## ----cdk-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)

## ----cdk-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)

## ----cdk-elbow----------------------------------------------------------------
elbow <- reconstruction_error_curve(F_hat, K_range = 2:6,
                                    dfspa_args = list(denoise = FALSE))
plot(elbow$K, elbow$error, type = "b",
     xlab = "K", ylab = "reconstruction error",
     main = "Reconstruction error vs K",
     ylim = c(0, max(elbow$error, na.rm = TRUE) * 1.05))

## ----cdk-cv-------------------------------------------------------------------
cv <- cv_error_curve(F_hat, W, K_range = 2:6, n_folds = 4,
                     dfspa_args = list(denoise = FALSE), seed = 1)
plot(cv$K, cv$cv_error, type = "b",
     xlab = "K", ylab = "CV prediction error",
     main = "CV prediction error vs K",
     ylim = c(0, max(cv$cv_error, na.rm = TRUE) * 1.05))

## ----cdk-bypass---------------------------------------------------------------
fit_no <- metahunt(F_hat, W, K = K_true,
                   dfspa_args = list(denoise = FALSE))
fit_no

## ----cdk-dfspa-knobs----------------------------------------------------------
fit_no <- metahunt(F_hat, W, K = K_true,
                   dfspa_args = list(denoise = FALSE))

fit_manual <- metahunt(F_hat, W, K = K_true,
                       dfspa_args = list(N = 0.5 * log(nrow(F_hat)),
                                         Delta = 0.4))

## ----cdk-select-denoising, warning = FALSE------------------------------------
tune <- select_denoising_params(F_hat, W, K = K_true, n_folds = 4, seed = 1)
tune$best