The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Comparing DRR and PCA

Guido Kraemer

2020-02-12

This is an example application to compare the accuracy and computational speed of DRR for different parameters to PCA.

Load libraries

library(DRR)
set.seed(123)

Read in data

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)

Fit the dimensionality reductions.

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))

Plot the data

Calculate RMSE

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))
}

The Results

More blocks for fastkrr speed up calculation, too are bad for accuracy.

RMSE

##              1        2        3            4
## pca   7.166770 3.899313 1.884524 1.379328e-14
## drr.1 5.549355 3.427558 1.700925 1.357570e-14
## drr.2 5.508291 3.193501 1.638346 1.358499e-14
## drr.3 5.555269 3.485765 1.734136 1.356369e-14
## drr.4 5.631050 2.302263 1.473392 1.353796e-14

Processing time

##       user.self sys.self elapsed
## pca       0.004    0.000   0.001
## drr.1    25.580   12.392  20.916
## drr.2    21.864   11.116  19.129
## drr.3    32.384   15.672  26.720
## drr.4    24.992    7.656  22.929

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.