Description Usage Arguments Value Examples
Fit the Variable selection by combining method fits (VSC) method
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X |
covariates (n times p matrix, n: number of entries, p: number of covariates) |
Y |
response (vector with n entries) |
intercept |
TRUE to fit the data with an intercept, FALSE to fit the data without an intercept |
method.names |
vector of method names to be used in VSC. Choose among "lasso", "elastic", "relaxo", "mcp" and "scad". Default is to use all methods listed above |
coef.est.method |
method to estimate the coefficients of covariates after variable selection. User can provide his/her function. Default is ordinary least square |
B |
number of subsampling. Default is 100 |
q |
percentile of fitted models used per each subsampling in VSC, according to the selection criterion on out-of-sample data in ascending order. Default is q = 0 (only the fitted model with the lowest MSE in a subsampling data is used) |
fit.percent |
percentage of observations used in fitting in VSC |
selection.criterion |
= c("mse", "ebic"). Measure to select fitted models in subsampling dataset. "mse" is mean square error and "ebic" is extended BIC. Default is mse |
num.core |
number of cores to use. Default is 1 (i.e. no parallel running) |
all.fits |
(optional) all fitted models. If all.fits is provided, then VSC will use the fitted models in all.fitted instead of fitting using subsampling data |
a list, which includes estimated coefficients (est.b), subsampling fitted models (mod.collection), number of times a method is selected (method.freq), relative frequency of each covariate (variable.freq), covariates ordered by relative frequency (variable.order).
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