Last updated: 2019-02-21
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The credible sets information from SuSiE and DAP are comparable. The credible set contains one causal variable with at least 95% posterior probability. However, there is no credible set for signals in FINEMAP. We generate the credible set of causal signals as the union of the variables included in the smallest set of causal configurations that already covered 95% of the total posterior probability.
We randomly generated 1200 by 1000 matrix X, each entry is random from N(0,1).
We random generate 100 null y.
library(dscrutils)
dscout = dscquery('output/finemap_compare_random_data_null', target='method score_susie.converged score.total score.valid score.size score_susie.purity score_dap.avgr2',group = c("score: score_susie score_finemap score_dap", "method: susie_z susie_z_init finemap dap_z"))
colnames(dscout) = c('DSC', 'method', 'output.file', 'score', 'total', 'valid', 'size', 'converged', 'purity', 'avgr2')library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(knitr)
library(kableExtra)
library(susieR)
dscout = readRDS('output/finemap_compare_random_data_null_dscout.rds')
dscout.susie = dscout[dscout$method == 'susie_z',]
dscout.susie.init = dscout[dscout$method == 'susie_z_init',]
dscout.finemap = dscout[dscout$method == 'finemap',]
dscout.dap = dscout[dscout$method == 'dap_z',]total = aggregate(total ~ method, dscout, sum)
size = aggregate(size ~ method, dscout, sum)
res = merge(total, size)
res %>% kable() %>% kable_styling()| method | total | size |
|---|---|---|
| dap_z | 0 | 0e+00 |
| finemap | 100 | 1e+05 |
| susie_z | 0 | 0e+00 |
| susie_z_init | 0 | 0e+00 |
There are no false discoveries for SuSiE z and DAP. For FINEMAP, there is no posterior probability of zero causal. The posterior probability for each configuration is very small. The credible set contains all variables. One example for FINEMAP
How to summarize FINEMAP result?
We simulate a gaussian y under various number of causal variables, total percentage of variance explained (PVE) and whether the signals have equal effect. The reason I control the effect size is that if we random generate the effect size for the signals, some signals have large effect size by chance. Therefore these signals have larger PVE.
We fit SuSiE with L = 5, FINEMAP with max 5 causals.
library(dscrutils)
dscout = dscquery('output/finemap_compare_random_data_signal', target='method sim_gaussian.pve sim_gaussian.n_signal sim_gaussian.effect_weight score_susie.objective score_susie.converged score.total score.valid score.size score.signal_pip score_susie.purity score_dap.avgr2 score_susie.top score_dap.top score_susie.overlap score_dap.overlap ',group = c("score: score_susie score_finemap score_dap", "method: susie_z susie_z_init finemap dap_z"))
colnames(dscout) = c('DSC', 'method', 'output.file', 'pve', 'n_signal', 'effect_weight', 'score', 'total', 'valid', 'size', 'signal_pip', 'objective', 'converged', 'purity', 'top', 'overlap', 'avgr2')
dscout$effect_weight[which(dscout$effect_weight == 'rep(1/n_signal, n_signal)')] = 'equal'
dscout$effect_weight[which(dscout$effect_weight != 'equal')] = 'notequal'library(dplyr)
library(knitr)
library(kableExtra)
library(susieR)
dscout = readRDS('output/finemap_compare_random_data_signal_dscout.rds')
dscout.susie = dscout[dscout$method == 'susie_z',]
dscout.susie.init = dscout[dscout$method == 'susie_z_init',]
dscout.finemap = dscout[dscout$method == 'finemap',]
dscout.dap = dscout[dscout$method == 'dap_z',]SuSiE and DAP performs similarly.
size.susie = aggregate(size~effect_weight+n_signal+pve, dscout.susie, mean)
colnames(size.susie)[colnames(size.susie) == 'size'] <- 'size.susie'
size.susie.init = aggregate(size~effect_weight+n_signal+pve, dscout.susie.init, mean)
colnames(size.susie.init)[colnames(size.susie.init) == 'size'] <- 'size.susie.init'
size.finemap = aggregate(size~effect_weight+n_signal+pve, dscout.finemap, mean)
colnames(size.finemap)[colnames(size.finemap) == 'size'] <- 'size.finemap'
size.dap = aggregate(size~pve+n_signal+effect_weight, dscout.dap, mean)
colnames(size.dap)[colnames(size.dap) == 'size'] <- 'size.dap'
size = Reduce(function(...) merge(...),
list(size.susie, size.susie.init, size.dap, size.finemap))
size %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | size.susie | size.susie.init | size.dap | size.finemap |
|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 1.00 | 1.00 | 1.00 | 1000.00 |
| equal | 1 | 0.10 | 1.00 | 1.00 | 1.00 | 1000.00 |
| equal | 1 | 0.20 | 1.00 | 1.00 | 1.00 | 998.30 |
| equal | 1 | 0.60 | 1.00 | 1.00 | 1.00 | 907.18 |
| equal | 1 | 0.80 | 1.00 | 1.00 | 1.00 | 941.55 |
| equal | 10 | 0.05 | 0.13 | 0.13 | 0.06 | 1000.00 |
| equal | 10 | 0.10 | 0.62 | 0.65 | 0.53 | 1000.00 |
| equal | 10 | 0.20 | 1.00 | 1.00 | 1.00 | 569.36 |
| equal | 10 | 0.60 | 1.00 | 1.00 | 1.00 | 7.57 |
| equal | 10 | 0.80 | 1.00 | 1.00 | 1.00 | 7.19 |
| equal | 3 | 0.05 | 0.76 | 0.75 | 0.73 | 1000.00 |
| equal | 3 | 0.10 | 0.99 | 0.99 | 0.99 | 1000.00 |
| equal | 3 | 0.20 | 1.00 | 1.00 | 1.00 | 999.34 |
| equal | 3 | 0.60 | 1.00 | 1.00 | 1.00 | 908.26 |
| equal | 3 | 0.80 | 1.00 | 1.00 | 1.00 | 885.09 |
| equal | 5 | 0.05 | 0.51 | 0.51 | 0.40 | 1000.00 |
| equal | 5 | 0.10 | 0.97 | 0.96 | 0.99 | 977.18 |
| equal | 5 | 0.20 | 1.00 | 1.00 | 1.00 | 43.75 |
| equal | 5 | 0.60 | 1.00 | 1.00 | 1.00 | 5.00 |
| equal | 5 | 0.80 | 1.00 | 1.00 | 1.00 | 5.00 |
| notequal | 1 | 0.05 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 1 | 0.10 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 1 | 0.20 | 1.00 | 1.00 | 1.00 | 998.47 |
| notequal | 1 | 0.60 | 1.00 | 1.00 | 1.00 | 907.11 |
| notequal | 1 | 0.80 | 1.00 | 1.00 | 1.00 | 941.62 |
| notequal | 10 | 0.05 | 0.99 | 0.99 | 0.98 | 1000.00 |
| notequal | 10 | 0.10 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 10 | 0.20 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 10 | 0.60 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 10 | 0.80 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 3 | 0.05 | 0.99 | 0.99 | 0.98 | 1000.00 |
| notequal | 3 | 0.10 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 3 | 0.20 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 3 | 0.60 | 1.00 | 1.00 | 1.00 | 907.02 |
| notequal | 3 | 0.80 | 1.00 | 1.00 | 1.00 | 890.33 |
| notequal | 5 | 0.05 | 1.00 | 1.00 | 0.99 | 1000.00 |
| notequal | 5 | 0.10 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 5 | 0.20 | 1.00 | 1.00 | 1.00 | 1000.00 |
| notequal | 5 | 0.60 | 1.00 | 1.00 | 1.00 | 846.83 |
| notequal | 5 | 0.80 | 1.00 | 1.00 | 1.00 | 101.83 |
purity.susie = aggregate(purity~effect_weight+n_signal+pve, dscout.susie, mean)
colnames(purity.susie)[colnames(purity.susie) == 'purity'] <- 'purity.susie'
purity.susie.init = aggregate(purity~effect_weight+n_signal+pve, dscout.susie.init, mean)
colnames(purity.susie.init)[colnames(purity.susie.init) == 'purity'] <- 'purity.susie.init'
purity.dap = aggregate(avgr2~effect_weight+n_signal+pve, dscout.dap, mean)
colnames(purity.dap)[colnames(purity.dap) == 'avgr2'] <- 'avgr2.dap'
purity = Reduce(function(...) merge(...),
list(purity.susie, purity.susie.init, purity.dap))
purity %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | purity.susie | purity.susie.init | avgr2.dap |
|---|---|---|---|---|---|
| equal | 1 | 0.05 | 1.00 | 1.00 | 1.00 |
| equal | 1 | 0.10 | 1.00 | 1.00 | 1.00 |
| equal | 1 | 0.20 | 1.00 | 1.00 | 1.00 |
| equal | 1 | 0.60 | 1.00 | 1.00 | 1.00 |
| equal | 1 | 0.80 | 1.00 | 1.00 | 1.00 |
| equal | 10 | 0.05 | 0.13 | 0.13 | 0.06 |
| equal | 10 | 0.10 | 0.62 | 0.65 | 0.53 |
| equal | 10 | 0.20 | 1.00 | 1.00 | 1.00 |
| equal | 10 | 0.60 | 1.00 | 1.00 | 1.00 |
| equal | 10 | 0.80 | 1.00 | 1.00 | 1.00 |
| equal | 3 | 0.05 | 0.76 | 0.75 | 0.73 |
| equal | 3 | 0.10 | 0.99 | 0.99 | 0.99 |
| equal | 3 | 0.20 | 1.00 | 1.00 | 1.00 |
| equal | 3 | 0.60 | 1.00 | 1.00 | 1.00 |
| equal | 3 | 0.80 | 1.00 | 1.00 | 1.00 |
| equal | 5 | 0.05 | 0.51 | 0.51 | 0.40 |
| equal | 5 | 0.10 | 0.97 | 0.96 | 0.99 |
| equal | 5 | 0.20 | 1.00 | 1.00 | 1.00 |
| equal | 5 | 0.60 | 1.00 | 1.00 | 1.00 |
| equal | 5 | 0.80 | 1.00 | 1.00 | 1.00 |
| notequal | 1 | 0.05 | 1.00 | 1.00 | 1.00 |
| notequal | 1 | 0.10 | 1.00 | 1.00 | 1.00 |
| notequal | 1 | 0.20 | 1.00 | 1.00 | 1.00 |
| notequal | 1 | 0.60 | 1.00 | 1.00 | 1.00 |
| notequal | 1 | 0.80 | 1.00 | 1.00 | 1.00 |
| notequal | 10 | 0.05 | 0.99 | 0.99 | 0.98 |
| notequal | 10 | 0.10 | 1.00 | 1.00 | 1.00 |
| notequal | 10 | 0.20 | 1.00 | 1.00 | 1.00 |
| notequal | 10 | 0.60 | 1.00 | 1.00 | 1.00 |
| notequal | 10 | 0.80 | 1.00 | 1.00 | 1.00 |
| notequal | 3 | 0.05 | 0.99 | 0.99 | 0.98 |
| notequal | 3 | 0.10 | 1.00 | 1.00 | 1.00 |
| notequal | 3 | 0.20 | 1.00 | 1.00 | 1.00 |
| notequal | 3 | 0.60 | 1.00 | 1.00 | 1.00 |
| notequal | 3 | 0.80 | 1.00 | 1.00 | 1.00 |
| notequal | 5 | 0.05 | 1.00 | 1.00 | 0.99 |
| notequal | 5 | 0.10 | 1.00 | 1.00 | 1.00 |
| notequal | 5 | 0.20 | 1.00 | 1.00 | 1.00 |
| notequal | 5 | 0.60 | 1.00 | 1.00 | 1.00 |
| notequal | 5 | 0.80 | 1.00 | 1.00 | 1.00 |
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie, sum)
total = aggregate(DSC~ effect_weight + n_signal + pve, dscout.susie, length)
total$total_true = total$DSC * total$n_signal
power.susie = merge(valid, total)
power.susie$power.susie = round(power.susie$valid/(power.susie$total_true), 3)
colnames(power.susie)[colnames(power.susie) == 'valid'] <- 'valid.susie'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie.init, sum)
total = aggregate(DSC~ effect_weight + n_signal + pve, dscout.susie.init, length)
total$total_true = total$DSC * total$n_signal
power.susie.init = merge(valid, total)
power.susie.init$power.susie.init = round(power.susie.init$valid/(power.susie.init$total_true), 3)
colnames(power.susie.init)[colnames(power.susie.init) == 'valid'] <- 'valid.susie.init'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.dap, sum)
total = aggregate(DSC~ effect_weight + n_signal + pve, dscout.dap, length)
total$total_true = total$DSC * total$n_signal
power.dap = merge(valid, total)
power.dap$power.dap = round(power.dap$valid/(power.dap$total_true), 3)
colnames(power.dap)[colnames(power.dap) == 'valid'] <- 'valid.dap'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(DSC ~ effect_weight + n_signal + pve, dscout.finemap, length)
total$total_true = total$DSC * total$n_signal
power.finemap = merge(valid, total)
power.finemap$power.finemap = round(power.finemap$valid/(power.finemap$total_true),3)
colnames(power.finemap)[colnames(power.finemap) == 'valid'] <- 'valid.finemap'
power = Reduce(function(...) merge(...),
list(power.susie, power.susie.init, power.dap, power.finemap))
power = power[,-4]
power %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", " "," ", "SuSiE z" = 2, "SuSiE z init" = 2,"DAP" = 2, "FINEMAP" = 2))| effect_weight | n_signal | pve | total_true | valid.susie | power.susie | valid.susie.init | power.susie.init | valid.dap | power.dap | valid.finemap | power.finemap |
|---|---|---|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| equal | 1 | 0.10 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| equal | 1 | 0.20 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| equal | 1 | 0.60 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| equal | 1 | 0.80 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| equal | 10 | 0.05 | 1000 | 13 | 0.013 | 13 | 0.013 | 6 | 0.006 | 1000 | 1.000 |
| equal | 10 | 0.10 | 1000 | 89 | 0.089 | 93 | 0.093 | 87 | 0.087 | 1000 | 1.000 |
| equal | 10 | 0.20 | 1000 | 361 | 0.361 | 716 | 0.716 | 639 | 0.639 | 989 | 0.989 |
| equal | 10 | 0.60 | 1000 | 423 | 0.423 | 1000 | 1.000 | 1000 | 1.000 | 757 | 0.757 |
| equal | 10 | 0.80 | 1000 | 439 | 0.439 | 1000 | 1.000 | 1000 | 1.000 | 719 | 0.719 |
| equal | 3 | 0.05 | 300 | 103 | 0.343 | 101 | 0.337 | 110 | 0.367 | 300 | 1.000 |
| equal | 3 | 0.10 | 300 | 284 | 0.947 | 284 | 0.947 | 279 | 0.930 | 300 | 1.000 |
| equal | 3 | 0.20 | 300 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 |
| equal | 3 | 0.60 | 300 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 |
| equal | 3 | 0.80 | 300 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 | 300 | 1.000 |
| equal | 5 | 0.05 | 500 | 68 | 0.136 | 68 | 0.136 | 54 | 0.108 | 500 | 1.000 |
| equal | 5 | 0.10 | 500 | 302 | 0.604 | 300 | 0.600 | 299 | 0.598 | 500 | 1.000 |
| equal | 5 | 0.20 | 500 | 496 | 0.992 | 496 | 0.992 | 495 | 0.990 | 500 | 1.000 |
| equal | 5 | 0.60 | 500 | 500 | 1.000 | 500 | 1.000 | 500 | 1.000 | 500 | 1.000 |
| equal | 5 | 0.80 | 500 | 500 | 1.000 | 500 | 1.000 | 500 | 1.000 | 500 | 1.000 |
| notequal | 1 | 0.05 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| notequal | 1 | 0.10 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| notequal | 1 | 0.20 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| notequal | 1 | 0.60 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| notequal | 1 | 0.80 | 100 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 | 100 | 1.000 |
| notequal | 10 | 0.05 | 1000 | 99 | 0.099 | 99 | 0.099 | 98 | 0.098 | 1000 | 1.000 |
| notequal | 10 | 0.10 | 1000 | 100 | 0.100 | 100 | 0.100 | 100 | 0.100 | 1000 | 1.000 |
| notequal | 10 | 0.20 | 1000 | 106 | 0.106 | 106 | 0.106 | 100 | 0.100 | 1000 | 1.000 |
| notequal | 10 | 0.60 | 1000 | 117 | 0.117 | 116 | 0.116 | 151 | 0.151 | 1000 | 1.000 |
| notequal | 10 | 0.80 | 1000 | 100 | 0.100 | 100 | 0.100 | 326 | 0.326 | 1000 | 1.000 |
| notequal | 3 | 0.05 | 300 | 99 | 0.330 | 99 | 0.330 | 98 | 0.327 | 300 | 1.000 |
| notequal | 3 | 0.10 | 300 | 106 | 0.353 | 106 | 0.353 | 101 | 0.337 | 300 | 1.000 |
| notequal | 3 | 0.20 | 300 | 187 | 0.623 | 186 | 0.620 | 152 | 0.507 | 300 | 1.000 |
| notequal | 3 | 0.60 | 300 | 296 | 0.987 | 298 | 0.993 | 300 | 1.000 | 300 | 1.000 |
| notequal | 3 | 0.80 | 300 | 267 | 0.890 | 267 | 0.890 | 300 | 1.000 | 300 | 1.000 |
| notequal | 5 | 0.05 | 500 | 100 | 0.200 | 100 | 0.200 | 99 | 0.198 | 500 | 1.000 |
| notequal | 5 | 0.10 | 500 | 104 | 0.208 | 104 | 0.208 | 101 | 0.202 | 500 | 1.000 |
| notequal | 5 | 0.20 | 500 | 131 | 0.262 | 131 | 0.262 | 112 | 0.224 | 500 | 1.000 |
| notequal | 5 | 0.60 | 500 | 310 | 0.620 | 315 | 0.630 | 404 | 0.808 | 500 | 1.000 |
| notequal | 5 | 0.80 | 500 | 127 | 0.254 | 126 | 0.252 | 483 | 0.966 | 500 | 1.000 |
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie, sum)
total = aggregate(total~ effect_weight + n_signal + pve, dscout.susie, sum)
fdr.susie = merge(valid, total)
fdr.susie$fdr.susie = round((fdr.susie$total - fdr.susie$valid)/fdr.susie$total, 4)
colnames(fdr.susie)[colnames(fdr.susie) == 'valid'] <- 'valid.susie'
fdr.susie = fdr.susie[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie.init, sum)
total = aggregate(total~ effect_weight + n_signal + pve, dscout.susie.init, sum)
fdr.susie.init = merge(valid, total)
fdr.susie.init$fdr.susie.init = round((fdr.susie.init$total - fdr.susie.init$valid)/fdr.susie.init$total, 4)
colnames(fdr.susie.init)[colnames(fdr.susie.init) == 'valid'] <- 'valid.susie.init'
fdr.susie.init = fdr.susie.init[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.dap, sum)
total = aggregate(total ~ effect_weight + n_signal + pve, dscout.dap, sum)
fdr.dap = merge(valid, total)
fdr.dap$fdr.dap = round((fdr.dap$total - fdr.dap$valid)/fdr.dap$total, 4)
colnames(fdr.dap)[colnames(fdr.dap) == 'valid'] <- 'valid.dap'
fdr.dap = fdr.dap[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(size ~ effect_weight + n_signal + pve, dscout.finemap, sum)
fdr.finemap = merge(valid, total)
fdr.finemap$fdr.finemap = round((fdr.finemap$size - fdr.finemap$valid)/fdr.finemap$size, 4)
colnames(fdr.finemap)[colnames(fdr.finemap) == 'valid'] <- 'valid.finemap'
fdr.finemap = fdr.finemap[,-5]
fdr = Reduce(function(...) merge(...),
list(fdr.susie, fdr.susie.init, fdr.dap, fdr.finemap))
fdr %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive","bordered"), full_width = F) %>% add_header_above(c(" ", " ", " ", "SuSiE z" = 2, "SuSiE z init" = 2,"DAP" = 2, "FINEMAP" = 2))| effect_weight | n_signal | pve | valid.susie | fdr.susie | valid.susie.init | fdr.susie.init | valid.dap | fdr.dap | valid.finemap | fdr.finemap |
|---|---|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| equal | 1 | 0.10 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| equal | 1 | 0.20 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| equal | 1 | 0.60 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9989 |
| equal | 1 | 0.80 | 100 | 0 | 100 | 0 | 100 | 0.0385 | 100 | 0.9989 |
| equal | 10 | 0.05 | 13 | 0 | 13 | 0 | 6 | 0.0000 | 1000 | 0.9900 |
| equal | 10 | 0.10 | 89 | 0 | 93 | 0 | 87 | 0.0000 | 1000 | 0.9900 |
| equal | 10 | 0.20 | 361 | 0 | 716 | 0 | 639 | 0.0000 | 989 | 0.9826 |
| equal | 10 | 0.60 | 423 | 0 | 1000 | 0 | 1000 | 0.0000 | 757 | 0.0000 |
| equal | 10 | 0.80 | 439 | 0 | 1000 | 0 | 1000 | 0.0000 | 719 | 0.0000 |
| equal | 3 | 0.05 | 103 | 0 | 101 | 0 | 110 | 0.0000 | 300 | 0.9970 |
| equal | 3 | 0.10 | 284 | 0 | 284 | 0 | 279 | 0.0000 | 300 | 0.9970 |
| equal | 3 | 0.20 | 300 | 0 | 300 | 0 | 300 | 0.0000 | 300 | 0.9970 |
| equal | 3 | 0.60 | 300 | 0 | 300 | 0 | 300 | 0.0000 | 300 | 0.9967 |
| equal | 3 | 0.80 | 300 | 0 | 300 | 0 | 300 | 0.0000 | 300 | 0.9966 |
| equal | 5 | 0.05 | 68 | 0 | 68 | 0 | 54 | 0.0000 | 500 | 0.9950 |
| equal | 5 | 0.10 | 302 | 0 | 300 | 0 | 299 | 0.0000 | 500 | 0.9949 |
| equal | 5 | 0.20 | 496 | 0 | 496 | 0 | 495 | 0.0000 | 500 | 0.8857 |
| equal | 5 | 0.60 | 500 | 0 | 500 | 0 | 500 | 0.0000 | 500 | 0.0000 |
| equal | 5 | 0.80 | 500 | 0 | 500 | 0 | 500 | 0.0000 | 500 | 0.0000 |
| notequal | 1 | 0.05 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| notequal | 1 | 0.10 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| notequal | 1 | 0.20 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9990 |
| notequal | 1 | 0.60 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 100 | 0.9989 |
| notequal | 1 | 0.80 | 100 | 0 | 100 | 0 | 100 | 0.0385 | 100 | 0.9989 |
| notequal | 10 | 0.05 | 99 | 0 | 99 | 0 | 98 | 0.0000 | 1000 | 0.9900 |
| notequal | 10 | 0.10 | 100 | 0 | 100 | 0 | 100 | 0.0000 | 1000 | 0.9900 |
| notequal | 10 | 0.20 | 106 | 0 | 106 | 0 | 100 | 0.0000 | 1000 | 0.9900 |
| notequal | 10 | 0.60 | 117 | 0 | 116 | 0 | 151 | 0.0000 | 1000 | 0.9900 |
| notequal | 10 | 0.80 | 100 | 0 | 100 | 0 | 326 | 0.0000 | 1000 | 0.9900 |
| notequal | 3 | 0.05 | 99 | 0 | 99 | 0 | 98 | 0.0000 | 300 | 0.9970 |
| notequal | 3 | 0.10 | 106 | 0 | 106 | 0 | 101 | 0.0000 | 300 | 0.9970 |
| notequal | 3 | 0.20 | 187 | 0 | 186 | 0 | 152 | 0.0000 | 300 | 0.9970 |
| notequal | 3 | 0.60 | 296 | 0 | 298 | 0 | 300 | 0.0000 | 300 | 0.9967 |
| notequal | 3 | 0.80 | 267 | 0 | 267 | 0 | 300 | 0.0000 | 300 | 0.9966 |
| notequal | 5 | 0.05 | 100 | 0 | 100 | 0 | 99 | 0.0000 | 500 | 0.9950 |
| notequal | 5 | 0.10 | 104 | 0 | 104 | 0 | 101 | 0.0000 | 500 | 0.9950 |
| notequal | 5 | 0.20 | 131 | 0 | 131 | 0 | 112 | 0.0000 | 500 | 0.9950 |
| notequal | 5 | 0.60 | 310 | 0 | 315 | 0 | 404 | 0.0000 | 500 | 0.9941 |
| notequal | 5 | 0.80 | 127 | 0 | 126 | 0 | 483 | 0.0000 | 500 | 0.9509 |
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.3
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] susieR_0.6.4.0438 kableExtra_1.0.1 knitr_1.20 dplyr_0.7.8
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 highr_0.7 compiler_3.5.1
[4] pillar_1.3.1 git2r_0.24.0 workflowr_1.1.1
[7] bindr_0.1.1 R.methodsS3_1.7.1 R.utils_2.7.0
[10] tools_3.5.1 digest_0.6.18 lattice_0.20-38
[13] evaluate_0.12 tibble_2.0.1 viridisLite_0.3.0
[16] pkgconfig_2.0.2 rlang_0.3.1 Matrix_1.2-15
[19] rstudioapi_0.9.0 yaml_2.2.0 bindrcpp_0.2.2
[22] stringr_1.3.1 httr_1.4.0 xml2_1.2.0
[25] hms_0.4.2 grid_3.5.1 webshot_0.5.1
[28] rprojroot_1.3-2 tidyselect_0.2.5 glue_1.3.0
[31] R6_2.3.0 rmarkdown_1.11 purrr_0.2.5
[34] readr_1.3.1 magrittr_1.5 whisker_0.3-2
[37] backports_1.1.3 scales_1.0.0 htmltools_0.3.6
[40] assertthat_0.2.0 rvest_0.3.2 colorspace_1.4-0
[43] stringi_1.2.4 munsell_0.5.0 crayon_1.3.4
[46] R.oo_1.22.0 This reproducible R Markdown analysis was created with workflowr 1.1.1