Description Usage Arguments Details Value Examples
run_evals: Main function to benchmark FDR methods on given simulations.
1 |
sim_funs |
List of simulation settings |
fdr_methods |
List of FDR controlling methods to be benchmarked |
nreps |
Integer, number of Monte Carlo replicates for the simulations |
alphas |
Numeric, vector of nominal significance levels at which to apply FDR controlling methods |
... |
Additional arguments passed to sim_fun_eval |
This is the main workhorse function which runs all simulation benchmarks for IHWpaper. It receives input as described above, and the output is a data.frame with the following columns:
fdr_method: Multiple testing method which was used
fdr_pars: Custom parameters of the multiple testing method
alpha: Nominal significance level at which the benchmark was run
FDR: False Discovery Rate of benchmarked method on simulated dataset
power: Power of benchmarked method on simulated dataset
rj_ratio: Average rejections divided by total number of hypotheses
FPR: False positive rate of benchmarked method on simulated dataset
FWER: Familywise Error Rate of benchmarked method on simulated dataset
nsuccessful: Number of successful evaluations of the method
sim_method: Simulation scenario under which benchmark was run
m: Total number of hypotheses
sim_pars: Custom parameters of the simulation scenario
data.frame which summarizes results of numerical experiment
1 2 3 4 5 6 7 8 9 10 11 | nreps <- 3 # monte carlo replicates
ms <- 5000 # number of hypothesis tests
eff_sizes <- c(2,3)
sim_funs <- lapply(eff_sizes,
function(x) du_ttest_sim_fun(ms,0.95,x, uninformative_filter=FALSE))
continuous_methods_list <- list(bh,
lsl_gbh,
clfdr,
ddhf)
fdr_methods <- lapply(continuous_methods_list, continuous_wrap)
eval_table <- run_evals(sim_funs, fdr_methods, nreps, 0.1, BiocParallel=FALSE)
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