# Predict from glmnet_with_cv Fits (svem_cv Objects) Generate predictions from a fitted object returned by `glmnet_with_cv()` (class `"svem_cv"`). ```r predict_cv(object, newdata, debias = FALSE, strict = FALSE, ...) ## S3 method for class 'svem_cv' predict(object, newdata, debias = FALSE, strict = FALSE, ...) ``` ## Arguments - `object`: A fitted object returned by `glmnet_with_cv()` (class `"svem_cv"`). - `newdata`: A data frame of new predictor values. - `debias`: Logical; if `TRUE` and a debiasing fit is available, apply it. Has an effect only for Gaussian models where `debias_fit` is present. - `strict`: Logical; if `TRUE`, enforce a strict column-name match between the aligned design for `newdata` and the training design (including the intercept position). Default `FALSE`. - `...`: Additional arguments (currently unused). ## Returns A numeric vector of predictions on the response scale: numeric fitted values for Gaussian models; probabilities in `[0,1]` for binomial models. Rows with unseen factor/character levels return `NA`. ## Details The design matrix for `newdata` is rebuilt using the stored training `terms` (with environment set to `baseenv()`), together with the saved factor `xlevels` and `contrasts` (cached in `object$schema`). Columns are then aligned back to the training design in a robust way: * Any training columns that `model.matrix()` drops for `newdata` (for example, a factor collapsing to a single level) are added back as zero columns. * Columns are reordered to exactly match the training order. * Rows containing unseen factor/character levels are warned about and their predictions are set to `NA`. For Gaussian fits (`family = "gaussian"`), the returned values are on the original response (identity-link) scale. For binomial fits (`family = "binomial"`), the returned values are probabilities in `[0,1]` (logit-link inverted via `plogis()`). If `debias = TRUE` and a calibration model `lm(y ~ y_pred)` is present with a finite slope, predictions are adjusted via `a + b * pred`. Debiasing is only fitted and used for Gaussian models; for binomial models the `debias` argument has no effect. `predict_cv()` is a small convenience wrapper that simply calls the underlying S3 method `predict.svem_cv()`, keeping a single code path for prediction from `glmnet_with_cv()` objects. ## Examples ```r set.seed(1) n <- 50; p <- 5 X <- matrix(rnorm(n * p), n, p) y <- X[, 1] - 0.5 * X[, 2] + rnorm(n) df_ex <- data.frame(y = as.numeric(y), X) colnames(df_ex) <- c("y", paste0("x", 1:p)) fit <- glmnet_with_cv( y ~ ., df_ex, glmnet_alpha = 1, nfolds = 5, repeats = 2, seed = 9, family = "gaussian" ) preds_raw <- predict_cv(fit, df_ex) preds_db <- predict_cv(fit, df_ex, debias = TRUE) cor(preds_raw, df_ex$y) # Binomial example (probability predictions on [0,1] scale) set.seed(2) n2 <- 80; p2 <- 4 X2 <- matrix(rnorm(n2 * p2), n2, p2) eta2 <- X2[, 1] - 0.8 * X2[, 2] pr2 <- plogis(eta2) y2 <- rbinom(n2, size = 1, prob = pr2) df_bin <- data.frame(y = y2, X2) colnames(df_bin) <- c("y", paste0("x", 1:p2)) fit_bin <- glmnet_with_cv( y ~ ., df_bin, glmnet_alpha = c(0.5, 1), nfolds = 5, repeats = 2, seed = 11, family = "binomial" ) prob_hat <- predict_cv(fit_bin, df_bin) summary(prob_hat) ```

predict_cv function