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This is an example application to compare the accuracy and computational speed of DRR for different parameters to PCA.
library(DRR) set.seed(123)
data(iris) in_data <- iris[, 1:4] npoints <- nrow(in_data) nvars <- ncol(in_data) for (i in seq_len(nvars)) in_data[[i]] <- as.numeric(in_data[[i]]) my_data <- scale(in_data[sample(npoints), ], scale = FALSE)
t0 <- system.time(pca <- prcomp(my_data, center = FALSE, scale. = FALSE)) t1 <- system.time(drr.1 <- drr(my_data, verbose = FALSE)) t2 <- system.time(drr.2 <- drr(my_data, fastkrr = 2, verbose = FALSE)) t3 <- system.time(drr.3 <- drr(my_data, fastkrr = 5, verbose = FALSE)) t4 <- system.time(drr.4 <- drr(my_data, fastkrr = 2, fastcv = TRUE, verbose = FALSE))
pairs(my_data, gap = 0, main = "iris") pairs(pca$x, gap = 0, main = "pca") pairs(drr.1$fitted.data, gap = 0, main = "drr.1") pairs(drr.2$fitted.data, gap = 0, main = "drr.2") pairs(drr.3$fitted.data, gap = 0, main = "drr.3") pairs(drr.4$fitted.data, gap = 0, main = "drr.4")
rmse <- matrix(NA_real_, nrow = 5, ncol = nvars, dimnames = list(c("pca", "drr.1", "drr.2", "drr.3", "drr.4"), seq_len(nvars))) for (i in seq_len(nvars)){ pca_inv <- pca$x[, 1:i, drop = FALSE] %*% t(pca$rotation[, 1:i, drop = FALSE]) rmse["pca", i] <- sqrt( sum( ( my_data - pca_inv ) ^ 2 ) ) rmse["drr.1", i] <- sqrt( sum( ( my_data - drr.1$inverse(drr.1$fitted.data[, 1:i, drop = FALSE]) ) ^ 2 ) ) rmse["drr.2", i] <- sqrt( sum( ( my_data - drr.2$inverse(drr.2$fitted.data[, 1:i, drop = FALSE]) ) ^ 2) ) rmse["drr.3", i] <- sqrt( sum( ( my_data - drr.3$inverse(drr.3$fitted.data[, 1:i, drop = FALSE]) ) ^ 2) ) rmse["drr.4", i] <- sqrt( sum( ( my_data - drr.4$inverse(drr.4$fitted.data[, 1:i, drop = FALSE]) ) ^ 2) ) }
More blocks for fastkrr speed up calculation, too are bad for accuracy.
print(rmse)
print(rbind(pca = t0, drr.1 = t1, drr.2 = t2, drr.3 = t3, drr.4 = t4)[, 1:3])
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