Last updated: 2019-04-30
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Knit directory: dsc-susie-z/
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The design matrix X are real human genotype data from GTEx project, the 150 data in dsc-finemap repo. We simulate under various number of causal variables (1,2) and total percentage of variance explained (0.1, 0.2). We set effect size of each causal variable to be equal. Using the summary statistics from univariate regression, we fit SuSiE model using in-sample/out-sample correlation matrix, and compare their results.
library(dscrutils)
library(tibble)Warning: package 'tibble' was built under R version 3.5.2library(kableExtra)dscout = dscquery('r_compare_add_z', targets = 'sim_gaussian sim_gaussian.pve sim_gaussian.n_signal sim_gaussian.meta data.N_in susie.maxL susie_bhat.L susie_bhat.ld_method susie_bhat_add_z.L susie_bhat_add_z.ld_method susie_rss.L susie_rss.ld_method susie_rss_add_z.L susie_rss_add_z.ld_method finemap.ld_method finemap_add_z.ld_method score_susie.total score_susie.valid score_susie.size score_susie.purity score_susie.top score_susie.converged score_susie.pip score_finemap.pip', omit.filenames = FALSE)
dscout.tibble = as_tibble(dscout)dscout = readRDS('output/r_compare_add_z_dscout_susie_finemap_tibble.rds')
dscout$method = rep(NA, nrow(dscout))
dscout$method[!is.na(dscout$susie.maxL)] = 'susie'
dscout$method[!is.na(dscout$susie_bhat.L)] = 'susie_b'
dscout$method[!is.na(dscout$susie_bhat_add_z.L)] = 'susie_b'
dscout$method[!is.na(dscout$susie_rss.L)] = 'susie_rss'
dscout$method[!is.na(dscout$susie_rss_add_z.L)] = 'susie_rss'
dscout$method[!is.na(dscout$finemap.ld_method)] = 'finemap'
dscout$method[!is.na(dscout$finemap_add_z.ld_method)] = 'finemap'
dscout$add_z = rep(FALSE, nrow(dscout))
dscout$add_z[!is.na(dscout$susie_bhat_add_z.L)] = TRUE
dscout$add_z[!is.na(dscout$susie_rss_add_z.L)] = TRUE
dscout$add_z[!is.na(dscout$finemap_add_z.ld_method)] = TRUE
dscout$ld_method = dscout$susie_bhat.ld_method
dscout$ld_method[!is.na(dscout$susie_bhat_add_z.ld_method)] = dscout$susie_bhat_add_z.ld_method[!is.na(dscout$susie_bhat_add_z.ld_method)]
dscout$ld_method[!is.na(dscout$susie_rss.ld_method)] = dscout$susie_rss.ld_method[!is.na(dscout$susie_rss.ld_method)]
dscout$ld_method[!is.na(dscout$susie_rss_add_z.ld_method)] = dscout$susie_rss_add_z.ld_method[!is.na(dscout$susie_rss_add_z.ld_method)]
dscout$ld_method[!is.na(dscout$finemap.ld_method)] = dscout$finemap.ld_method[!is.na(dscout$finemap.ld_method)]
dscout$ld_method[!is.na(dscout$finemap_add_z.ld_method)] = dscout$finemap_add_z.ld_method[!is.na(dscout$finemap_add_z.ld_method)]
dscout$L = dscout$susie.maxL
dscout$L[!is.na(dscout$susie_bhat.L)] = dscout$susie_bhat.L[!is.na(dscout$susie_bhat.L)]
dscout$L[!is.na(dscout$susie_bhat_add_z.L)] = dscout$susie_bhat_add_z.L[!is.na(dscout$susie_bhat_add_z.L)]
dscout$L[!is.na(dscout$susie_rss.L)] = dscout$susie_rss.L[!is.na(dscout$susie_rss.L)]
dscout$L[!is.na(dscout$susie_rss_add_z.L)] = dscout$susie_rss_add_z.L[!is.na(dscout$susie_rss_add_z.L)]
dscout = dscout[,-c(6,8:18)]
colnames(dscout) = c('DSC', 'filename','pve', 'n_signal', 'meta','N_in', 'total', 'valid', 'size', 'purity', 'top', 'converged', 'susie.pip','finemap.pip', 'method', 'add_z', 'ld_method', 'L')dscout.susie = dscout[dscout$method == 'susie',]
dscout.susierss = dscout[dscout$method == 'susie_rss',]
dscout.susieb = dscout[dscout$method == 'susie_b',]
dscout.finemap = dscout[dscout$method == 'finemap',]The model from susie all converge.
purity.susie.1 = round(aggregate(purity~pve+n_signal, dscout.susie[dscout.susie$L==1, ], mean), 3)
purity.susie.5 = round(aggregate(purity~pve+n_signal, dscout.susie[dscout.susie$L==5, ], mean), 3)
purity.susie = cbind(purity.susie.1, purity.susie.5)
purity.susie = purity.susie[,-c(4,5)]
colnames(purity.susie) = c('pve', 'n_signal', 'L = 1', 'L = 5')
purity.susie %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | L = 1 | L = 5 |
|---|---|---|---|
| 0.1 | 1 | 0.747 | 0.754 |
| 0.2 | 1 | 0.952 | 0.952 |
| 0.1 | 2 | 0.325 | 0.506 |
| 0.2 | 2 | 0.637 | 0.877 |
valid.1 = aggregate(valid ~ pve+n_signal, dscout.susie[dscout.susie$L==1, ], sum)
total.1 = aggregate(DSC~ pve+n_signal, dscout.susie[dscout.susie$L==1, ], length)
total.1$total_true = total.1$DSC * total.1$n_signal
power.susie.1 = merge(valid.1, total.1)
power.susie.1$power = round(power.susie.1$valid/(power.susie.1$total_true), 3)
power.susie.1 = power.susie.1[,-c(3,4,5)]
valid.5 = aggregate(valid ~ pve+n_signal, dscout.susie[dscout.susie$L==5, ], sum)
total.5 = aggregate(DSC~ pve+n_signal, dscout.susie[dscout.susie$L==5, ], length)
total.5$total_true = total.5$DSC * total.5$n_signal
power.susie.5 = merge(valid.5, total.5)
power.susie.5$power = round(power.susie.5$valid/(power.susie.5$total_true), 3)
power.susie.5 = power.susie.5[,-c(3,4,5)]
power.susie = cbind(power.susie.1, power.susie.5)
power.susie = power.susie[,-c(4,5)]
colnames(power.susie) = c('pve', 'n signal', 'L1_power', 'L5_power')
power.susie %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n signal | L1_power | L5_power |
|---|---|---|---|
| 0.1 | 1 | 0.860 | 0.867 |
| 0.1 | 2 | 0.140 | 0.313 |
| 0.2 | 1 | 0.993 | 0.993 |
| 0.2 | 2 | 0.240 | 0.707 |
valid.1 = aggregate(valid ~ pve+n_signal, dscout.susie[dscout.susie$L==1, ], sum)
total.1 = aggregate(total~ pve+n_signal, dscout.susie[dscout.susie$L==1, ], sum)
fdr.1 = merge(valid.1, total.1)
fdr.1$fdr.1 = round((fdr.1$total - fdr.1$valid)/fdr.1$total, 4)
fdr.1 = fdr.1[,-c(3,4)]
valid.5 = aggregate(valid ~ pve+n_signal, dscout.susie[dscout.susie$L==5, ], sum)
total.5 = aggregate(total~ pve+n_signal, dscout.susie[dscout.susie$L==5, ], sum)
fdr.5 = merge(valid.5, total.5)
fdr.5$fdr.5 = round((fdr.5$total - fdr.5$valid)/fdr.5$total, 4)
fdr.5 = fdr.5[,-c(3,4)]
fdr = Reduce(function(...) merge(...),
list(fdr.1, fdr.5))
colnames(fdr) = c( 'pve', 'n_signal', 'L = 1', 'L = 5')
fdr %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | L = 1 | L = 5 |
|---|---|---|---|
| 0.1 | 1 | 0.0077 | 0.0076 |
| 0.1 | 2 | 0.2759 | 0.1681 |
| 0.2 | 1 | 0.0067 | 0.0067 |
| 0.2 | 2 | 0.3208 | 0.1347 |
dscout.susieb.in_sample = dscout.susieb[dscout.susieb$ld_method == 'in_sample',]
dscout.susieb.out_sample = dscout.susieb[dscout.susieb$ld_method == 'out_sample',]
dscout.susieb.all = dscout.susieb[dscout.susieb$ld_method == 'all',]
dscout.susieb.all.addz = dscout.susieb.all[dscout.susieb.all$add_z == TRUE,]
dscout.susieb.out_sample.addz = dscout.susieb.out_sample[dscout.susieb.out_sample$add_z == TRUE,]
dscout.susieb.out_sample = dscout.susieb.out_sample[dscout.susieb.out_sample$add_z == FALSE,]
dscout.susieb.all = dscout.susieb.all[dscout.susieb.all$add_z == FALSE,]The model from susie_bhat all converge. But some cases with out-sample R failed (out of 600). The estimated residual variance becomes negative.
converge.summary = aggregate(converged ~ ld_method + add_z + L, dscout.susieb, sum)
converge.summary$Fail = 600 - converge.summary$converged
Fail = converge.summary[converge.summary$Fail!=0,]
Fail[,-4] %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | ld_method | add_z | L | Fail | |
|---|---|---|---|---|
| 8 | out_sample | FALSE | 5 | 6 |
| 10 | out_sample | TRUE | 5 | 2 |
purity.susieb.in_sample.5 = round(aggregate(purity~pve+n_signal, dscout.susieb.in_sample[dscout.susieb.in_sample$L==5,], mean), 3)
colnames(purity.susieb.in_sample.5)[colnames(purity.susieb.in_sample.5) == 'purity'] <- 'in_sample'
purity.susieb.out_sample.5 = round(aggregate(purity~pve+n_signal, dscout.susieb.out_sample[as.logical((!is.na(dscout.susieb.out_sample$converged)) * (dscout.susieb.out_sample$L==5)),], mean), 3)
colnames(purity.susieb.out_sample.5)[colnames(purity.susieb.out_sample.5) == 'purity'] <- 'out_sample'
# purity.susieb.all.5 = round(aggregate(purity~pve+n_signal, dscout.susieb.all[as.logical((!is.na(dscout.susieb.all$converged)) * (dscout.susieb.all$L==5)),], mean), 3)
# colnames(purity.susieb.all.5)[colnames(purity.susieb.all.5) == 'purity'] <- 'all'
purity.susieb.out_sample.addz.5 = round(aggregate(purity~pve+n_signal, dscout.susieb.out_sample.addz[as.logical((!is.na(dscout.susieb.out_sample.addz$converged)) * (dscout.susieb.out_sample.addz$L==5)),], mean), 3)
colnames(purity.susieb.out_sample.addz.5)[colnames(purity.susieb.out_sample.addz.5) == 'purity'] <- 'out_sample.addz'
# purity.susieb.all.addz.5 = round(aggregate(purity~pve+n_signal, dscout.susieb.all.addz[as.logical((!is.na(dscout.susieb.all.addz$converged)) * (dscout.susieb.all.addz$L==5)),], mean), 3)
# colnames(purity.susieb.all.addz.5)[colnames(purity.susieb.all.addz.5) == 'purity'] <- 'all.addz'
purity.susieb.5 = cbind(purity.susieb.in_sample.5, purity.susieb.out_sample.5, purity.susieb.out_sample.addz.5) #, purity.susieb.all.5, purity.susieb.all.addz.5)
purity.susieb.5 = purity.susieb.5[,-c(4,5,7,8)]
purity.susieb.5 %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.754 | 0.742 | 0.754 |
| 0.2 | 1 | 0.952 | 0.945 | 0.943 |
| 0.1 | 2 | 0.506 | 0.545 | 0.450 |
| 0.2 | 2 | 0.877 | 0.898 | 0.821 |
valid.in = aggregate(valid ~ pve+n_signal, dscout.susieb.in_sample[dscout.susieb.in_sample$L==5,], sum)
total.in = aggregate(DSC~ pve+n_signal, dscout.susieb.in_sample[dscout.susieb.in_sample$L==5,], length)
total.in$total_true = total.in$DSC * total.in$n_signal
power.susieb.in = merge(valid.in, total.in)
power.susieb.in$in_sample = round(power.susieb.in$valid/(power.susieb.in$total_true), 3)
power.susieb.in = power.susieb.in[,-c(3,4,5)]
valid.out = aggregate(valid ~ pve+n_signal, dscout.susieb.out_sample[as.logical((!is.na(dscout.susieb.out_sample$converged)) * (dscout.susieb.out_sample$L==5)),], sum)
total.out = aggregate(DSC~ pve+n_signal, dscout.susieb.out_sample[as.logical((!is.na(dscout.susieb.out_sample$converged)) * (dscout.susieb.out_sample$L==5)),], length)
total.out$total_true = total.out$DSC * total.out$n_signal
power.susieb.out = merge(valid.out, total.out)
power.susieb.out$out_sample = round(power.susieb.out$valid/(power.susieb.out$total_true), 3)
power.susieb.out = power.susieb.out[,-c(3,4,5)]
valid.out.addz = aggregate(valid ~ pve+n_signal, dscout.susieb.out_sample.addz[as.logical((!is.na(dscout.susieb.out_sample.addz$converged)) * (dscout.susieb.out_sample.addz$L==5)),], sum)
total.out.addz = aggregate(DSC~ pve+n_signal, dscout.susieb.out_sample.addz[as.logical((!is.na(dscout.susieb.out_sample.addz$converged)) * (dscout.susieb.out_sample.addz$L==5)),], length)
total.out.addz$total_true = total.out.addz$DSC * total.out.addz$n_signal
power.susieb.out.addz = merge(valid.out.addz, total.out.addz)
power.susieb.out.addz$out_sample.addz = round(power.susieb.out.addz$valid/(power.susieb.out.addz$total_true), 3)
power.susieb.out.addz = power.susieb.out.addz[,-c(3,4,5)]
# valid.all = aggregate(valid ~ pve+n_signal, dscout.susieb.all[as.logical((!is.na(dscout.susieb.all$converged)) * (dscout.susieb.all$L==5)),], sum)
# total.all = aggregate(DSC~ pve+n_signal, dscout.susieb.all[as.logical((!is.na(dscout.susieb.all$converged)) * (dscout.susieb.all$L==5)),], length)
# total.all$total_true = total.all$DSC * total.all$n_signal
# power.susieb.all = merge(valid.all, total.all)
# power.susieb.all$all_sample = round(power.susieb.all$valid/(power.susieb.all$total_true), 3)
# power.susieb.all = power.susieb.all[,-c(3,4,5)]
#
# valid.all.addz = aggregate(valid ~ pve+n_signal, dscout.susieb.all.addz[as.logical((!is.na(dscout.susieb.all.addz$converged)) * (dscout.susieb.all.addz$L==5)),], sum)
# total.all.addz = aggregate(DSC~ pve+n_signal, dscout.susieb.all.addz[as.logical((!is.na(dscout.susieb.all.addz$converged)) * (dscout.susieb.all.addz$L==5)),], length)
# total.all.addz$total_true = total.all.addz$DSC * total.all.addz$n_signal
# power.susieb.all.addz = merge(valid.all.addz, total.all.addz)
# power.susieb.all.addz$all_sample.addz = round(power.susieb.all.addz$valid/(power.susieb.all.addz$total_true), 3)
# power.susieb.all.addz = power.susieb.all.addz[,-c(3,4,5)]
power.susieb = cbind(power.susieb.in, power.susieb.out, power.susieb.out.addz) #, power.susieb.all, power.susieb.all.addz)
power.susieb = power.susieb[,-c(4,5,7,8)]
power.susieb %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.867 | 0.833 | 0.860 |
| 0.1 | 2 | 0.313 | 0.310 | 0.227 |
| 0.2 | 1 | 0.993 | 0.952 | 0.986 |
| 0.2 | 2 | 0.707 | 0.571 | 0.543 |
valid.in = aggregate(valid ~ pve+n_signal, dscout.susieb.in_sample[dscout.susieb.in_sample$L==5,], sum)
total.in = aggregate(total~ pve+n_signal, dscout.susieb.in_sample[dscout.susieb.in_sample$L==5,], sum)
fdr.in = merge(valid.in, total.in)
fdr.in$in_sample = round((fdr.in$total - fdr.in$valid)/fdr.in$total, 4)
fdr.in = fdr.in[,-c(3,4)]
valid.out = aggregate(valid ~ pve+n_signal, dscout.susieb.out_sample[as.logical((!is.na(dscout.susieb.out_sample$converged)) * (dscout.susieb.out_sample$L==5)),], sum)
total.out = aggregate(total~ pve+n_signal, dscout.susieb.out_sample[as.logical((!is.na(dscout.susieb.out_sample$converged)) * (dscout.susieb.out_sample$L==5)),], sum)
fdr.out = merge(valid.out, total.out)
fdr.out$out_sample = round((fdr.out$total - fdr.out$valid)/fdr.out$total, 4)
fdr.out = fdr.out[,-c(3,4)]
valid.out.addz = aggregate(valid ~ pve+n_signal, dscout.susieb.out_sample.addz[as.logical((!is.na(dscout.susieb.out_sample.addz$converged)) * (dscout.susieb.out_sample.addz$L==5)),], sum)
total.out.addz = aggregate(total~ pve+n_signal, dscout.susieb.out_sample.addz[as.logical((!is.na(dscout.susieb.out_sample.addz$converged)) * (dscout.susieb.out_sample.addz$L==5)),], sum)
fdr.out.addz = merge(valid.out.addz, total.out.addz)
fdr.out.addz$out_sample.addz = round((fdr.out.addz$total - fdr.out.addz$valid)/fdr.out.addz$total, 4)
fdr.out.addz = fdr.out.addz[,-c(3,4)]
# valid.all = aggregate(valid ~ pve+n_signal, dscout.susieb.all[as.logical((!is.na(dscout.susieb.all$converged)) * (dscout.susieb.all$L==5)),], sum)
# total.all = aggregate(total~ pve+n_signal, dscout.susieb.all[as.logical((!is.na(dscout.susieb.all$converged)) * (dscout.susieb.all$L==5)),], sum)
# fdr.all = merge(valid.all, total.all)
# fdr.all$all = round((fdr.all$total - fdr.all$valid)/fdr.all$total, 4)
# fdr.all = fdr.all[,-c(3,4)]
#
# valid.all.addz = aggregate(valid ~ pve+n_signal, dscout.susieb.all.addz[as.logical((!is.na(dscout.susieb.all.addz$converged)) * (dscout.susieb.all.addz$L==5)),], sum)
# total.all.addz = aggregate(total~ pve+n_signal, dscout.susieb.all.addz[as.logical((!is.na(dscout.susieb.all.addz$converged)) * (dscout.susieb.all.addz$L==5)),], sum)
# fdr.all.addz = merge(valid.all.addz, total.all.addz)
# fdr.all.addz$all.addz = round((fdr.all.addz$total - fdr.all.addz$valid)/fdr.all.addz$total, 4)
# fdr.all.addz = fdr.all.addz[,-c(3,4)]
fdr.susieb = cbind(fdr.in, fdr.out, fdr.out.addz) #, fdr.all, fdr.all.addz)
fdr.susieb = fdr.susieb[,-c(4,5,7,8)]
fdr.susieb %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.0076 | 0.1259 | 0.0515 |
| 0.1 | 2 | 0.1681 | 0.3758 | 0.2766 |
| 0.2 | 1 | 0.0067 | 0.3694 | 0.0988 |
| 0.2 | 2 | 0.1347 | 0.4766 | 0.2756 |
dscout.susierss.in_sample = dscout.susierss[dscout.susierss$ld_method == 'in_sample',]
dscout.susierss.out_sample = dscout.susierss[dscout.susierss$ld_method == 'out_sample',]
dscout.susierss.all = dscout.susierss[dscout.susierss$ld_method == 'all',]
dscout.susierss.all.addz = dscout.susierss.all[dscout.susierss.all$add_z == TRUE,]
dscout.susierss.out_sample.addz = dscout.susierss.out_sample[dscout.susierss.out_sample$add_z == TRUE,]
dscout.susierss.out_sample = dscout.susierss.out_sample[dscout.susierss.out_sample$add_z == FALSE,]
dscout.susierss.all = dscout.susierss.all[dscout.susierss.all$add_z == FALSE,]The model from susie_rss all converge.
purity.susierss.in_sample.5 = round(aggregate(purity~pve+n_signal, dscout.susierss.in_sample[dscout.susierss.in_sample$L==5,], mean), 3)
colnames(purity.susierss.in_sample.5)[colnames(purity.susierss.in_sample.5) == 'purity'] <- 'in_sample'
purity.susierss.out_sample.5 = round(aggregate(purity~pve+n_signal, dscout.susierss.out_sample[as.logical((!is.na(dscout.susierss.out_sample$converged)) * (dscout.susierss.out_sample$L==5)),], mean), 3)
colnames(purity.susierss.out_sample.5)[colnames(purity.susierss.out_sample.5) == 'purity'] <- 'out_sample'
# purity.susierss.all.5 = round(aggregate(purity~pve+n_signal, dscout.susierss.all[dscout.susierss.all$L==5,], mean), 3)
# colnames(purity.susierss.all.5)[colnames(purity.susierss.all.5) == 'purity'] <- 'all'
purity.susierss.out_sample.addz.5 = round(aggregate(purity~pve+n_signal, dscout.susierss.out_sample.addz[dscout.susierss.out_sample.addz$L==5,], mean), 3)
colnames(purity.susierss.out_sample.addz.5)[colnames(purity.susierss.out_sample.addz.5) == 'purity'] <- 'out_sample.addz'
# purity.susierss.all.addz.5 = round(aggregate(purity~pve+n_signal, dscout.susierss.all.addz[dscout.susierss.all.addz$L==5,], mean), 3)
# colnames(purity.susierss.all.addz.5)[colnames(purity.susierss.all.addz.5) == 'purity'] <- 'all.addz'
purity.susierss.5 = cbind(purity.susierss.in_sample.5, purity.susierss.out_sample.5, purity.susierss.out_sample.addz.5) #, purity.susierss.all.5, purity.susierss.all.addz.5)
purity.susierss.5 = purity.susierss.5[,-c(4,5,7,8)]
purity.susierss.5 %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.760 | 0.701 | 0.760 |
| 0.2 | 1 | 0.954 | 0.937 | 0.945 |
| 0.1 | 2 | 0.512 | 0.493 | 0.436 |
| 0.2 | 2 | 0.878 | 0.849 | 0.813 |
valid.in = aggregate(valid ~ pve+n_signal, dscout.susierss.in_sample[dscout.susierss.in_sample$L==5,], sum)
total.in = aggregate(DSC~ pve+n_signal, dscout.susierss.in_sample[dscout.susierss.in_sample$L==5,], length)
total.in$total_true = total.in$DSC * total.in$n_signal
power.susierss.in = merge(valid.in, total.in)
power.susierss.in$in_sample = round(power.susierss.in$valid/(power.susierss.in$total_true), 3)
power.susierss.in = power.susierss.in[,-c(3,4,5)]
valid.out = aggregate(valid ~ pve+n_signal, dscout.susierss.out_sample[dscout.susierss.out_sample$L==5,], sum)
total.out = aggregate(DSC~ pve+n_signal, dscout.susierss.out_sample[dscout.susierss.out_sample$L==5,], length)
total.out$total_true = total.out$DSC * total.out$n_signal
power.susierss.out = merge(valid.out, total.out)
power.susierss.out$out_sample = round(power.susierss.out$valid/(power.susierss.out$total_true), 3)
power.susierss.out = power.susierss.out[,-c(3,4,5)]
valid.out.addz = aggregate(valid ~ pve+n_signal, dscout.susierss.out_sample.addz[dscout.susierss.out_sample.addz$L==5,], sum)
total.out.addz = aggregate(DSC~ pve+n_signal, dscout.susierss.out_sample.addz[dscout.susierss.out_sample.addz$L==5,], length)
total.out.addz$total_true = total.out.addz$DSC * total.out.addz$n_signal
power.susierss.out.addz = merge(valid.out.addz, total.out.addz)
power.susierss.out.addz$out_sample.addz = round(power.susierss.out.addz$valid/(power.susierss.out.addz$total_true), 3)
power.susierss.out.addz = power.susierss.out.addz[,-c(3,4,5)]
# valid.all = aggregate(valid ~ pve+n_signal, dscout.susierss.all[dscout.susierss.all$L==5,], sum)
# total.all = aggregate(DSC~ pve+n_signal, dscout.susierss.all[dscout.susierss.all$L==5,], length)
# total.all$total_true = total.all$DSC * total.all$n_signal
# power.susierss.all = merge(valid.all, total.all)
# power.susierss.all$all_sample = round(power.susierss.all$valid/(power.susierss.all$total_true), 3)
# power.susierss.all = power.susierss.all[,-c(3,4,5)]
#
# valid.all.addz = aggregate(valid ~ pve+n_signal, dscout.susierss.all.addz[dscout.susierss.all.addz$L==5,], sum)
# total.all.addz = aggregate(DSC~ pve+n_signal, dscout.susierss.all.addz[dscout.susierss.all.addz$L==5,], length)
# total.all.addz$total_true = total.all.addz$DSC * total.all.addz$n_signal
# power.susierss.all.addz = merge(valid.all.addz, total.all.addz)
# power.susierss.all.addz$all_sample.addz = round(power.susierss.all.addz$valid/(power.susierss.all.addz$total_true), 3)
# power.susierss.all.addz = power.susierss.all.addz[,-c(3,4,5)]
power.susierss = cbind(power.susierss.in, power.susierss.out, power.susierss.out.addz) #, power.susierss.all, power.susierss.all.addz)
power.susierss = power.susierss[,-c(4,5,7,8)]
power.susierss %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.867 | 0.787 | 0.867 |
| 0.1 | 2 | 0.310 | 0.260 | 0.213 |
| 0.2 | 1 | 0.980 | 0.800 | 0.973 |
| 0.2 | 2 | 0.700 | 0.553 | 0.513 |
valid.in = aggregate(valid ~ pve+n_signal, dscout.susierss.in_sample[dscout.susierss.in_sample$L==5,], sum)
total.in = aggregate(total~ pve+n_signal, dscout.susierss.in_sample[dscout.susierss.in_sample$L==5,], sum)
fdr.in = merge(valid.in, total.in)
fdr.in$in_sample = round((fdr.in$total - fdr.in$valid)/fdr.in$total, 4)
fdr.in = fdr.in[,-c(3,4)]
valid.out = aggregate(valid ~ pve+n_signal, dscout.susierss.out_sample[dscout.susierss.out_sample$L==5,], sum)
total.out = aggregate(total~ pve+n_signal, dscout.susierss.out_sample[dscout.susierss.out_sample$L==5,], sum)
fdr.out = merge(valid.out, total.out)
fdr.out$out_sample = round((fdr.out$total - fdr.out$valid)/fdr.out$total, 4)
fdr.out = fdr.out[,-c(3,4)]
valid.out.addz = aggregate(valid ~ pve+n_signal, dscout.susierss.out_sample.addz[dscout.susierss.out_sample.addz$L==5,], sum)
total.out.addz = aggregate(total~ pve+n_signal, dscout.susierss.out_sample.addz[dscout.susierss.out_sample.addz$L==5,], sum)
fdr.out.addz = merge(valid.out.addz, total.out.addz)
fdr.out.addz$out_sample.addz = round((fdr.out.addz$total - fdr.out.addz$valid)/fdr.out.addz$total, 4)
fdr.out.addz = fdr.out.addz[,-c(3,4)]
# valid.all = aggregate(valid ~ pve+n_signal, dscout.susierss.all[dscout.susierss.all$L==5,], sum)
# total.all = aggregate(total~ pve+n_signal, dscout.susierss.all[dscout.susierss.all$L==5,], sum)
# fdr.all = merge(valid.all, total.all)
# fdr.all$all = round((fdr.all$total - fdr.all$valid)/fdr.all$total, 4)
# fdr.all = fdr.all[,-c(3,4)]
#
# valid.all.addz = aggregate(valid ~ pve+n_signal, dscout.susierss.all.addz[dscout.susierss.all.addz$L==5,], sum)
# total.all.addz = aggregate(total~ pve+n_signal, dscout.susierss.all.addz[dscout.susierss.all.addz$L==5,], sum)
# fdr.all.addz = merge(valid.all.addz, total.all.addz)
# fdr.all.addz$all.addz = round((fdr.all.addz$total - fdr.all.addz$valid)/fdr.all.addz$total, 4)
# fdr.all.addz = fdr.all.addz[,-c(3,4)]
fdr.susierss = cbind(fdr.in, fdr.out, fdr.out.addz) #, fdr.all, fdr.all.addz)
fdr.susierss = fdr.susierss[,-c(4,5,7,8)]
fdr.susierss %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F)| pve | n_signal | in_sample | out_sample | out_sample.addz |
|---|---|---|---|---|
| 0.1 | 1 | 0.0076 | 0.0635 | 0.0441 |
| 0.1 | 2 | 0.1770 | 0.2844 | 0.2471 |
| 0.2 | 1 | 0.0200 | 0.2357 | 0.0875 |
| 0.2 | 2 | 0.1250 | 0.3112 | 0.2667 |
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] kableExtra_1.0.1 tibble_2.0.1 dscrutils_0.3.8
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 rstudioapi_0.9.0 xml2_1.2.0
[4] knitr_1.20 whisker_0.3-2 magrittr_1.5
[7] workflowr_1.3.0 hms_0.4.2 munsell_0.5.0
[10] rvest_0.3.2 viridisLite_0.3.0 colorspace_1.4-0
[13] R6_2.3.0 rlang_0.3.1 highr_0.7
[16] httr_1.4.0 stringr_1.3.1 tools_3.5.1
[19] webshot_0.5.1 git2r_0.24.0 htmltools_0.3.6
[22] yaml_2.2.0 rprojroot_1.3-2 digest_0.6.18
[25] crayon_1.3.4 readr_1.3.1 fs_1.2.6
[28] glue_1.3.0 evaluate_0.12 rmarkdown_1.11
[31] stringi_1.2.4 compiler_3.5.1 pillar_1.3.1
[34] scales_1.0.0 backports_1.1.3 pkgconfig_2.0.2