Last updated: 2020-01-14
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 88668a4 | Peter Carbonetto | 2020-01-14 | wflow_publish(“susie_jrssb_example.Rmd”) |
| html | f73d1cb | Peter Carbonetto | 2020-01-14 | First build of the susie example. |
| Rmd | fd08150 | Peter Carbonetto | 2020-01-14 | wflow_publish(“susie_jrssb_example.Rmd”) |
| Rmd | 781b68e | Peter Carbonetto | 2020-01-14 | Added/modified workflowr files. |
| Rmd | 43ad1c3 | Peter Carbonetto | 2020-01-14 | Added workflowr files. |
Add text here.
The mvtnorm package is used to simulate the data.
library(mvtnorm)
library(varbvs)
library(susieR)One of the referees was interested in understanding the behaviour of SuSiE in the following scenario.
There are 5 predictors, and they are all somewhat correlated with each other. The first two predictors are very correlated with each other, and the third and fourth predictors are also very strongly correlated. The fifth predictor is also more correlated with the others; the weakest correlates are between \((x1,x2)\) and $(x3,x4).
set.seed(1)
n <- 100
b <- c(1.1,0,-0.75,0,0)
S <- rbind(c( 1, 0.99, 0.5, 0.5, 0.8),
c(0.99, 1, 0.5, 0.5, 0.8),
c( 0.5, 0.5, 1, 0.99, 0.8),
c( 0.5, 0.5, 0.99, 1, 0.8),
c( 0.8, 0.8, 0.8, 0.8, 1))
X <- rmvnorm(n,sigma = S)
y <- c(X %*% b + 3*rnorm(n))Add text here.
for (i in 1:5) {
dat <- data.frame(cbind(X[,i],y))
names(dat) <- c("x","y")
cat(sprintf("x%d: %0.2e\n",i,
summary(lm(y ~ x,dat))$coefficients["x","Pr(>|t|)"]))
}
# x1: 1.29e-01
# x2: 7.35e-02
# x3: 6.17e-02
# x4: 8.29e-02
# x5: 8.71e-01
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.2
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] susieR_0.8.1.0564 varbvs_2.6-5 mvtnorm_1.0-11
#
# loaded via a namespace (and not attached):
# [1] Rcpp_1.0.3 knitr_1.26 whisker_0.4
# [4] magrittr_1.5 workflowr_1.6.0 lattice_0.20-38
# [7] R6_2.4.1 jpeg_0.1-8.1 rlang_0.4.2
# [10] nor1mix_1.3-0 stringr_1.4.0 tools_3.6.2
# [13] grid_3.6.2 xfun_0.11 png_0.1-7
# [16] latticeExtra_0.6-29 git2r_0.26.1 htmltools_0.4.0
# [19] yaml_2.2.0 digest_0.6.23 rprojroot_1.3-2
# [22] Matrix_1.2-18 RColorBrewer_1.1-2 later_1.0.0
# [25] promises_1.1.0 fs_1.3.1 glue_1.3.1
# [28] evaluate_0.14 rmarkdown_2.0 stringi_1.4.3
# [31] compiler_3.6.2 backports_1.1.5 httpuv_1.5.2