filter_control: Control for filtering step of 5-STAR algorithm
| filter_control | R Documentation |
Control for filtering step of 5-STAR algorithm
Description
Parameters for control of the elastic net or random forest filtering step (step 2 of the 5-STAR algorithm)
Usage
filter_control(method = "ENET", lambdatype = "min", mixparm = NULL,
vimpalpha = 0.05, nfolds = 10, filterseed = 2019, ...)
Arguments
method |
specifying method used for filtering; either "ENET" for elastic net, "RF" for random forest using defaults in randomForestSRC package (with possible modifications through "..."), or "RFbest" for tuned random forest (slower but more optimized) |
lambdatype |
Optional elastic net parameter; whether to use lambda that minimizes cross validation error (lambdatype="min", default) or the largest lambda that gives error within 1 standard error of minimum error (lambdatype="1se"). Ignored when method is "RF" or "RFbest" |
mixparm |
Optional elastic net mixing parameter alpha or grid of alpha values to search over. If nothing is entered, will search for best value between 0.05 and 0.95. Ignored when method is "RF" or "RFbest" |
vimpalpha |
For "RF" or "RFbest", the significance level for variable importance (VIMP) confidence intervals (CIs) to determine which covariates are passed through the filtering stage (default = 0.05, ignored for method == "ENET") |
nfolds |
Number of folds used for cross validation when tuning the elastic net tuning parameter lambda (Default = 10; ignored when method is "RF" or "RFbest") |
filterseed |
Seed for the filtering step to control variability in cross validation step of the elastic net filtering |
... |
Optional additional arguments passed into
|
Value
A list of control parameters for filtering step
