Last updated: 2019-02-24
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | bc45647 | zouyuxin | 2019-02-24 | wflow_publish(“analysis/SusieZPerformanceRE3.Rmd”) |
DAP-z is not stable, so we exclude it from the simulation. It has two problems:
The algorithm doesn’t stop.
It uses more than 6GB memories.
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 use datasets from dsc-finemap, which is from GTExV8 genotypes. The genotype matrix X is 574 by 1001.
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.
In SuSiE z, we truncate the eigenvalue of R at \(10^{-3}\), instead of \(10^{-8}\). There are some failed cases. Problem 92
library(dscrutils)
dscout = dscquery('output/finemap_compare_small_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_susie.top score_susie.overlap ',group = c("score: score_susie score_finemap", "method: susie_z susie_z_init finemap"))
colnames(dscout) = c('DSC', 'method', 'output.file', 'pve', 'n_signal', 'effect_weight', 'score', 'objective', 'converged', 'total', 'valid', 'size', 'signal_pip', 'purity', 'top', 'overlap')
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)
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_small_data_signal_dscout.rds')
dscout.susie = dscout[dscout$method == 'susie_z',]
dscout.susie.init = dscout[dscout$method == 'susie_z_init',]
dscout.finemap = dscout[dscout$method == 'finemap',]success.susie = aggregate(converged~effect_weight+n_signal+pve, dscout.susie, sum)
times.susie = aggregate(DSC~effect_weight+n_signal+pve, dscout.susie, length)
success.times.susie = merge(times.susie, success.susie)
success.times.susie$Fail.susie = success.times.susie$DSC - success.times.susie$converged
colnames(success.times.susie)[colnames(success.times.susie) == 'DSC'] = 'times'
success.times.susie = success.times.susie[,-5]
success.susie.init = aggregate(converged~effect_weight+n_signal+pve, dscout.susie.init, sum)
times.susie.init = aggregate(DSC~effect_weight+n_signal+pve, dscout.susie.init, length)
success.times.susie.init = merge(times.susie, success.susie.init)
success.times.susie.init$Fail.susie.init = success.times.susie.init$DSC - success.times.susie.init$converged
colnames(success.times.susie.init)[colnames(success.times.susie.init) == 'DSC'] = 'times'
success.times.susie.init = success.times.susie.init[,-5]
success.times = Reduce(function(...) merge(...),
list(success.times.susie, success.times.susie.init))
success.times %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | times | Fail.susie | Fail.susie.init |
|---|---|---|---|---|---|
| equal | 1 | 0.05 | 150 | 0 | 0 |
| equal | 1 | 0.10 | 150 | 0 | 0 |
| equal | 1 | 0.20 | 150 | 0 | 0 |
| equal | 1 | 0.60 | 150 | 0 | 0 |
| equal | 1 | 0.80 | 150 | 2 | 0 |
| equal | 10 | 0.05 | 150 | 0 | 0 |
| equal | 10 | 0.10 | 150 | 0 | 0 |
| equal | 10 | 0.20 | 150 | 0 | 0 |
| equal | 10 | 0.60 | 150 | 0 | 0 |
| equal | 10 | 0.80 | 150 | 0 | 0 |
| equal | 3 | 0.05 | 150 | 0 | 0 |
| equal | 3 | 0.10 | 150 | 0 | 0 |
| equal | 3 | 0.20 | 150 | 0 | 0 |
| equal | 3 | 0.60 | 150 | 0 | 0 |
| equal | 3 | 0.80 | 150 | 0 | 0 |
| equal | 5 | 0.05 | 150 | 0 | 0 |
| equal | 5 | 0.10 | 150 | 0 | 0 |
| equal | 5 | 0.20 | 150 | 0 | 0 |
| equal | 5 | 0.60 | 150 | 0 | 0 |
| equal | 5 | 0.80 | 150 | 0 | 0 |
| notequal | 1 | 0.05 | 150 | 0 | 0 |
| notequal | 1 | 0.10 | 150 | 0 | 0 |
| notequal | 1 | 0.20 | 150 | 0 | 0 |
| notequal | 1 | 0.60 | 150 | 0 | 0 |
| notequal | 1 | 0.80 | 150 | 2 | 0 |
| notequal | 10 | 0.05 | 150 | 0 | 0 |
| notequal | 10 | 0.10 | 150 | 0 | 0 |
| notequal | 10 | 0.20 | 150 | 0 | 0 |
| notequal | 10 | 0.60 | 150 | 0 | 0 |
| notequal | 10 | 0.80 | 150 | 2 | 2 |
| notequal | 3 | 0.05 | 150 | 0 | 0 |
| notequal | 3 | 0.10 | 150 | 0 | 0 |
| notequal | 3 | 0.20 | 150 | 0 | 0 |
| notequal | 3 | 0.60 | 150 | 1 | 1 |
| notequal | 3 | 0.80 | 150 | 2 | 1 |
| notequal | 5 | 0.05 | 150 | 0 | 0 |
| notequal | 5 | 0.10 | 150 | 0 | 0 |
| notequal | 5 | 0.20 | 150 | 0 | 0 |
| notequal | 5 | 0.60 | 150 | 1 | 1 |
| notequal | 5 | 0.80 | 150 | 3 | 3 |
size.susie = aggregate(size~effect_weight+n_signal+pve, dscout.susie, FUN = function(x) round(mean(x), 2))
colnames(size.susie)[colnames(size.susie) == 'size'] <- 'size.susie'
size.susie.init = aggregate(size~effect_weight+n_signal+pve, dscout.susie.init, FUN = function(x) round(mean(x), 2))
colnames(size.susie.init)[colnames(size.susie.init) == 'size'] <- 'size.susie.init'
size.finemap = aggregate(size~effect_weight+n_signal+pve, dscout.finemap, FUN = function(x) round(mean(x), 2))
colnames(size.finemap)[colnames(size.finemap) == 'size'] <- 'size.finemap'
size = Reduce(function(...) merge(...),
list(size.susie, size.susie.init, 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.finemap |
|---|---|---|---|---|---|
| equal | 1 | 0.05 | 7.84 | 9.11 | 998.07 |
| equal | 1 | 0.10 | 6.12 | 6.52 | 992.40 |
| equal | 1 | 0.20 | 11.11 | 10.75 | 968.32 |
| equal | 1 | 0.60 | 17.34 | 16.99 | 942.25 |
| equal | 1 | 0.80 | 4.60 | 4.02 | 563.46 |
| equal | 10 | 0.05 | 12.06 | 12.37 | 996.23 |
| equal | 10 | 0.10 | 11.34 | 11.02 | 942.31 |
| equal | 10 | 0.20 | 14.03 | 13.64 | 681.95 |
| equal | 10 | 0.60 | 25.43 | 25.44 | 229.12 |
| equal | 10 | 0.80 | 18.72 | 18.59 | 147.27 |
| equal | 3 | 0.05 | 9.97 | 10.53 | 990.67 |
| equal | 3 | 0.10 | 7.77 | 9.41 | 905.46 |
| equal | 3 | 0.20 | 12.04 | 12.30 | 702.05 |
| equal | 3 | 0.60 | 20.63 | 20.94 | 427.31 |
| equal | 3 | 0.80 | 12.26 | 12.18 | 174.79 |
| equal | 5 | 0.05 | 12.20 | 12.19 | 992.99 |
| equal | 5 | 0.10 | 12.87 | 12.82 | 942.01 |
| equal | 5 | 0.20 | 11.99 | 12.01 | 580.85 |
| equal | 5 | 0.60 | 19.21 | 19.22 | 165.55 |
| equal | 5 | 0.80 | 18.91 | 18.78 | 99.75 |
| notequal | 1 | 0.05 | 7.84 | 9.11 | 998.08 |
| notequal | 1 | 0.10 | 6.12 | 6.52 | 992.31 |
| notequal | 1 | 0.20 | 11.11 | 10.75 | 967.49 |
| notequal | 1 | 0.60 | 17.34 | 16.99 | 941.80 |
| notequal | 1 | 0.80 | 4.60 | 4.02 | 565.49 |
| notequal | 10 | 0.05 | 9.04 | 8.87 | 992.35 |
| notequal | 10 | 0.10 | 9.44 | 8.79 | 967.02 |
| notequal | 10 | 0.20 | 14.33 | 14.41 | 902.51 |
| notequal | 10 | 0.60 | 19.50 | 19.50 | 746.36 |
| notequal | 10 | 0.80 | 12.47 | 12.25 | 664.63 |
| notequal | 3 | 0.05 | 12.83 | 14.16 | 993.11 |
| notequal | 3 | 0.10 | 11.06 | 12.86 | 972.49 |
| notequal | 3 | 0.20 | 15.63 | 15.77 | 885.79 |
| notequal | 3 | 0.60 | 22.00 | 22.05 | 712.27 |
| notequal | 3 | 0.80 | 12.33 | 12.07 | 548.23 |
| notequal | 5 | 0.05 | 10.44 | 10.91 | 994.36 |
| notequal | 5 | 0.10 | 9.64 | 9.62 | 979.18 |
| notequal | 5 | 0.20 | 12.39 | 12.28 | 927.49 |
| notequal | 5 | 0.60 | 19.27 | 19.27 | 855.17 |
| notequal | 5 | 0.80 | 13.61 | 13.61 | 644.88 |
purity.susie = aggregate(purity~effect_weight+n_signal+pve, dscout.susie, FUN = function(x) round(mean(x), 3))
colnames(purity.susie)[colnames(purity.susie) == 'purity'] <- 'purity.susie'
purity.susie.init = aggregate(purity~effect_weight+n_signal+pve, dscout.susie.init, FUN = function(x) round(mean(x), 3))
colnames(purity.susie.init)[colnames(purity.susie.init) == 'purity'] <- 'purity.susie.init'
purity = Reduce(function(...) merge(...),
list(purity.susie, purity.susie.init))
purity %>% kable() %>% kable_styling(bootstrap_options = c("striped", "condensed", "responsive"), full_width = F) | effect_weight | n_signal | pve | purity.susie | purity.susie.init |
|---|---|---|---|---|
| equal | 1 | 0.05 | 0.927 | 0.881 |
| equal | 1 | 0.10 | 0.968 | 0.970 |
| equal | 1 | 0.20 | 0.978 | 0.987 |
| equal | 1 | 0.60 | 0.906 | 0.914 |
| equal | 1 | 0.80 | 0.666 | 0.683 |
| equal | 10 | 0.05 | 0.769 | 0.760 |
| equal | 10 | 0.10 | 0.911 | 0.893 |
| equal | 10 | 0.20 | 0.947 | 0.952 |
| equal | 10 | 0.60 | 0.943 | 0.944 |
| equal | 10 | 0.80 | 0.893 | 0.892 |
| equal | 3 | 0.05 | 0.836 | 0.823 |
| equal | 3 | 0.10 | 0.939 | 0.931 |
| equal | 3 | 0.20 | 0.965 | 0.963 |
| equal | 3 | 0.60 | 0.947 | 0.946 |
| equal | 3 | 0.80 | 0.863 | 0.866 |
| equal | 5 | 0.05 | 0.787 | 0.786 |
| equal | 5 | 0.10 | 0.917 | 0.916 |
| equal | 5 | 0.20 | 0.959 | 0.959 |
| equal | 5 | 0.60 | 0.959 | 0.960 |
| equal | 5 | 0.80 | 0.905 | 0.904 |
| notequal | 1 | 0.05 | 0.927 | 0.881 |
| notequal | 1 | 0.10 | 0.968 | 0.970 |
| notequal | 1 | 0.20 | 0.978 | 0.987 |
| notequal | 1 | 0.60 | 0.906 | 0.914 |
| notequal | 1 | 0.80 | 0.666 | 0.683 |
| notequal | 10 | 0.05 | 0.937 | 0.939 |
| notequal | 10 | 0.10 | 0.949 | 0.960 |
| notequal | 10 | 0.20 | 0.953 | 0.956 |
| notequal | 10 | 0.60 | 0.955 | 0.956 |
| notequal | 10 | 0.80 | 0.870 | 0.871 |
| notequal | 3 | 0.05 | 0.918 | 0.917 |
| notequal | 3 | 0.10 | 0.941 | 0.943 |
| notequal | 3 | 0.20 | 0.955 | 0.951 |
| notequal | 3 | 0.60 | 0.941 | 0.940 |
| notequal | 3 | 0.80 | 0.828 | 0.832 |
| notequal | 5 | 0.05 | 0.913 | 0.913 |
| notequal | 5 | 0.10 | 0.951 | 0.951 |
| notequal | 5 | 0.20 | 0.945 | 0.946 |
| notequal | 5 | 0.60 | 0.944 | 0.944 |
| notequal | 5 | 0.80 | 0.833 | 0.833 |
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$total_true), 3)
colnames(power.susie.init)[colnames(power.susie.init) == 'valid'] <- 'valid.susie.init'
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(DSC ~ effect_weight + n_signal + pve, dscout.finemap, length)
total$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.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,"FINEMAP" = 2)) %>% column_spec(c(6, 8, 10), bold = T)| effect_weight | n_signal | pve | total_true | valid.susie | power.susie | valid.susie.init | power.susie.init | valid.finemap | power.finemap |
|---|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 150 | 110 | 0.733 | 110 | 0.733 | 150 | 1.000 |
| equal | 1 | 0.10 | 150 | 120 | 0.800 | 123 | 0.820 | 150 | 1.000 |
| equal | 1 | 0.20 | 150 | 141 | 0.940 | 141 | 0.940 | 150 | 1.000 |
| equal | 1 | 0.60 | 150 | 141 | 0.940 | 141 | 0.940 | 139 | 0.927 |
| equal | 1 | 0.80 | 150 | 101 | 0.673 | 104 | 0.693 | 140 | 0.933 |
| equal | 10 | 0.05 | 1500 | 100 | 0.067 | 105 | 0.070 | 1494 | 0.996 |
| equal | 10 | 0.10 | 1500 | 198 | 0.132 | 193 | 0.129 | 1455 | 0.970 |
| equal | 10 | 0.20 | 1500 | 327 | 0.218 | 396 | 0.264 | 1320 | 0.880 |
| equal | 10 | 0.60 | 1500 | 387 | 0.258 | 655 | 0.437 | 1104 | 0.736 |
| equal | 10 | 0.80 | 1500 | 350 | 0.233 | 573 | 0.382 | 1004 | 0.669 |
| equal | 3 | 0.05 | 450 | 125 | 0.278 | 111 | 0.247 | 449 | 0.998 |
| equal | 3 | 0.10 | 450 | 237 | 0.527 | 230 | 0.511 | 446 | 0.991 |
| equal | 3 | 0.20 | 450 | 307 | 0.682 | 311 | 0.691 | 446 | 0.991 |
| equal | 3 | 0.60 | 450 | 329 | 0.731 | 336 | 0.747 | 438 | 0.973 |
| equal | 3 | 0.80 | 450 | 304 | 0.676 | 311 | 0.691 | 412 | 0.916 |
| equal | 5 | 0.05 | 750 | 119 | 0.159 | 118 | 0.157 | 748 | 0.997 |
| equal | 5 | 0.10 | 750 | 277 | 0.369 | 275 | 0.367 | 743 | 0.991 |
| equal | 5 | 0.20 | 750 | 432 | 0.576 | 435 | 0.580 | 715 | 0.953 |
| equal | 5 | 0.60 | 750 | 512 | 0.683 | 513 | 0.684 | 686 | 0.915 |
| equal | 5 | 0.80 | 750 | 473 | 0.631 | 475 | 0.633 | 653 | 0.871 |
| notequal | 1 | 0.05 | 150 | 110 | 0.733 | 110 | 0.733 | 150 | 1.000 |
| notequal | 1 | 0.10 | 150 | 120 | 0.800 | 123 | 0.820 | 150 | 1.000 |
| notequal | 1 | 0.20 | 150 | 141 | 0.940 | 141 | 0.940 | 150 | 1.000 |
| notequal | 1 | 0.60 | 150 | 141 | 0.940 | 141 | 0.940 | 138 | 0.920 |
| notequal | 1 | 0.80 | 150 | 101 | 0.673 | 104 | 0.693 | 142 | 0.947 |
| notequal | 10 | 0.05 | 1500 | 137 | 0.091 | 139 | 0.093 | 1492 | 0.995 |
| notequal | 10 | 0.10 | 1500 | 163 | 0.109 | 159 | 0.106 | 1471 | 0.981 |
| notequal | 10 | 0.20 | 1500 | 203 | 0.135 | 211 | 0.141 | 1414 | 0.943 |
| notequal | 10 | 0.60 | 1500 | 194 | 0.129 | 199 | 0.133 | 1323 | 0.882 |
| notequal | 10 | 0.80 | 1500 | 156 | 0.104 | 154 | 0.103 | 1246 | 0.831 |
| notequal | 3 | 0.05 | 450 | 122 | 0.271 | 122 | 0.271 | 449 | 0.998 |
| notequal | 3 | 0.10 | 450 | 154 | 0.342 | 142 | 0.316 | 449 | 0.998 |
| notequal | 3 | 0.20 | 450 | 193 | 0.429 | 186 | 0.413 | 443 | 0.984 |
| notequal | 3 | 0.60 | 450 | 177 | 0.393 | 175 | 0.389 | 428 | 0.951 |
| notequal | 3 | 0.80 | 450 | 135 | 0.300 | 132 | 0.293 | 400 | 0.889 |
| notequal | 5 | 0.05 | 750 | 122 | 0.163 | 122 | 0.163 | 748 | 0.997 |
| notequal | 5 | 0.10 | 750 | 151 | 0.201 | 150 | 0.200 | 743 | 0.991 |
| notequal | 5 | 0.20 | 750 | 200 | 0.267 | 198 | 0.264 | 732 | 0.976 |
| notequal | 5 | 0.60 | 750 | 182 | 0.243 | 182 | 0.243 | 716 | 0.955 |
| notequal | 5 | 0.80 | 750 | 145 | 0.193 | 145 | 0.193 | 667 | 0.889 |
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$total, 3)
colnames(fdr.susie)[colnames(fdr.susie) == 'valid'] <- 'valid.susie'
fdr.susie = fdr.susie[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.susie.init, sum)
total = aggregate(total~ effect_weight + n_signal + pve, dscout.susie.init, sum)
fdr.susie.init = merge(valid, total)
fdr.susie.init$fdr.susie.init = round((fdr.susie.init$total - fdr.susie.init$valid)/fdr.susie.init$total, 3)
colnames(fdr.susie.init)[colnames(fdr.susie.init) == 'valid'] <- 'valid.susie.init'
fdr.susie.init = fdr.susie.init[,-5]
valid = aggregate(valid ~ effect_weight + n_signal + pve, dscout.finemap, sum)
total = aggregate(size ~ effect_weight + n_signal + pve, dscout.finemap, sum)
fdr.finemap = merge(valid, total)
fdr.finemap$fdr.finemap = round((fdr.finemap$size - fdr.finemap$valid)/fdr.finemap$size, 3)
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.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,"FINEMAP" = 2))| effect_weight | n_signal | pve | valid.susie | fdr.susie | valid.susie.init | fdr.susie.init | valid.finemap | fdr.finemap |
|---|---|---|---|---|---|---|---|---|
| equal | 1 | 0.05 | 110 | 0.534 | 110 | 0.231 | 150 | 0.999 |
| equal | 1 | 0.10 | 120 | 0.444 | 123 | 0.180 | 150 | 0.999 |
| equal | 1 | 0.20 | 141 | 0.217 | 141 | 0.060 | 150 | 0.999 |
| equal | 1 | 0.60 | 141 | 0.212 | 141 | 0.000 | 139 | 0.999 |
| equal | 1 | 0.80 | 101 | 0.409 | 104 | 0.000 | 140 | 0.998 |
| equal | 10 | 0.05 | 100 | 0.628 | 105 | 0.591 | 1494 | 0.990 |
| equal | 10 | 0.10 | 198 | 0.519 | 193 | 0.545 | 1455 | 0.990 |
| equal | 10 | 0.20 | 327 | 0.402 | 396 | 0.437 | 1320 | 0.987 |
| equal | 10 | 0.60 | 387 | 0.273 | 655 | 0.234 | 1104 | 0.968 |
| equal | 10 | 0.80 | 350 | 0.236 | 573 | 0.201 | 1004 | 0.955 |
| equal | 3 | 0.05 | 125 | 0.604 | 111 | 0.554 | 449 | 0.997 |
| equal | 3 | 0.10 | 237 | 0.441 | 230 | 0.361 | 446 | 0.997 |
| equal | 3 | 0.20 | 307 | 0.315 | 311 | 0.228 | 446 | 0.996 |
| equal | 3 | 0.60 | 329 | 0.167 | 336 | 0.123 | 438 | 0.993 |
| equal | 3 | 0.80 | 304 | 0.172 | 311 | 0.114 | 412 | 0.984 |
| equal | 5 | 0.05 | 119 | 0.584 | 118 | 0.586 | 748 | 0.995 |
| equal | 5 | 0.10 | 277 | 0.409 | 275 | 0.415 | 743 | 0.995 |
| equal | 5 | 0.20 | 432 | 0.281 | 435 | 0.280 | 715 | 0.992 |
| equal | 5 | 0.60 | 512 | 0.142 | 513 | 0.142 | 686 | 0.972 |
| equal | 5 | 0.80 | 473 | 0.131 | 475 | 0.128 | 653 | 0.956 |
| notequal | 1 | 0.05 | 110 | 0.534 | 110 | 0.231 | 150 | 0.999 |
| notequal | 1 | 0.10 | 120 | 0.444 | 123 | 0.180 | 150 | 0.999 |
| notequal | 1 | 0.20 | 141 | 0.217 | 141 | 0.060 | 150 | 0.999 |
| notequal | 1 | 0.60 | 141 | 0.212 | 141 | 0.000 | 138 | 0.999 |
| notequal | 1 | 0.80 | 101 | 0.409 | 104 | 0.000 | 142 | 0.998 |
| notequal | 10 | 0.05 | 137 | 0.477 | 139 | 0.469 | 1492 | 0.990 |
| notequal | 10 | 0.10 | 163 | 0.455 | 159 | 0.463 | 1471 | 0.990 |
| notequal | 10 | 0.20 | 203 | 0.316 | 211 | 0.310 | 1414 | 0.990 |
| notequal | 10 | 0.60 | 194 | 0.174 | 199 | 0.191 | 1323 | 0.988 |
| notequal | 10 | 0.80 | 156 | 0.235 | 154 | 0.312 | 1246 | 0.988 |
| notequal | 3 | 0.05 | 122 | 0.540 | 122 | 0.463 | 449 | 0.997 |
| notequal | 3 | 0.10 | 154 | 0.462 | 142 | 0.396 | 449 | 0.997 |
| notequal | 3 | 0.20 | 193 | 0.306 | 186 | 0.271 | 443 | 0.997 |
| notequal | 3 | 0.60 | 177 | 0.088 | 175 | 0.054 | 428 | 0.996 |
| notequal | 3 | 0.80 | 135 | 0.220 | 132 | 0.143 | 400 | 0.995 |
| notequal | 5 | 0.05 | 122 | 0.472 | 122 | 0.472 | 748 | 0.995 |
| notequal | 5 | 0.10 | 151 | 0.415 | 150 | 0.414 | 743 | 0.995 |
| notequal | 5 | 0.20 | 200 | 0.270 | 198 | 0.272 | 732 | 0.995 |
| notequal | 5 | 0.60 | 182 | 0.180 | 182 | 0.180 | 716 | 0.994 |
| notequal | 5 | 0.80 | 145 | 0.341 | 145 | 0.344 | 667 | 0.993 |
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