#
# Function_Call
#1 lmm_sc_K<-biglmm(dat$y,Z=dat$Z,scale=TRUE,compute_K=FALSE,verbose=2)
#2 lmm_sc<-biglmm(dat$y,Z=dat$Z,scale=TRUE,compute_K=TRUE,verbose=2)
#3 lmm_K<-biglmm(dat$y,Z=dat$Zsc,scale=FALSE,compute_K=FALSE,verbose=2)
#4 lmm<-biglmm(dat$y,Z=dat$Zsc,scale=FALSE,compute_K=TRUE,verbose=2)
# Elapsed_Time_sec Total_RAM_Used_MiB Peak_RAM_Used_MiB
#1 16.458 0.1 1655.7
#2 14.759 0.1 1654.9
#3 17.135 0.0 548.9
#4 11.571 0.0 427.0
### inc
library(peakRAM)
library(bigstatsr)
### par
N <- 100e3 # individuals
M <- 500 # markers
h2 <- 0.8
### data simulation
sim_data <- function(N, M, h2)
{
Zg <- sapply(1:M, function(i) rbinom(N, 2, 0.5)) # allele freq. = 0.5
col_means <- colMeans(Zg, na.rm = TRUE)
col_freq <- col_means / 2 # col_means = 2 * col_freq
col_sd <- sqrt(2 * col_freq * (1 - col_freq))
Z <- sweep(Zg, 2, col_means, "-")
Z <- sweep(Z, 2, col_sd , "/")
b <- rnorm(M, 0, sqrt(h2/M))
y <- Z %*% b + rnorm(N, 0, sqrt(1 - h2))
Zsc <- Z / sqrt(M)
list(y = y, Z = as_FBM(Zg), Zsc = as_FBM(Zsc))
}
dat <- sim_data(N, M, h2)
### profiling
prof <- peakRAM(
lmm_sc_K <- biglmm(dat$y, Z = dat$Z, scale = TRUE, compute_K = FALSE, verbose = 2),
lmm_sc <- biglmm(dat$y, Z = dat$Z, scale = TRUE, compute_K = TRUE, verbose = 2),
lmm_K <- biglmm(dat$y, Z = dat$Zsc, scale = FALSE, compute_K = FALSE, verbose = 2),
lmm <- biglmm(dat$y, Z = dat$Zsc, scale = FALSE, compute_K = TRUE, verbose = 2)
)
print(prof)
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