Tuning function for parameter c1 in sgcca
Wei Zhang
2023-04-12
This tuning function is inspired by the tuning function of PMACCA. The code can be found in: tuning-c1-for-sgcca The method is also compared with tuning parameter with cross validation
1 Load library
library(RGCCA)
library(MASS)
library(tidyverse)
library(purrr)2 Simulation two datasets with common latent variables
\[ \begin{aligned} X&=u w_1^{T}+\epsilon_1, \\ Y&=u w_2^{T}+\epsilon_2 \end{aligned} \]
set.seed(510)
sim.dat <- sim.linear2(n = 100, p = 150, psel = 30)$XWe selected the first 30 features as non-zero features.
3 Tuning function
set.seed(510)
system.time({
tune.sgcca <- sgcca_tune(sim.dat, scheme = "centroid", nperms = 10)
})## user system elapsed
## 13.952 2.163 3.284tune.sgccasgcca.m <- sgcca(sim.dat, c1 = rep(tune.sgcca$best_l1))Here we calculated the first canonical correlations with the tuned penalized parameter c1. We can see that most of the zero loadings are shrink to 0.
4 Using CV to tue c1
set.seed(510)
system.time({
cv.sgcca <- sgcca_cv(sim.dat, scheme = "centroid", K = 10)
})## user system elapsed
## 24.201 3.025 3.914print(cv.sgcca)## l1 cor
## 12 0.6816497 0.9768444sgcca.m <- sgcca(sim.dat, c1 = rep(cv.sgcca$l1))
5 Without tuning c1
sgcca.m2 <- sgcca(sim.dat)
get_corr(sgcca.m2$Y, scheme = "centroid")## comp1
## comp1 0.9794492par(mfrow = c(1,2))
plot(sgcca.m2$a$X1, ylab = "loading for X1", pch = 20, col = c(rep("red",30), rep("black",170)))
plot(sgcca.m2$a$X2, ylab = "loading for X2", pch = 20, col = c(rep("red",30), rep("black",170)))
6 Session Information
devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.1 (2022-06-23)
## os macOS Big Sur ... 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/New_York
## date 2023-04-12
## pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
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