SVEMnet (version 3.2.0)

predict_cv: Predict from glmnet_with_cv Fits (svem_cv Objects)

Description

Generate predictions from a fitted object returned by glmnet_with_cv() (class "svem_cv").

Usage

predict_cv(object, newdata, debias = FALSE, strict = FALSE, ...)
# S3 method for svem_cv
predict(object, newdata, debias = FALSE, strict = FALSE, ...)

Value

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.

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).

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

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)