In stephaniehicks/benchmarkfdrData2019: Data and Benchmarking Results from Korthauer and Kimes et al. (2019)
Summary
In this set of simulations, we consider settings with both null and non-null
tests with varying levels of informativeness of an informative covariate. The
covariate is sampled uniformly from the interval [0, 1], and the conditional
probability of a test being non-null is a smooth function of the covariate taking
values between 0 and 1 based on the logistic function.
For all settings, the marginal proportion of null hypotheses is set to 80%.
A tuning parameter is used to modify the informativeness of the covariate
such that 0 corresponds to a completely uninformative covariate, and increasing
"informativeness" corresponds to more non-null tests concentrating in a smaller
range of the covariate. The functional relationship between the covariate and
the null probability over varying informativeness is shown below.
Workspace Setup
library(dplyr)
library(ggplot2)
library(SummarizedBenchmark)
library(parallel)
## load helper functions
for (f in list.files("../R", "\\.(r|R)$", full.names = TRUE)) {
source(f)
}
## project data/results folders
resdir <- "results"
dir.create(resdir, showWarnings = FALSE, recursive = TRUE)
## intermediary files we create below
info00_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level00.rds")
info20_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level20.rds")
info40_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level40.rds")
info60_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level60.rds")
info80_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level80.rds")
info100_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level100.rds")
## number of cores for parallelization
cores <- 20
B <- 100
## define bechmarking design
bd <- initializeBenchDesign()
As described in simulations-null.Rmd, we include Scott's FDR Regression in the analysis
for simulations with Gaussian test statistics. Again, we include both
nulltype = "empirical" and nulltype = "theoretical". Since all settings in this
series of simulations use test statistics simulated with Gaussian test statistics, we include
Scott's FDR Regression in all of the comparisons.
bdplus <- bd
bdplus <- addBMethod(bdplus, "fdrreg-t",
FDRreg::FDRreg,
function(x) { x$FDR },
z = test_statistic,
features = model.matrix( ~ splines::bs(ind_covariate, df = 3) - 1),
nulltype = 'theoretical',
control = list(lambda = 0.01))
bdplus <- addBMethod(bdplus, "fdrreg-e",
FDRreg::FDRreg,
function(x) { x$FDR },
z = test_statistic,
features = model.matrix( ~ splines::bs(ind_covariate, df = 3) - 1),
nulltype = 'empirical',
control = list(lambda = 0.01))
All simulation settings will share the following parameters.
m <- 20000 # integer: number of hypothesis tests
es_dist <- rnorm_generator(2) # functional: dist of alternative test stats
ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats
null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values
icovariate <- runif # functional: independent covariate
Simulation results will be presented excluding a subset of methods, and
for certain plots (upset plots), a single alpha cutoff will be used.
excludeSet <- c("unadjusted", "bl-df02", "bl-df04", "bl-df05")
ualpha <- 0.05
First, we show the form of the relationship between the informative covariate and null
proportion, pi0, across varying levels of "informativeness."
xseq <- seq(0, 1, by = .01)
pi0trends <- lapply(seq(0, 1, by = .2), function(i) {
tibble(info = i, x = xseq,
pi0 = pi0_varyinfo80l(i)(xseq))
})
pi0trends <- bind_rows(pi0trends)
ggplot(pi0trends, aes(x = x, y = pi0)) +
geom_line() +
geom_text(aes(label = round(pi0, 3), hjust = x, vjust = round(pi0)),
dplyr::filter(pi0trends, x == 0 | x == 1),
size = 3, color = 'blue') +
theme_bw() +
scale_x_continuous("x (informative covariate)", breaks = 0:1) +
scale_y_continuous(labels = scales::percent) +
facet_grid(. ~ info, labeller = label_both) +
ggtitle("Covariate vs. pi0 for varying informativeness")
Informativeness = 0 Setting
First, we consider the setting where the informativeness is 0.
Data Simulation
pi0 <- pi0_varyinfo80l(0.00) # numeric: proportion of null hypotheses
seed <- 1010
We next run the simulations.
if (file.exists(info00_file)) {
res <- readRDS(info00_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info00_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 20 Setting
Next, we consider the setting where the informativeness is 0.20.
Data Simulation
pi0 <- pi0_varyinfo80l(0.20) # numeric: proportion of null hypotheses
seed <- 1201
We next run the simulations.
if (file.exists(info20_file)) {
res <- readRDS(info20_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info20_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 40 Setting
Next, we consider the setting where the informativeness is 0.40.
Data Simulation
pi0 <- pi0_varyinfo80l(0.40) # numeric: proportion of null hypotheses
seed <- 51
We next run the simulations.
if (file.exists(info40_file)) {
res <- readRDS(info40_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info40_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 60 Setting
Next, we consider the setting where the informativeness is 0.60.
Data Simulation
pi0 <- pi0_varyinfo80l(0.60) # numeric: proportion of null hypotheses
seed <- 500
We next run the simulations.
if (file.exists(info60_file)) {
res <- readRDS(info60_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info60_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 80 Setting
Next, we consider the setting where the informativeness is 0.80.
Data Simulation
pi0 <- pi0_varyinfo80l(0.80) # numeric: proportion of null hypotheses
seed <- 608
We next run the simulations.
if (file.exists(info80_file)) {
res <- readRDS(info80_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info80_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 100 Setting
Finally, we consider the setting where the informativeness is 1.00.
Data Simulation
pi0 <- pi0_varyinfo80l(1.00) # numeric: proportion of null hypotheses
seed <- 808
We next run the simulations.
if (file.exists(info100_file)) {
res <- readRDS(info100_file)
} else {
res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m,
pi0 = pi0, es_dist = es_dist, icovariate = icovariate,
ts_dist = ts_dist, null_dist = null_dist,
seed = seed, mc.cores = cores)
saveRDS(res, file = info100_file)
}
res_i <- lapply(res, `[[`, "informative")
res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a
single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist,
icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01))
plotsim_average(resdf, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
plotsim_average(resdf, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of
the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size")
covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) {
aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE)
} else {
message("Not enough methods found rejections at alpha ", ualpha,
"; skipping upset plot")
}
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01))
resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
select(resdfu, rep, blabel, param.alpha, key,
performanceMetric, alpha, value),
by = c("rep", "blabel", "param.alpha", "key",
"performanceMetric", "alpha"),
suffix = c(".info", ".uninfo"))
resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo)
plotsim_average(resdfiu, met="rejections", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="FDR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TPR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
plotsim_average(resdfiu, met="TNR", filter_set = excludeSet,
merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Session Info
sessionInfo()
stephaniehicks/benchmarkfdrData2019 documentation built on June 20, 2021, 10 a.m.
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Summary
In this set of simulations, we consider settings with both null and non-null tests with varying levels of informativeness of an informative covariate. The covariate is sampled uniformly from the interval [0, 1], and the conditional probability of a test being non-null is a smooth function of the covariate taking values between 0 and 1 based on the logistic function.
For all settings, the marginal proportion of null hypotheses is set to 80%. A tuning parameter is used to modify the informativeness of the covariate such that 0 corresponds to a completely uninformative covariate, and increasing "informativeness" corresponds to more non-null tests concentrating in a smaller range of the covariate. The functional relationship between the covariate and the null probability over varying informativeness is shown below.
Workspace Setup
library(dplyr) library(ggplot2) library(SummarizedBenchmark) library(parallel) ## load helper functions for (f in list.files("../R", "\\.(r|R)$", full.names = TRUE)) { source(f) } ## project data/results folders resdir <- "results" dir.create(resdir, showWarnings = FALSE, recursive = TRUE) ## intermediary files we create below info00_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level00.rds") info20_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level20.rds") info40_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level40.rds") info60_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level60.rds") info80_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level80.rds") info100_file <- file.path(resdir, "varyinginfo-smooth-benchmark-level100.rds") ## number of cores for parallelization cores <- 20 B <- 100 ## define bechmarking design bd <- initializeBenchDesign()
As described in simulations-null.Rmd, we include Scott's FDR Regression in the analysis
for simulations with Gaussian test statistics. Again, we include both
nulltype = "empirical" and nulltype = "theoretical". Since all settings in this
series of simulations use test statistics simulated with Gaussian test statistics, we include
Scott's FDR Regression in all of the comparisons.
bdplus <- bd bdplus <- addBMethod(bdplus, "fdrreg-t", FDRreg::FDRreg, function(x) { x$FDR }, z = test_statistic, features = model.matrix( ~ splines::bs(ind_covariate, df = 3) - 1), nulltype = 'theoretical', control = list(lambda = 0.01)) bdplus <- addBMethod(bdplus, "fdrreg-e", FDRreg::FDRreg, function(x) { x$FDR }, z = test_statistic, features = model.matrix( ~ splines::bs(ind_covariate, df = 3) - 1), nulltype = 'empirical', control = list(lambda = 0.01))
All simulation settings will share the following parameters.
m <- 20000 # integer: number of hypothesis tests es_dist <- rnorm_generator(2) # functional: dist of alternative test stats ts_dist <- rnorm_perturber(1) # functional: sampling dist/noise for test stats null_dist <- rnorm_2pvaluer(1) # functional: dist to calc p-values icovariate <- runif # functional: independent covariate
Simulation results will be presented excluding a subset of methods, and for certain plots (upset plots), a single alpha cutoff will be used.
excludeSet <- c("unadjusted", "bl-df02", "bl-df04", "bl-df05") ualpha <- 0.05
First, we show the form of the relationship between the informative covariate and null proportion, pi0, across varying levels of "informativeness."
xseq <- seq(0, 1, by = .01) pi0trends <- lapply(seq(0, 1, by = .2), function(i) { tibble(info = i, x = xseq, pi0 = pi0_varyinfo80l(i)(xseq)) }) pi0trends <- bind_rows(pi0trends) ggplot(pi0trends, aes(x = x, y = pi0)) + geom_line() + geom_text(aes(label = round(pi0, 3), hjust = x, vjust = round(pi0)), dplyr::filter(pi0trends, x == 0 | x == 1), size = 3, color = 'blue') + theme_bw() + scale_x_continuous("x (informative covariate)", breaks = 0:1) + scale_y_continuous(labels = scales::percent) + facet_grid(. ~ info, labeller = label_both) + ggtitle("Covariate vs. pi0 for varying informativeness")
Informativeness = 0 Setting
First, we consider the setting where the informativeness is 0.
Data Simulation
pi0 <- pi0_varyinfo80l(0.00) # numeric: proportion of null hypotheses seed <- 1010
We next run the simulations.
if (file.exists(info00_file)) { res <- readRDS(info00_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info00_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 20 Setting
Next, we consider the setting where the informativeness is 0.20.
Data Simulation
pi0 <- pi0_varyinfo80l(0.20) # numeric: proportion of null hypotheses seed <- 1201
We next run the simulations.
if (file.exists(info20_file)) { res <- readRDS(info20_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info20_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 40 Setting
Next, we consider the setting where the informativeness is 0.40.
Data Simulation
pi0 <- pi0_varyinfo80l(0.40) # numeric: proportion of null hypotheses seed <- 51
We next run the simulations.
if (file.exists(info40_file)) { res <- readRDS(info40_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info40_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 60 Setting
Next, we consider the setting where the informativeness is 0.60.
Data Simulation
pi0 <- pi0_varyinfo80l(0.60) # numeric: proportion of null hypotheses seed <- 500
We next run the simulations.
if (file.exists(info60_file)) { res <- readRDS(info60_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info60_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 80 Setting
Next, we consider the setting where the informativeness is 0.80.
Data Simulation
pi0 <- pi0_varyinfo80l(0.80) # numeric: proportion of null hypotheses seed <- 608
We next run the simulations.
if (file.exists(info80_file)) { res <- readRDS(info80_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info80_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Informativeness = 100 Setting
Finally, we consider the setting where the informativeness is 1.00.
Data Simulation
pi0 <- pi0_varyinfo80l(1.00) # numeric: proportion of null hypotheses seed <- 808
We next run the simulations.
if (file.exists(info100_file)) { res <- readRDS(info100_file) } else { res <- mclapply(X = 1:B, FUN = simIteration, bench = bdplus, m = m, pi0 = pi0, es_dist = es_dist, icovariate = icovariate, ts_dist = ts_dist, null_dist = null_dist, seed = seed, mc.cores = cores) saveRDS(res, file = info100_file) } res_i <- lapply(res, `[[`, "informative") res_u <- lapply(res, `[[`, "uninformative")
Covariate Diagnostics
Here, we show the relationship between the independent covariate and p-values for a single replication of the experiment.
onerun <- simIteration(bdplus, m = m, pi0 = pi0, es_dist = es_dist, ts_dist = ts_dist, icovariate = icovariate, null_dist = null_dist, execute = FALSE)
rank_scatter(onerun, pvalue = "pval", covariate = "ind_covariate")
strat_hist(onerun, pvalue = "pval", covariate = "ind_covariate", maxy = 10, numQ = 3)
Benchmark Metrics
We plot the averaged results across r B replications.
resdf <- plotsim_standardize(res_i, alpha = seq(0.01, 0.10, 0.01)) plotsim_average(resdf, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE) plotsim_average(resdf, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE)
We also take a look at the distribution of rejects for each method as a function of the effect size and independent covariate.
covariateLinePlot(res_i, alpha = ualpha, covname = "effect_size") covariateLinePlot(res_i, alpha = ualpha, covname = "ind_covariate")
Finally, (if enough methods produce rejections at r ualpha) we take a look at
the overlap of rejections between methods.
if (numberMethodsReject(resdf, alphacutoff = ualpha, filterSet = excludeSet) >= 3) { aggupset(res_i, alpha = ualpha, supplementary = FALSE, return_list = FALSE) } else { message("Not enough methods found rejections at alpha ", ualpha, "; skipping upset plot") }
We also compare the simulation results with and without an informative covariate.
resdfu <- plotsim_standardize(res_u, alpha = seq(0.01, 0.10, 0.01)) resdfiu <- dplyr::full_join(select(resdf, rep, blabel, param.alpha, key, performanceMetric, alpha, value), select(resdfu, rep, blabel, param.alpha, key, performanceMetric, alpha, value), by = c("rep", "blabel", "param.alpha", "key", "performanceMetric", "alpha"), suffix = c(".info", ".uninfo")) resdfiu <- dplyr::mutate(resdfiu, value = value.info - value.uninfo) plotsim_average(resdfiu, met="rejections", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="FDR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TPR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE) plotsim_average(resdfiu, met="TNR", filter_set = excludeSet, merge_ihw = TRUE, errorBars = TRUE, diffplot = TRUE)
Session Info
sessionInfo()
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