Variable Selection in Leading Sets under the S3VS Framework

VS_method applies the chosen variable-selection algorithm to each leading set produced by S3VS at every iteration.

VS_method(y, X, family, surv_model = NULL, vsel_method, alpha = 0.5,
          p_thresh = 0.1, gamma = 0.9, verbose = FALSE)

Arguments

y: Response. If family = "normal", a numeric vector. If family = "binomial", a numeric/integer/logical vector with values in {0,1}. If family = "survival", a list with components time and status (1 = event, 0 = censored).

X: Predictor matrix. Can be a base matrix or something as.matrix() can coerce. No missing values are allowed.

family: Model family; one of c("normal","binomial","survival"). Determines which engine is called (VS_method_LM, VS_method_GLM, or VS_method_SURV).

surv_model: Character string specifying the survival model (family="survival" only). Must be explicitly provided; there is no default. Values are "Cox" for proportional hazards models, "AFT" for accelerated failure time models.

vsel_method: Character string indicating the variable-selection engine used inside S3VS at each iteration. Allowed values depend on family: for family = "normal"(S3VS_LM) and "binomial" (S3VS_GLM), available options are "NLP", "ENET", "LASSO", "SCAD", "MCP"; for family = "survival", available options are "LASSO", "ENET" for surv_model = "COX" and "AFTGEE", "BRIDGE", "PVAFT" for surv_model = "AFT". See Details for more information.

alpha: Only used when vsel_method == "ENET". Elastic net mixing parameter, with \(\alpha \in (0,1)\).

p_thresh: Only used for surv_model = "AFT" with vsel_method = "AFTGEE". p-value threshold for variable selection.

gamma: Only used for surv_model = "AFT" with vsel_method = "BRIDGE". Numeric scalar (default 0.5) giving the exponent in the bridge penalty. Must be \(> 0\). See details.

verbose: If TRUE, some information is printed. FALSE, by default.

Details

Details to come...

Value

A list containing:

sel: Character vector with names of the selected predictors.
nosel: Character vector with names of the predictors not selected.

Author

Nilotpal Sanyal <nsanyal@utep.edu>, Padmore N. Prempeh <pprempeh@albany.edu>

Examples

# Simulate continuous data
set.seed(123)
n <- 100
p <- 150
X <- matrix(rnorm(n * p), n, p)
colnames(X) <- paste0("V", 1:p)
y <- X[,1] + 0.5 * X[,2] + rnorm(n)
# Run VS_method
VS_method(y, X, family = "normal", vsel_method = "NLP", verbose = FALSE)
#> $sel
#> [1] "V1"
#> 
#> $nosel
#>   [1] "V2"   "V3"   "V4"   "V5"   "V6"   "V7"   "V8"   "V9"   "V10"  "V11" 
#>  [11] "V12"  "V13"  "V14"  "V15"  "V16"  "V17"  "V18"  "V19"  "V20"  "V21" 
#>  [21] "V22"  "V23"  "V24"  "V25"  "V26"  "V27"  "V28"  "V29"  "V30"  "V31" 
#>  [31] "V32"  "V33"  "V34"  "V35"  "V36"  "V37"  "V38"  "V39"  "V40"  "V41" 
#>  [41] "V42"  "V43"  "V44"  "V45"  "V46"  "V47"  "V48"  "V49"  "V50"  "V51" 
#>  [51] "V52"  "V53"  "V54"  "V55"  "V56"  "V57"  "V58"  "V59"  "V60"  "V61" 
#>  [61] "V62"  "V63"  "V64"  "V65"  "V66"  "V67"  "V68"  "V69"  "V70"  "V71" 
#>  [71] "V72"  "V73"  "V74"  "V75"  "V76"  "V77"  "V78"  "V79"  "V80"  "V81" 
#>  [81] "V82"  "V83"  "V84"  "V85"  "V86"  "V87"  "V88"  "V89"  "V90"  "V91" 
#>  [91] "V92"  "V93"  "V94"  "V95"  "V96"  "V97"  "V98"  "V99"  "V100" "V101"
#> [101] "V102" "V103" "V104" "V105" "V106" "V107" "V108" "V109" "V110" "V111"
#> [111] "V112" "V113" "V114" "V115" "V116" "V117" "V118" "V119" "V120" "V121"
#> [121] "V122" "V123" "V124" "V125" "V126" "V127" "V128" "V129" "V130" "V131"
#> [131] "V132" "V133" "V134" "V135" "V136" "V137" "V138" "V139" "V140" "V141"
#> [141] "V142" "V143" "V144" "V145" "V146" "V147" "V148" "V149" "V150"
#>