predict.subgroup_fitted | R Documentation |
Predicts benefit scores or treatment recommendations based on a fitted subgroup identification model
Function to obtain predictions for weighted ksvm objects
## S3 method for class 'subgroup_fitted' predict( object, newx, type = c("benefit.score", "trt.group"), cutpoint = 0, ... ) ## S3 method for class 'wksvm' predict(object, newx, type = c("class", "linear.predictor"), ...)
object |
fitted object returned by For |
newx |
new design matrix for which predictions will be made |
type |
type of prediction. For |
cutpoint |
numeric value for patients with benefit scores above which
(or below which if |
... |
not used |
fit.subgroup
for function which fits subgroup identification models.
weighted.ksvm
for fitting weighted.ksvm
objects
library(personalized) set.seed(123) n.obs <- 500 n.vars <- 15 x <- matrix(rnorm(n.obs * n.vars, sd = 3), n.obs, n.vars) # simulate non-randomized treatment xbetat <- 0.5 + 0.5 * x[,11] - 0.5 * x[,3] trt.prob <- exp(xbetat) / (1 + exp(xbetat)) trt01 <- rbinom(n.obs, 1, prob = trt.prob) trt <- 2 * trt01 - 1 # simulate response delta <- 2 * (0.5 + x[,2] - x[,3] - x[,11] + x[,1] * x[,12]) xbeta <- x[,1] + x[,11] - 2 * x[,12]^2 + x[,13] xbeta <- xbeta + delta * trt # continuous outcomes y <- drop(xbeta) + rnorm(n.obs, sd = 2) # create function for fitting propensity score model prop.func <- function(x, trt) { # fit propensity score model propens.model <- cv.glmnet(y = trt, x = x, family = "binomial") pi.x <- predict(propens.model, s = "lambda.min", newx = x, type = "response")[,1] pi.x } subgrp.model <- fit.subgroup(x = x, y = y, trt = trt01, propensity.func = prop.func, loss = "sq_loss_lasso", nfolds = 3) # option for cv.glmnet subgrp.model$subgroup.trt.effects benefit.scores <- predict(subgrp.model, newx = x, type = "benefit.score") rec.trt.grp <- predict(subgrp.model, newx = x, type = "trt.group")
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