covariate adjustment with continuous outcome

[nick-youngblut]

Given that one would not use glm(family='binomial') for a continuous outcome, it would be helpful to include a code snippet as in:

xTest <- x[-trainIndex,]
yTest <- z[-trainIndex]
yHatLogit <- predict(partial, newdata = dfTest) + predict(model, xTest, logits=T)
yHat <- yHatLogit > 0 # in case we need binary predictions e.g. to compute accuracy
testAUC <- pROC::auc(pROC::roc(yTest, yHatLogit, quiet=T))
cat("Test AUC:", round(100 * testAUC), "%")

...but for continuous outcomes

[egr95]

Good idea, thanks. Have added in such a snippet, let me know if anything is unclear.