| screen.wgtd.corP | R Documentation | 
Performs feature selection according to the ranking of P-values
returned from weighted correlations. Implemented via
wtd.cor.
screen.wgtd.corP(
  Y,
  X,
  family,
  obsWeights,
  id,
  method = "pearson",
  minPvalue = 0.1,
  k = 2,
  ...
)
| Y | Outcome (numeric vector). See  | 
| X | Predictor variable(s) (data.frame or matrix). See
 | 
| family | Error distribution to be used in the model:
 | 
| obsWeights | Optional numeric vector of observation weights. See
 | 
| id | Cluster identification variable. Currently unused. | 
| method | Which correlation coefficient to compute. Currently only
accepts  | 
| minPvalue | To pass the screen, resulting P-values must not exceed this number. | 
| k | Minimum number of features to select. Only used
if less than this number of features are selected using  | 
| ... | Passed to  | 
A logical vector with length equal to ncol(X)
# based on example in SuperLearner package
set.seed(1)
n <- 100
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)
obsWeights <- 1/runif(n)
screen.wgtd.corP(Y, X, gaussian(), obsWeights, seq(n), minPvalue = 0.000001)
screen.wgtd.corP01 <- function(..., minPvalue = 0.01){
    screen.wgtd.corP(..., minPvalue = minPvalue)
}
library(SuperLearner)
sl = SuperLearner(Y, X, family = gaussian(), cvControl = list(V = 2),
                  obsWeights = obsWeights,
                  SL.library = list(c("SL.glm", "All"),
                                    c("SL.glm.interaction", "screen.wgtd.corP01")))
sl
sl$whichScreen
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