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- as.list(smooth_tuning) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != 1) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = 1) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar)[1:mvmfd_obj$nvar] } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (method == "power") { result <- joint_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type) } else { result <- eigen_approach(mvmfd_obj = mvmfd_obj, n = ncomp, alpha = smooth_tuning, centerfns = centerfns, penalty_type = smoothing_type) }}: the condition has length > 1
fpc0 <- Re0$pc_mfd
#> Error: object 'Re0' not found
scores0 <- inprod_mvmfd(mvmfd_obj, fpc0)
#> Error: object 'fpc0' not found
dim(scores0)
#> Error: object 'scores0' not found
Re0$alpha
#> Error: object 'Re0' not found
Re1 <- Remfpca(mvmfd_obj, ncomp = k, alpha = alpha1)
#> Error in if (method == "power" & alpha_orth == "FALSE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != ncomp) { warning("The length of 'smooth_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = ncomp) } smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } else { warning("The length of 'smooth_tuning' did not match 'mvmfd_obj$nvar' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, ncomp))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = ncomp)] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != ncomp) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = ncomp) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (sparse_CV == FALSE & length(sparse_tuning) != ncomp & !is.null(sparse_tuning)) { warning("The length of 'sparse_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) sparse_tuning <- rep(sparse_tuning, length.out = ncomp) } result <- sequential_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, sparse_tuning = sparse_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type, sparse_tuning_type = sparse_type, K_fold = K_fold, sparse_CV, smooth_GCV)} else if (method == "eigen" || alpha_orth == "TRUE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(1:mvmfd_obj$nvar, function(i) smooth_tuning[i]) } else { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, 1))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = 1)] smooth_tuning <- as.list(smooth_tuning) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- as.list(smooth_tuning) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != 1) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = 1) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar)[1:mvmfd_obj$nvar] } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (method == "power") { result <- joint_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type) } else { result <- eigen_approach(mvmfd_obj = mvmfd_obj, n = ncomp, alpha = smooth_tuning, centerfns = centerfns, penalty_type = smoothing_type) }}: the condition has length > 1
Re1$alpha
#> Error: object 'Re1' not found
Re3 <- Remfpca(mfd1, ncomp = k, alpha = alpha1$a1)
#> Error in if (method == "power" & alpha_orth == "FALSE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != ncomp) { warning("The length of 'smooth_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = ncomp) } smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } else { warning("The length of 'smooth_tuning' did not match 'mvmfd_obj$nvar' and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, ncomp))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = ncomp)] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != ncomp) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = ncomp) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (sparse_CV == FALSE & length(sparse_tuning) != ncomp & !is.null(sparse_tuning)) { warning("The length of 'sparse_tuning' did not match 'ncomp' and has been adjusted accordingly.", call. = FALSE) sparse_tuning <- rep(sparse_tuning, length.out = ncomp) } result <- sequential_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, sparse_tuning = sparse_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type, sparse_tuning_type = sparse_type, K_fold = K_fold, sparse_CV, smooth_GCV)} else if (method == "eigen" || alpha_orth == "TRUE") { if (is.vector(smooth_tuning) & !is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(1:mvmfd_obj$nvar, function(i) smooth_tuning[i]) } else { warning("The length of 'smooth_tuning' did not match number of variables and has been adjusted accordingly.", call. = FALSE) smooth_tuning <- replicate(mvmfd_obj$nvar, smooth_tuning, simplify = FALSE) } } else if (is.matrix(smooth_tuning)) { if (smooth_GCV == FALSE) { if (!all(dim(smooth_tuning) == c(mvmfd_obj$nvar, 1))) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), rep(1:ncol(smooth_tuning), length.out = 1)] smooth_tuning <- as.list(smooth_tuning) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- as.list(smooth_tuning) } } else { if (dim(smooth_tuning)[1] != mvmfd_obj$nvar) { smooth_tuning <- smooth_tuning[rep(1:nrow(smooth_tuning), length.out = mvmfd_obj$nvar), , drop = FALSE][1:mvmfd_obj$nvar, , drop = FALSE] smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) warning("The dimensions of 'smooth_tuning' did not match the expected size and have been adjusted accordingly.", call. = FALSE) } else { smooth_tuning <- split(smooth_tuning, row(smooth_tuning)) } } } else if (is.list(smooth_tuning)) { if (smooth_GCV == FALSE) { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar) } smooth_tuning <- lapply(smooth_tuning, function(vec) { if (length(vec) != 1) { warning("Adjusting vector length in 'smooth_tuning' to match 'ncomp'.", call. = FALSE) vec <- rep(vec, length.out = 1) } vec }) } else { if (length(smooth_tuning) != mvmfd_obj$nvar) { warning("Adjusting 'smooth_tuning' to match 'mvmfd_obj$nvar'.", call. = FALSE) smooth_tuning <- rep(smooth_tuning, length.out = mvmfd_obj$nvar)[1:mvmfd_obj$nvar] } } } if (!is.null(smooth_tuning)) { names(smooth_tuning) <- paste0("var", 1:mvmfd_obj$nvar) } if (method == "power") { result <- joint_power(mvmfd_obj = mvmfd_obj, n = ncomp, smooth_tuning = smooth_tuning, centerfns = centerfns, alpha_orth = alpha_orth, smooth_tuning_type = smoothing_type) } else { result <- eigen_approach(mvmfd_obj = mvmfd_obj, n = ncomp, alpha = smooth_tuning, centerfns = centerfns, penalty_type = smoothing_type) }}: the condition has length > 1
Re3$alpha
#> Error: object 'Re3' not found