rope {rope} | R Documentation |
FDR controlled model selection
Description
Estimates a model from bootstap counts. The objective is to maximize accuracy while controlling the false discovery rate of selected variables. Developed for high-dimensional models with number of variables in the order of at least 10000.
Usage
rope(data, B, fdr = 0.1, mc.cores = getOption("mc.cores", 2L),
only.first = FALSE)
Arguments
data |
Matrix of variable presence counts. One column for each variable, one row for each parameter value (e.g. levels of regularization). |
B |
Number of bootstraps used to construct |
fdr |
Vector of target false discovery rates to return selections for |
mc.cores |
Number of threads to run in parallel (1 turns of parallelization) |
only.first |
Skip second part of algorithm. Saves time but gives worse results. |
Value
A list with components
selection |
matrix (one row for each fdr target, one column for each variable) |
q |
vector of q-values, one for each variable |
level |
index of most separating parameter value |
alt.prop |
estimated proportion of alternative variables |
Author(s)
Jonatan Kallus, kallus@chalmers.se
Examples
## Not run:
data # a matrix of selection counts, for 100 bootstraps, with ncol(data)
# potential variables counted for nrow(data) different penalization levels
fdr <- c(0.05, 0.1)
result <- rope(data, 100, fdr)
## End(Not run)