Predict target functions (or scalar summaries) from a MetaHunt fit

Usage

# S3 method for class 'metahunt'
predict(object, newdata, wrapper = NULL, grid_weights = NULL, ...)

Arguments

object: A "metahunt" object from metahunt().
newdata: A matrix or data frame of new study-level covariates.
wrapper: Optional reduction function. If NULL, returns the full predicted function matrix (nrow(newdata)-by-G_grid). If a function, applied to each predicted function to return a scalar per new target; see apply_wrapper().
grid_weights: Optional length-G_grid non-negative numeric vector used only when wrapper = NULL with the default weighted-mean path. Defaults to the grid_weights stored in object.
...: Ignored.

Value

Either an nrow(newdata)-by-G_grid matrix of predicted functions (when wrapper = NULL) or a length-nrow(newdata) numeric vector of scalar summaries.

Examples

set.seed(1)
G <- 25; m <- 40
x <- seq(0, 1, length.out = G)
basis <- rbind(sin(pi * x), cos(pi * x), x)
W <- data.frame(w1 = rnorm(m), w2 = rnorm(m))
eta <- as.matrix(W) %*% cbind(c(1, -0.8), c(-0.5, 1.2), c(0, 0))
pi_true <- exp(eta) / rowSums(exp(eta))
F_hat <- pi_true %*% basis + matrix(rnorm(m * G, sd = 0.05), m, G)

fit <- metahunt(F_hat, W, K = 3, dfspa_args = list(denoise = FALSE))
f_pred <- predict(fit, newdata = W[1:3, ])
dim(f_pred)                                       # 3 x G
#> [1]  3 25
predict(fit, newdata = W[1:3, ], wrapper = mean)  # scalar summaries
#> [1] 0.3084921 0.4289405 0.1178668