residuals.logistic now handling missing values properly

R/logistic.R

@@ -19,12 +19,22 @@ train_logistic <- function(.data, specials, ...) {
   keep <- complete.cases(xreg) & complete.cases(y)
   fit <- stats::glm.fit(xreg[keep, , drop = FALSE], y[keep, , drop = FALSE],
                         family = stats::binomial())
-  resid <- fits <- matrix(nrow = nrow(y), ncol = ncol(y))
-  resid[keep, ] <- as.matrix(fit$residuals)
-  fit$residuals <- resid
-  fits[keep, ] <- as.matrix(fit$fitted.values)
-  fit$fitted.values <- fits
-
+  # Fill in missing values
+  tmp <- matrix(nrow = nrow(y), ncol = ncol(y))
+  fit$y <- y
+  tmp[keep, ] <- as.matrix(fit$residuals)
+  fit$residuals <- tmp
+  tmp[keep, ] <- as.matrix(fit$fitted.values)
+  fit$fitted.values <- tmp
+  tmp[keep, ] <- as.matrix(fit$effects)
+  fit$effects <- tmp
+  tmp[keep, ] <- as.matrix(fit$linear.predictors)
+  fit$linear.predictors <- tmp
+  tmp[keep,] <- as.matrix(fit$weights)
+  fit$weights <- tmp
+  tmp[keep,] <- as.matrix(fit$prior.weights)
+  fit$prior.weights <- tmp
+
   if (is_empty(fit$coefficients)) {
     fit$coefficients <- matrix(nrow = 0, ncol = NCOL(y))
   }