postpi_boot_logistic {ipd} | R Documentation |
PostPI Logistic Regression (Bootstrap Correction)
Description
Helper function for PostPI logistic regression (bootstrap correction)
Usage
postpi_boot_logistic(
X_l,
Y_l,
f_l,
X_u,
f_u,
nboot = 100,
se_type = "par",
seed = NULL
)
Arguments
X_l |
(matrix): n x p matrix of covariates in the labeled data. |
Y_l |
(vector): n-vector of labeled outcomes. |
f_l |
(vector): n-vector of predictions in the labeled data. |
X_u |
(matrix): N x p matrix of covariates in the unlabeled data. |
f_u |
(vector): N-vector of predictions in the unlabeled data. |
nboot |
(integer): Number of bootstrap samples. Defaults to 100. |
se_type |
(string): Which method to calculate the standard errors. Options include "par" (parametric) or "npar" (nonparametric). Defaults to "par". |
seed |
(optional) An |
Details
Methods for correcting inference based on outcomes predicted by machine learning (Wang et al., 2020) https://www.pnas.org/doi/abs/10.1073/pnas.2001238117
Value
A list of outputs: estimate of inference model parameters and corresponding standard error based on both parametric and non-parametric bootstrap methods.
Examples
dat <- simdat(model = "logistic")
form <- Y - f ~ X1
X_l <- model.matrix(form, data = dat[dat$set_label == "labeled",])
Y_l <- dat[dat$set_label == "labeled", all.vars(form)[1]] |> matrix(ncol = 1)
f_l <- dat[dat$set_label == "labeled", all.vars(form)[2]] |> matrix(ncol = 1)
X_u <- model.matrix(form, data = dat[dat$set_label == "unlabeled",])
f_u <- dat[dat$set_label == "unlabeled", all.vars(form)[2]] |> matrix(ncol = 1)
postpi_boot_logistic(X_l, Y_l, f_l, X_u, f_u, nboot = 200)