add: multivariate Y (aka projective resampling)
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CVE/R/CVE.R
73
CVE/R/CVE.R
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@ -163,7 +163,7 @@
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#'
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#' @importFrom stats model.frame
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#' @export
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cve <- function(formula, data, method = "simple", max.dim = 10L, ...) {
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cve <- function(formula, data, method = "mean", max.dim = 10L, ...) {
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# check for type of `data` if supplied and set default
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if (missing(data)) {
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data <- environment(formula)
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@ -173,8 +173,11 @@ cve <- function(formula, data, method = "simple", max.dim = 10L, ...) {
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# extract `X`, `Y` from `formula` with `data`
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model <- stats::model.frame(formula, data)
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X <- as.matrix(model[ ,-1L, drop = FALSE])
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Y <- as.double(model[ , 1L])
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Y <- stats::model.response(model, "double")
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X <- stats::model.matrix(model)
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if ("(Intercept)" %in% colnames(X)) {
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X <- X[, "(Intercept)" != colnames(X), drop = FALSE]
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}
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# pass extracted data on to [cve.call()]
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dr <- cve.call(X, Y, method = method, max.dim = max.dim, ...)
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@ -252,25 +255,39 @@ cve <- function(formula, data, method = "simple", max.dim = 10L, ...) {
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#' coef(cve.obj.simple1, k = 1)
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#' coef(cve.obj.simple2, k = 1)
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#' @export
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cve.call <- function(X, Y, method = "simple",
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func_list = list(function (x) x),
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nObs = sqrt(nrow(X)), h = NULL,
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cve.call <- function(X, Y,
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method = c("mean", "weighted.mean", "central", "weighted.central"),
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func_list = NULL, nObs = sqrt(nrow(X)), h = NULL,
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min.dim = 1L, max.dim = 10L, k = NULL,
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momentum = 0.0, tau = 1.0, tol = 1e-3,
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slack = 0.0, gamma = 0.5,
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V.init = NULL,
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max.iter = 50L, attempts = 10L,
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logger = NULL) {
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max.iter = 50L, attempts = 10L, nr.proj = 500L,
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logger = NULL
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) {
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# Determine method with partial matching (shortcuts: "Weight" -> "weighted")
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methods <- list(
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"simple" = 0L,
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"weighted" = 1L
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method <- match.arg(method)
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method_nr <- if(startsWith(method, "weighted")) 1L else 0L
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# Set default functions for ensamble methods (of indentity else)
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if (is.null(func_list)) {
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if (endsWith(method, "central")) {
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func_list <- list(
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function(Y) { q <- quantile(Y, 0.1); as.double(Y <= q) },
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function(Y) { q <- quantile(Y, c(0.1, 0.2)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.2, 0.3)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.3, 0.4)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.4, 0.5)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.5, 0.6)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.6, 0.7)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.7, 0.8)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, c(0.8, 0.9)); as.double(q[1] < Y & Y <= q[2]) },
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function(Y) { q <- quantile(Y, 0.9); as.double(q < Y) }
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)
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method_nr <- methods[[tolower(method), exact = FALSE]]
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if (!is.integer(method_nr)) {
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stop('Unable to determine method.')
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} else {
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func_list <- list(function(Y) Y)
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}
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}
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method <- names(which(method_nr == methods))
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# parameter checking
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if (!is.numeric(momentum) || length(momentum) > 1L) {
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@ -289,10 +306,13 @@ cve.call <- function(X, Y, method = "simple",
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if (!is.numeric(Y)) {
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stop("Parameter 'Y' must be numeric.")
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}
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if (is.matrix(Y) || !is.double(Y)) {
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Y <- as.double(Y)
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if (!is.double(Y)) {
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storage.mode(Y) <- "double"
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}
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if (nrow(X) != length(Y)) {
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if (!is.matrix(Y)) {
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Y <- as.matrix(Y)
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}
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if (nrow(X) != nrow(Y)) {
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stop("Rows of 'X' and 'Y' elements are not compatible.")
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}
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if (ncol(X) < 2) {
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@ -367,6 +387,13 @@ cve.call <- function(X, Y, method = "simple",
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stop("Parameter 'max.iter' must be at least 1L.")
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}
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if (!is.integer(nr.proj)) {
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nr.proj <- as.integer(nr.proj)
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}
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if (length(nr.proj) > 1 || nr.proj < 1) {
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stop("Parameter 'nr.proj' must be a single positive integer.")
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}
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if (is.null(V.init)) {
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if (!is.numeric(attempts) || length(attempts) > 1L) {
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stop("Parameter 'attempts' must be positive integer.")
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@ -378,8 +405,16 @@ cve.call <- function(X, Y, method = "simple",
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}
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}
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# Evaluate each function given `Y` and build a `n x |func_list|` matrix.
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# Projective resampling of the multivariate `Y` as a `n x nr.proj` matrix.
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if (ncol(Y) > 1) {
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projections <- matrix(rnorm(ncol(Y) * nr.proj), nr.proj)
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projections <- projections / sqrt(rowSums(projections^2))
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Y <- Y %*% t(projections)
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}
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# Evaluate each function given (possible projected) `Y` and build a
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# `n x (|func_list| * nr.proj)` matrix.
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Fy <- vapply(func_list, do.call, Y, list(Y))
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dim(Fy) <- c(nrow(Fy), prod(dim(Fy)[-1]))
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# Convert numerical values to "double".
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storage.mode(X) <- storage.mode(Fy) <- "double"
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@ -145,10 +145,11 @@ plot.sim <- function(sim) {
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pos = if(diff(stat) > 0) c("2", "4") else c("4", "2"))
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}
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multivariate.dataset <- function(n = 100, p = 6, q = 4) {
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multivariate.dataset <- function(dataset = 1, n = 100, p = 6, q = 4) {
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CVE <- getNamespace('CVE')
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X <- matrix(rnorm(n * p), n, p)
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if (dataset == 1) {
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Delta <- diag(1, q, q)
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Delta[1, 2] <- Delta[2, 1] <- -0.5
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epsilon <- CVE$rmvnorm(n, sigma = Delta)
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@ -158,46 +159,77 @@ multivariate.dataset <- function(n = 100, p = 6, q = 4) {
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B[3, 2] <- 1 / sqrt(5)
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Y <- X %*% B + epsilon
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list(X = X, Y = Y, B = B[, 1:2])
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B <- B[, 1:2]
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}
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if (dataset == 2) {
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B <- matrix(c(0.8, 0.6, 0, 0, 0, 0))
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eps <- matrix(0, n, q)
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Delta <- diag(1, q, q)
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for(i in 1:n) {
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Delta[1, 2] <- Delta[2, 1] <- sin(X[i, ] %*% B)
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eps[i, ] <- CVE$rmvnorm(1, sigma = Delta)
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}
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Y<-cbind(exp(eps[, 1]), eps[, 2:4])
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}
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list(X = X, Y = Y, B = B)
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}
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set.seed(42)
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reps <- 10L
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sim.cve <- vector("list", reps)
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reps <- 5L
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sim.cve.m <- vector("list", reps)
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sim.cve.c <- vector("list", reps)
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sim.cve.wm <- vector("list", reps)
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sim.cve.wc <- vector("list", reps)
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sim.tf1 <- vector("list", reps)
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sim.tf2 <- vector("list", reps)
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start <- Sys.time()
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for (i in 1:reps) {
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# ds <- dataset(1)
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ds <- multivariate.dataset()
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ds <- multivariate.dataset(2, n = 400)
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# sim.cve[[i]] <- list(
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# B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B)), ncol(ds$B)),
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sim.cve.m[[i]] <- list(
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B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "mean"), ncol(ds$B)),
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B.true = ds$B
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)
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sim.cve.c[[i]] <- list(
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B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "central"), ncol(ds$B)),
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B.true = ds$B
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)
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sim.cve.wm[[i]] <- list(
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B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "weighted.mean"), ncol(ds$B)),
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B.true = ds$B
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)
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sim.cve.wc[[i]] <- list(
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B.est = coef(CVE::cve.call(ds$X, ds$Y, k = ncol(ds$B), method = "weighted.central"), ncol(ds$B)),
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B.true = ds$B
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)
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# sim.tf1[[i]] <- list(
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# B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
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# optimizer_initialier = tf$optimizers$Adam)$B,
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# B.true = ds$B
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# )
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# sim.tf2[[i]] <- list(
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# B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
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# optimizer_initialier = tf$optimizers$Adam,
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# method = "weighted")$B,
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# B.true = ds$B
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# )
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sim.tf1[[i]] <- list(
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B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
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optimizer_initialier = tf$optimizers$Adam)$B,
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B.true = ds$B
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)
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sim.tf2[[i]] <- list(
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B.est = cve.tf(ds$X, ds$Y, ncol(ds$B),
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optimizer_initialier = tf$optimizers$Adam,
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method = "weighted")$B,
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B.true = ds$B
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)
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cat(sprintf("\r%4d/%d -", i, reps), format(Sys.time() - start), '\n')
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}
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# pdf('subspace_comp.pdf')
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par(mfrow = c(3, 1))
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plot.sim(sim.cve)
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plot.sim(sim.tf1)
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plot.sim(sim.tf2)
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par(mfrow = c(2, 2))
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plot.sim(sim.cve.m)
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plot.sim(sim.cve.c)
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plot.sim(sim.cve.wm)
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plot.sim(sim.cve.wc)
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# dev.off()
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