# Refit the logistic regression with chosen variables Refit the logistic regression with chosen variables. ```r Refit_glm(X, beta_pred, y) ``` ## Arguments - `X`: Design matrix of the logistic model considered. - `beta_pred`: Predicted coefficients to be refited. - `y`: Binary response ## Returns - **beta_refit**: The new estimated coefficients ## Author(s) Wencan Zhu, Celine Levy-Leduc, Nils Ternes ## Examples ```r data(X) data(y) data(beta) Refit_glm(X=X, beta_pred=beta, y=y) ##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ##-- or do help(data=index) for the standard data sets. ## The function is currently defined as function (X, beta_pred, y) { X_temp <- X[, which(beta_pred != 0)] if (length(which(beta_pred != 0)) == 0) { coef_est <- beta_pred } else if (is.null(ncol(X_temp))) { mydata <- data.frame(Y = y, X_temp) colnames(mydata) <- c("Y", "X") formula <- paste0("Y~-1 +", paste0(colnames(mydata)[-which(colnames(mydata) == "Y")], collapse = " + ")) myform <- as.formula(formula) mod_lm <- glm(myform, data = mydata, family = "binomial") coef_est <- mod_lm$coefficients } else { mydata <- data.frame(Y = y, as.matrix(X_temp)) formula <- paste0("Y~-1 +", paste0(colnames(mydata)[-which(colnames(mydata) == "Y")], collapse = " + ")) myform <- as.formula(formula) if (length(which(beta_pred != 0)) >= length(y)) { mod_ridge <- cv.glmnet(x = as.matrix(X_temp), y = y, alpha = 0, intercept = FALSE, family = "binomial") opt_lambda <- mod_ridge$lambda[which.min(mod_ridge$cvm)] coef_est <- as.vector(glmnet(x = as.matrix(X), y = y, alpha = 0, intercept = FALSE, family = "binomial", lambda = opt_lambda)$beta) } else { mod_lm <- glm(myform, data = mydata, family = "binomial") coef_est <- mod_lm$coefficients } } beta_refit <- rep(0, length(beta_pred)) beta_refit[which(beta_pred != 0)] <- coef_est return(beta_refit) } ```

Refit_glm function