install.packages("cdcsis")This is a basic example which shows you how to pick out the important feature from high-dimensional dataset:
library(cdcsis)
set.seed(1)
num <- 100
p <- 1000
x <- matrix(rnorm(num * p), nrow = num, ncol = p)
z <- rnorm(num)
y <- 3*x[, 1] + 1.5*x[, 2] + 4*z*x[, 5] + rnorm(num)
res <- cdcsis(x, y, z)
head(res[["ix"]], n = 10)cdcsis function successfully selects the informative variables from 1000 features pool.
[1] 1 5 2 628 17 87 912 903 395 630RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4