140 lines
4.2 KiB
R
140 lines
4.2 KiB
R
################################################################################
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### Sparce SIR against SIR ###
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################################################################################
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devtools::load_all('tensorPredictors/')
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library(dr)
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n <- 100
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p <- 10
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X <- rmvnorm(n, sigma = 0.5^abs(outer(1:p, 1:p, `-`)))
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y <- rowSums(X[, 1:3]) + rnorm(n, 0.5)
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B <- as.matrix(1:p <= 3) / sqrt(3)
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dr.sir <- dr(y ~ X, method = 'sir', numdir = ncol(B))
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B.sir <- dr.sir$evectors[, seq_len(ncol(B)), drop = FALSE]
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dist.projection(B, B.sir)
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B.poi <- with(GEP(X, y, 'sir'), {
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POI(lhs, rhs, ncol(B), method = 'POI-C', use.C = TRUE)$vectors
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})
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dist.projection(B, B.poi)
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################################################################################
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### LDA (sparse Linear Discrimina Analysis) ###
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################################################################################
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devtools::load_all('tensorPredictors/')
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C <- function(rho, p) {
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res <- matrix(rho, p, p)
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diag(res) <- 1
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res
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}
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R <- function(rho, p) {
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rho^abs(outer(1:p, 1:p, `-`))
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}
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dataset <- function(nr) {
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K <- 3 # Nr. Groups
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n.i <- 30 # Sample group size for each of the K groups
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n <- K * n.i # Sample size
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p <- 200 # Nr. of predictors
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# Generate test data
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V <- cbind(matrix(c(
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2, 1, 2, 1, 2,
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1,-1, 1,-1, 1,
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0, 1,-1, 1, 0
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), 3, 5, byrow = TRUE),
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matrix(0, 3, p - 5)
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)
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W <- cbind(matrix(c(
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-1, 1, 1, 1, 1,
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1,-1, 1,-1, 1,
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1, 1,-1, 1, 0
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), 3, 5, byrow = TRUE),
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matrix(0, 3, p - 5)
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)
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if (nr == 1) { # Model 1
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y <- factor(rep(1:K, each = n.i))
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X <- rmvnorm(n, mu = rep(0, p)) + V[y, ]
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B <- cbind(V[1, ] - V[2, ], V[2, ] - V[3, ])
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} else if (nr == 2) { # Model 2
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y <- factor(rep(1:K, each = n.i))
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X <- rmvnorm(n, sigma = C(0.5, p)) + (V %*% C(0.5, p))[y, ]
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B <- cbind(V[1, ] - V[2, ], V[2, ] - V[3, ])
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} else if (nr == 3) { # Model 3
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y <- factor(rep(1:K, each = n.i))
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X <- rmvnorm(n, sigma = R(0.5, p)) + (V %*% R(0.5, p))[y, ]
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B <- cbind(V[1, ] - V[2, ], V[2, ] - V[3, ])
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} else if (nr == 4) { # Model 4
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y <- factor(rep(1:K, each = n.i))
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X <- rmvnorm(n, sigma = C(0.5, p)) + (W %*% C(0.5, p))[y, ]
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B <- cbind(W[1, ] - W[2, ], W[2, ] - W[3, ])
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} else if (nr == 5) { # Model 5
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K <- 4
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n <- K * n.i
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W.tilde <- 2 * rbind(W, colMeans(W))
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mu.tilde <- W.tilde %*% C(0.5, p)
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y <- factor(rep(1:K, each = n.i))
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X <- rmvnorm(n, sigma = C(0.5, p)) + mu.tilde[y, ]
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B <- cbind(W[1, ] - W[2, ], W[2, ] - W[3, ])
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} else {
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stop("Unknown model nr.")
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}
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list(X = X, y = y, B = qr.Q(qr(B)))
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}
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# Model 1
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fit <- with(dataset(1), {
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with(GEP(X, y, 'lda'), POI(lhs, rhs, ncol(B), method = 'POI-C'))
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})
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fit <- with(dataset(1), {
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with(GEP(X, y, 'lda'), POI(lhs, rhs, ncol(B), method = 'FastPOI-C'))
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})
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fit <- with(dataset(1), {
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with(GEP(X, y, 'lda'), POI(lhs, rhs, ncol(B), method = 'POI-C', use.C = TRUE))
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})
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fit <- with(dataset(1), {
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with(GEP(X, y, 'lda'), POI(lhs, rhs, ncol(B), method = 'FastPOI-C', use.C = TRUE))
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})
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# head(fit$vectors, 10)
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count <- 0
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nr.reps <- 100
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sim <- replicate(nr.reps, {
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res <- double(0)
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for (model.nr in 1:5) {
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with(dataset(model.nr), {
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for (method in c('POI-C', 'FastPOI-C')) {
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for (use.C in c(FALSE, TRUE)) {
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fit <- with(GEP(X, y, 'lda'), {
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POI(lhs, rhs, ncol(B), method = 'POI-C', use.C = use.C)
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})
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dist <- dist.projection(B, fit$vectors)
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names(dist) <- paste('M', model.nr, '-', method, '-', use.C)
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res <<- c(res, dist)
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}
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}
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fit <- with(GEP(X, y, 'lda'), {
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solve.gep(lhs, rhs, ncol(B))
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})
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dist <- dist.projection(B, fit$vectors)
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names(dist) <- paste('M', model.nr, '- solve -', use.C)
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res <<- c(res, dist)
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})
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}
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cat("Counter", (count <<- count + 1), "/", nr.reps, "\n")
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res
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})
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(stats <- as.matrix(rowMeans(sim)))
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