Description Usage Arguments Value Examples
View source: R/getBestFeatureParameters.R
Estimate feature grid search FDR by decoy counting.
1 2 3 4 5 | estimateGridSearchDecoyFDR(complex_features_list,
grid_search_params = c("corr", "window", "rt_height",
"smoothing_length", "min_feature_completeness",
"min_hypothesis_completeness", "min_subunits", "min_peak_corr",
"min_monomer_distance_factor"))
|
complex_features_list |
data.table containing filtered complex feature results. |
grid_search_params |
Character vector of column names to report with the statistics for the dataset. Should contain all parameters that are of interest in the grid search. |
A data.table with FDR estimations for every parameter combination specified.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Generate example data
complexFeaturesGrid <- list(exampleComplexFeatures)
complexFeaturesGridFiltered <- filterGridSearchResults(complexFeaturesGrid,
peak_corr_cutoffs = c(0.5,0.75,0.9),
feature_completeness_cutoffs = c(0,0.5,1),
hypothesis_completeness_cutoffs = c(0.5,1),
n_subunits_cutoffs =c(2,3,4),
monomer_distance_cutoffs = c(1,2),
remove_decoys=FALSE)
## Calculate the FDR statistic for every grid search result
# Here we only performed grid search of different filter cutoffs, so only those are
# specified as grid_search_params
gridStats <- estimateGridSearchDecoyFDR(complexFeaturesGridFiltered,
grid_search_params =c("min_feature_completeness",
"min_hypothesis_completeness",
"min_subunits",
"min_peak_corr",
"min_monomer_distance_factor"))
## Inspect the oputput
gridStats
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