## ----mm-knit-opts, include = FALSE--------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6, fig.height = 4,
  fig.align = "center"
)

## ----setup--------------------------------------------------------------------
library(MetaHunt)
set.seed(1)

## ----mm-simulate--------------------------------------------------------------
m <- 30; G <- 20; K_true <- 3
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))
beta <- cbind(c(1, -0.8), c(-0.5, 1.2), c(0, 0))
pi_true <- exp(as.matrix(W) %*% beta); pi_true <- pi_true / rowSums(pi_true)
F_hat <- pi_true %*% basis + matrix(rnorm(m * G, sd = 0.05), m, G)

## ----mm-wnew------------------------------------------------------------------
W_new <- data.frame(w1 = 0, w2 = 0)

## ----mm-fit-------------------------------------------------------------------
mm <- minmax_regret(F_hat)
mm

## ----mm-fit-metahunt----------------------------------------------------------
fit <- metahunt(F_hat, W, K = K_true, dfspa_args = list(denoise = FALSE))
f_pred <- predict(fit, newdata = W_new)

## ----mm-vs-metahunt-----------------------------------------------------------
plot(x, mm$prediction, type = "l", col = "tomato", lwd = 2,
     ylim = range(c(mm$prediction, f_pred[1, ])),
     xlab = "x", ylab = expression(tilde(f)(x)),
     main = "minimax-regret vs. MetaHunt (target W = (0,0))")
lines(x, f_pred[1, ], col = "steelblue", lwd = 2)
legend("topright", legend = c("minmax_regret", "metahunt (W = (0,0))"),
       col = c("tomato", "steelblue"), lty = 1, lwd = 2, bty = "n")