tests/testthat/test-mcmc-functions.R
In jtipton25/BayesMRA: Bayesian Multi-Resolution Gaussian Process Approximations
context("mcmc functions")
# mcmc_mra ---------------------------------------------------------------------
test_that("mcmc_mra", {
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_s3_class(out, "mcmc_mra")
expect_s3_class(out$MRA, "mra_wendland_2d")
expect_error(mcmc_mra(y, X, locs, params, M = -3), "the number of resolutions M must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_coarse_grid = -3), "n_coarse_grid must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_neighbors = -3), "n_neighbors must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_padding = -3), "n_padding must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = -3), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = 1:4), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = NA), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = NULL), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = 1:4), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = NA), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = NULL), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, use_spam = FALSE), "The Matrix package is not currently supported")
expect_error(mcmc_mra(y, X), 'argument "locs" is missing, with no default')
expect_error(mcmc_mra(y, X, locs), 'argument "params" is missing, with no default')
y <- matrix(1:10, 5, 2)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(NA, 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep("aaa", 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(TRUE, 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rnorm(10)
X <- array(0, dim=c(2, 2, 2))
expect_error(mcmc_mra(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- matrix(0, 5, 3)
expect_error(mcmc_mra(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- array(0, dim=c(10, 2, 2))
expect_error(mcmc_mra(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(NA, 10, 3)
expect_error(mcmc_mra(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(1:20, 10, 2)
locs <- matrix(0, 10, 3)
expect_error(mcmc_mra(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(NA, 10, 2)
expect_error(mcmc_mra(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(1:20, 10, 2)
params$n_mcmc <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 500
params$n_adapt <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 500
params$n_thin <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 1
params$n_message <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message = 100
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
priors$mu_beta <- rep(0, ncol(X) + 1)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(NA, ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep("aa", ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- matrix(rep(0, ncol(X)))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(0, ncol(X))
priors$Sigma_beta <- diag(ncol(X) + 1)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(1, ncol(X), ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigam_beta <- diag(NA, ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(rep(0, ncol(X), ncol(X)))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- diag(ncol(X))
priors$alpha_sigma2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 1
priors$beta_sigma2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 1
priors$alpha_tau2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 1
priors$beta_tau2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 1
priors <- NULL
expect_error(mcmc_mra(y, X, locs, params, use_spam = "aaa"), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, use_spam = 3), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = "aaa"), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = 3), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = NA), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = "TRUE")), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = NA)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = 3)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = "TRUE")), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = NA)), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = 3)), "If specified, sample_tau2 must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = "TRUE")), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = NA)), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = 3)), "If specified, sample_lambda must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = "TRUE")), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = NA)), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = 3)), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = "TRUE")), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = NA)), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = 3)), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = 3)), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep("aaa", ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep(NA, ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = matrix(1:ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = 1:3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = matrix(1:ncol(X)))), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = NA)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = -3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = 2:ncol(out$alpha))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = matrix(1:ncol(out$alpha)))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = c(2:ncol(out$alpha), NA))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1:3)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = matrix(1:ncol(X)))), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = NA)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = -3)), "initial value for tau2 must be a positive numeric vector of length M")
# mcmc_mra(y, X, locs, params, priors, M = 2, n_coarse_grid = 4)
# expect_error(mcmc_mra(y, X, params), 'argument "priors" is missing, with no default')
# priors <- default_priors_pg_lm(y, X)
# out <- pg_lm(y, X, params, priors)
# expect_true(class(out) == "pg_lm")
})
# mcmc_mra_integrated ----------------------------------------------------------
test_that("mcmc_mra_integrated", {
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma = 1,
beta_sigma = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_s3_class(out, "mcmc_mra_integrated")
expect_s3_class(out$MRA, "mra_wendland_2d")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = -3), "the number of resolutions M must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_coarse_grid = -3), "n_coarse_grid must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_neighbors = -3), "n_neighbors must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_padding = -3), "n_padding must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = -3), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = 1:4), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = NA), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = NULL), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = 1:4), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = NA), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = NULL), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = FALSE), "The Matrix package is not currently supported")
expect_error(mcmc_mra_integrated(y, X), 'argument "locs" is missing, with no default')
expect_error(mcmc_mra_integrated(y, X, locs), 'argument "params" is missing, with no default')
y <- matrix(1:10, 5, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(NA, 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep("aaa", 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(TRUE, 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rnorm(10)
X <- array(0, dim=c(2, 2, 2))
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- matrix(0, 5, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- array(0, dim=c(10, 2, 2))
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(NA, 10, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(1:20, 10, 2)
locs <- matrix(0, 10, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(NA, 10, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(1:20, 10, 2)
params$n_mcmc <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 10
params$n_adapt <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 10
params$n_thin <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 1
params$n_message <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message = 100
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
priors$mu_beta <- rep(0, ncol(X) + 1)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(NA, ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep("aa", ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- matrix(rep(0, ncol(X)))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(0, ncol(X))
priors$Sigma_beta <- diag(ncol(X) + 1)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(1, ncol(X), ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigam_beta <- diag(NA, ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(rep(0, ncol(X), ncol(X)))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- diag(ncol(X))
priors$alpha_sigma2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 1
priors$beta_sigma2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 1
priors$alpha_tau2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 1
priors$beta_tau2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 1
priors <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = "aaa"), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = 3), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = "aaa"), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = 3), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = NA), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = "TRUE")), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = NA)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = 3)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = "TRUE")), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = NA)), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = 3)), "If specified, sample_tau2 must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = "TRUE")), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = NA)), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = 3)), "If specified, sample_lambda must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = "TRUE")), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = NA)), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = 3)), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = 3)), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep("aaa", ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep(NA, ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = matrix(1:ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = 1:3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = matrix(1:ncol(X)))), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = NA)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = -3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1:3)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = matrix(1:ncol(X)))), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = NA)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = -3)), "initial value for tau2 must be a positive numeric vector of length M")
# mcmc_mra_integrated(y, X, locs, params, priors, M = 2, n_coarse_grid = 4)
# expect_error(mcmc_mra_integrated(y, X, params), 'argument "priors" is missing, with no default')
# priors <- default_priors_pg_lm(y, X)
# out <- pg_lm(y, X, params, priors)
# expect_true(class(out) == "pg_lm")
})
# update_tuning ----------------------------------------------------------------
test_that("update_tuning", {
# update_tuning
expect_equal(
update_tuning(50, 0.6, 0.5),
list(accept = 0, tune = 0.575954955084454))
expect_equal(
update_tuning(250, 0.6, 0.5),
list(accept = 0, tune = 0.532644196047304))
expect_equal(
update_tuning(50, 0.3, 0.5),
list(accept = 0, tune = 0.434061722697292))
expect_equal(
update_tuning(50, 0.3, 0.1),
list(accept = 0, tune = 0.0868123445394585))
# update_tuning_mv
set.seed(111)
k <- 50
accept <- 0.6
lambda <- 0.2
batch_samples <- matrix(rnorm(150), 50, 3)
Sigma_tune <- stats::rWishart(1, 5, diag(3))[, , 1]
Sigma_tune_chol <- chol(Sigma_tune)
expect_equal(
update_tuning_mv(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = 0, lambda = 0.668921761531105,
batch_samples = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), dim = c(50L, 3L)),
Sigma_tune = structure(c(2.97876608285749,
0.412648437574117, 0.432599330050228, 0.412648437574117, 3.87407264820285,
0.587518320732799, 0.432599330050228, 0.587518320732799, 4.70641828672913
), dim = c(3L, 3L)),
Sigma_tune_chol = structure(c(1.72591021865492,
0, 0, 0.239090326434078, 1.95369098477945, 0, 0.250649961611196,
0.270047998225292, 2.13791182281402), dim = c(3L, 3L))))
accept <- 0.1
expect_equal(
update_tuning_mv(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = 0, lambda = 0.12854540862658,
batch_samples = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), dim = c(50L, 3L)),
Sigma_tune = structure(c(2.97876608285749,
0.412648437574117, 0.432599330050228, 0.412648437574117, 3.87407264820285,
0.587518320732799, 0.432599330050228, 0.587518320732799, 4.70641828672913
), dim = c(3L, 3L)),
Sigma_tune_chol = structure(c(1.72591021865492,
0, 0, 0.239090326434078, 1.95369098477945, 0, 0.250649961611196,
0.270047998225292, 2.13791182281402), dim = c(3L, 3L))))
# update_tuning_vec
expect_equal(
update_tuning_vec(50, c(0.1, 0.6), c(0.5, 0.5)),
list(accept = c(0, 0), tune = c(0.434061722697292, 0.575954955084454)))
expect_equal(
update_tuning_vec(250, c(0.1, 0.6), c(0.25, 0.75)),
list(accept = c(0, 0), tune = c(0.234678235354129, 0.798966294070957)))
expect_equal(
update_tuning_vec(50, c(0.6, 0.1), c(0.5, 0.5)),
list(accept = c(0, 0), tune = c(0.575954955084454, 0.434061722697292)))
expect_equal(
update_tuning_vec(250, c(0.6, 0.1), c(0.25, 0.75)),
list(accept = c(0, 0), tune = c(0.266322098023652, 0.704034706062386)))
# update_tuning_mat
expect_equal(
update_tuning_mat(50, matrix(c(0.1, 0.6, 0.6, 0.1), 2, 2), matrix(c(0.5, 0.25, 1, 0.5), 2, 2)),
list(accept = structure(c(0, 0, 0, 0), .Dim = c(2L, 2L)), tune = structure(c(0.434061722697292,
0.287977477542227, 1.15190991016891, 0.434061722697292), .Dim = c(2L,
2L))))
# update_tuning_mv_mat
set.seed(111)
k <- 50
accept <- c(0.1, 0.6, 0.6)
lambda <- c(0.1, 0.2, 0.5)
batch_samples <- array(rnorm(50*3*4), dim = c(50, 3, 4))
Sigma_tune <- stats::rWishart(4, 5, diag(3))
Sigma_tune_chol <- Sigma_tune
for (j in 1:4) {
Sigma_tune_chol[, , j] <- chol(Sigma_tune[, , j])
}
expect_equal(
update_tuning_mv_mat(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = c(0, 0, 0), lambda = c(0.0483971455170536, 0.50369599627459, 1.25923999068647),
batch_samples = structure(c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), .Dim = c(50L, 3L, 4L)),
Sigma_tune = structure(c(0.861843460847109,
0.0350297118298129, -0.205904229686601, 0.0350297118298129, 5.23938194426575,
0.113512965023776, -0.205904229686601, 0.113512965023776, 1.11610391509234,
12.819834315426, 0.0307410097016759, -3.91159618091204, 0.0307410097016759,
0.606063359369128, 0.0753128883635706, -3.91159618091204, 0.0753128883635706,
3.74553987550437, 3.58825485621149, 1.11700532441496, 0.294659706303476,
1.11700532441496, 6.08365678995394, 2.20167448094668, 0.294659706303476,
2.20167448094668, 6.46870007854512, 5.0065339819057, 2.07428171851721,
-3.28335619632131, 2.07428171851721, 2.42602278480844, -0.181070805667884,
-3.28335619632131, -0.181070805667884, 4.16265859287012), .Dim = c(3L, 3L, 4L)),
Sigma_tune_chol = structure(c(0.928355244961275, 0,
0, 0.0377330898058039, 2.28865859363066, 0, -0.221794653290498,
0.0532547593310908, 1.03154009978899, 3.5804796208645, 0, 0,
0.00858572396908477, 0.778453367076702, 0, -1.09247826970388,
0.108795990628847, 1.59379877592884, 1.89426895033717, 0, 0,
0.5896762042244, 2.39498199661823, 0, 0.155553257762594, 0.880987176243409,
2.38083280761989, 2.23752854326055, 0, 0, 0.927041455969336,
1.25164568617588, 0, -1.46740304440397, 0.94217769624022, 1.05909777020423
), .Dim = c(3L, 3L, 4L))))
})
# predict_mra ------------------------------------------------------------------
test_that("predict_mra", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
new_data <- list(locs_pred = locs, X_pred = X)
# test equality here
# expect_snapshot(predict_mra(out, new_data))
# expect_equal(predict_mra(out, new_data),
new_data <- list(locs_pred = locs, X_pred = NULL)
expect_error(predict_mra(out, new_data), "new_data must be a list that contains a n_pred by p matrix of covariates in the variable X_pred which are used to make predictions")
new_data <- list(locs_pred = NULL, X_pred = X)
expect_error(predict_mra(out, new_data), "new_data must be a list that contains a n_pred by 2 matrix of locations in the variable locs_pred at which to make predictions")
new_data <- list(locs_pred = locs, X_pred = cbind(X, 1))
expect_error(predict_mra(out, new_data), "The columns of X_pred in the list new_data must be the same number as X used to fit the model")
new_data <- list(locs_pred = cbind(locs, 1), X_pred = X)
expect_error(predict_mra(out, new_data), "locs_pred must be a numeric matrix with n_pred rows and 2 columns")
new_data <- list(locs_pred = locs, X_pred = rbind(X, 1))
expect_error(predict_mra(out, new_data), "The number of rows in locs_pred in the list new_data must be equal to the number of rows in X_pred in the list new_data")
new_data <- list(locs_pred = rbind(locs, 1), X_pred = X)
expect_error(predict_mra(out, new_data), "The number of rows in locs_pred in the list new_data must be equal to the number of rows in X_pred in the list new_data")
new_data <- list(locs_pred = locs, X_pred = X)
class(out) <- "adsfads"
expect_error(predict_mra(out, new_data), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
})
# recover_alpha ----------------------------------------------------------------
test_that("mcmc_mra_integrated", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
recover_alpha(out),
structure(c(-55.7413786355926, -120.397997808002, -60.5073216554941,
40.2287465535353, -391.52340363644, -55.3071576106442, -122.06358370527,
-60.8181089915441, 41.4601513129057, -391.526798263586, -54.9296863741474,
-122.116270095828, -61.0835728999231, 40.1437395471594, -392.196851077704,
-55.6916168505625, -122.127248091819, -61.4280435091201, 40.5199697179713,
-391.905384235902, -55.7901911047978, -122.161131564182, -61.190095935052,
40.3221659849157, -392.617192170157, -55.8635555549914, -122.216548960483,
-61.345927054694, 40.6304866315159, -392.958756995231, -56.4363063530628,
-122.596423051482, -60.6971236398901, 39.8418154467378, -392.6677611016,
-57.3412518194609, -122.201538968865, -60.7619747774417, 39.4618417222475,
-392.786002151808, -57.3136943073188, -122.611427917883, -61.397726312415,
39.9426183334513, -393.398335778229, -57.6060035567961, -123.168455878075,
-62.0237382460533, 40.4983262403619, -393.776345949422, -56.7961736828601,
-121.735264447158, -60.5973404032608, 39.6977984710337, -393.414095390959,
-56.7970038511444, -120.784074139994, -59.9286986812442, 39.1937857359592,
-392.924066148986, -57.4161435785065, -121.825171683805, -60.909711245295,
38.4689700923321, -393.351106673855, -57.6660881012975, -121.029305009045,
-59.9729311242236, 38.3777796358401, -393.42345922259, -56.5144596938562,
-121.582535006642, -60.0736252229986, 40.3637117466486, -391.206590608003,
-55.8173752434951, -121.24480497185, -60.5533285258018, 41.0872850269738,
-391.727086402727, -56.3937905872642, -122.005181333795, -60.1937860689367,
39.8841017985187, -391.959256928297, -56.5049496636514, -122.337780360058,
-61.0326467477304, 40.4201702135741, -391.739778106998, -56.0554384768086,
-122.114331084741, -61.4388914075504, 40.5588103286877, -392.566478425026,
-56.6606712675465, -121.976204908192, -60.957100057498, 40.3149781747688,
-392.257655203355, -57.2491008291326, -122.29464579614, -61.2494000057945,
39.76817389636, -392.701816453718, -57.6969889710255, -122.641799764267,
-61.6881055887692, 39.0872435271544, -393.010121852504, -57.2131158081545,
-122.242500630303, -61.251390929922, 40.1000202984715, -392.199539892977,
-56.9103294931197, -122.013944620863, -61.3808974892973, 40.3708922513825,
-393.464404440682, -57.2106425200307, -122.110955945347, -60.7176609536074,
39.3896937650642, -393.451999029011, -57.7036638101613, -121.627796754553,
-61.0618271348651, 38.9499788573248, -392.676963045824, -57.992214496565,
-122.326174541915, -60.4175054890317, 38.9742143977396, -393.196086930595,
-57.7833312192903, -121.556731010002, -60.845007674096, 38.7821426498925,
-392.843045636403, -56.7210564549941, -121.505839367115, -61.1093041207737,
39.1799868835695, -391.88985308275, -56.2811046614376, -121.858181456847,
-60.1616351417276, 40.1158125667916, -391.7012109666, -56.9370606515037,
-121.573214769566, -60.6393436783314, 40.0685327365464, -391.801689869026,
-56.831162837391, -121.695539839476, -60.7699380785708, 40.3986115878442,
-391.733803382586, -56.7355588083469, -121.984450524882, -60.980073424283,
40.1389370895812, -392.178203807032, -56.9231898113335, -122.238248203191,
-61.0559538574352, 39.6465416159737, -392.676362809342, -57.5296361074082,
-122.859124697033, -61.1083995333637, 39.9169791643099, -392.270299880068,
-57.7609759844901, -122.048948944002, -61.6963536771427, 40.3362594352182,
-392.659032675467, -57.2662962457858, -121.745424622488, -60.9758954597101,
40.4560685361592, -392.22609588679, -57.5784657919016, -121.743572926083,
-61.2057499028299, 39.8703405900925, -392.917231675397, -58.1350466397889,
-121.511235844667, -61.4774368220503, 39.025883710902, -392.678608000355,
-57.6912859756921, -121.758643946906, -61.1743432921329, 38.975234025561,
-392.722680900143, -58.0898657647473, -121.70917082115, -60.9182708953376,
37.9964856422404, -392.784835061214, -58.7051083198235, -122.512710350092,
-61.4478743671992, 37.7251954611625, -392.343876245246, -56.8882955691105,
-121.673359656923, -61.4688801029411, 40.7112459013008, -391.642856249102,
-56.5273221065341, -122.013989476164, -60.9366928373805, 40.0357624923904,
-391.032796092964, -56.1986518505785, -122.165266566712, -60.5038820477298,
40.3931701211333, -392.67708030965, -57.0049181718763, -122.228319390715,
-59.6224414485488, 39.6725165922504, -392.215952780478, -57.4493919433347,
-122.213389113138, -60.8351303726086, 39.2174702635679, -391.932674157906,
-56.3780726054856, -122.503948132302, -61.3787054232504, 39.4328186016168,
-392.297950993304, -56.8373552197226, -121.93939191923, -61.30358061344,
39.3848253158972, -392.253939438399, -57.4129411677633, -122.09146237739,
-61.3930825474689, 40.0019103815829, -392.139943349336, -56.7146395845286,
-121.165986773616, -61.5760002930151, 40.548668555753, -392.552940191632,
-57.4578413737373, -121.671947297901, -62.2616742604603, 39.9070548694529,
-393.008000854072, -58.1369563682681, -121.096126602408, -61.7847418724234,
38.7760845436551, -392.953736191798, -58.3250454119958, -121.846733393779,
-61.1355890114669, 38.5401641659439, -393.053885142929, -58.1595539682482,
-121.268685942023, -60.9500757423912, 38.5524104992738, -393.514089739042,
-58.6821818298643, -121.529528321149, -60.4362365717097, 38.6790648709246,
-393.134691115631, -56.8431934924598, -122.463172944714, -60.4785870465444,
41.5696456782836, -391.689652492694, -56.778424571016, -121.974987283305,
-60.2294731353409, 40.1116139842586, -391.336952576963, -57.290971202693,
-121.865778943152, -60.8863590221112, 39.9640667462759, -391.757880546192,
-57.1265769768168, -122.308373756515, -60.1118239266407, 39.4308567417701,
-392.356886520366, -56.8656872445314, -122.584841224696, -59.9051795062624,
39.3206400641795, -392.129640970767, -56.1966620001608, -121.640964764727,
-60.7785015487909, 39.1610686946324, -392.004755905792, -56.3281717621967,
-122.011662699231, -61.0443147685162, 38.9217456540037, -392.453841110885,
-56.9679233656307, -122.870545397558, -61.4763702872744, 39.7575025643557,
-392.193816929559, -56.5423448481881, -121.403244230028, -60.5695818351292,
39.8243977182443, -391.842032286666, -56.9618238220601, -121.307786194936,
-60.6900889277035, 39.8217391899102, -391.551474174942, -57.5750992360719,
-122.159567131468, -61.5694800797976, 39.4913591072263, -392.353958216651,
-57.4392787270497, -122.257450901962, -61.0719693665193, 39.7227314595445,
-393.158012290528, -58.4082551544691, -121.607993248204, -60.9749984826105,
39.2534756983471, -393.01592634787, -58.152916983382, -122.522163149989,
-61.7282153303861, 39.0707901376129, -394.006451003858, -57.4127771623812,
-123.025173606098, -60.6822379398436, 40.3369316820385, -391.528182554896,
-57.4368206861074, -121.496276580972, -60.8916132753648, 40.1256136565728,
-391.596608057322, -56.8109997683702, -122.1546127733, -60.4472888053975,
39.7016761947304, -392.229254693365, -56.6069343061311, -121.814981229489,
-60.3686154032532, 39.5142035857403, -392.477644560827, -56.40690088613,
-121.740237188517, -60.0740165355918, 39.5904966942067, -391.341359307486,
-56.2151682295834, -122.799595845356, -60.274733755626, 39.8024930761115,
-391.701849441469, -57.5376731955874, -122.975726815376, -60.7641524019836,
39.756820246743, -393.134582801652, -56.9492708596448, -122.706017923021,
-60.9758639801964, 39.7828700574124, -392.594884587282, -56.7732595104739,
-122.275531162885, -60.49893002737, 39.8613333677432, -391.744715947439,
-57.7527109293117, -121.90904366151, -60.2702921034023, 40.0259426032409,
-392.234294618311, -57.0598933937711, -121.906549801472, -60.6754977408629,
39.4904715862888, -393.053094083358, -57.453796215868, -122.472911066973,
-60.8571248507405, 39.5723753415354, -393.506473111461, -57.625757803891,
-122.506973194034, -60.9924574633719, 39.3277500298224, -393.239897056946,
-57.2085627896463, -123.103060907557, -60.9945664671716, 40.1679264968118,
-394.052792409369, -55.8342403451934, -122.166329428183, -61.4819118304808,
39.6344010894691, -391.539006392527, -56.6815375648364, -121.837215915867,
-60.5535994118298, 40.5252787085221, -391.483328797646, -57.6524036979367,
-121.793085075127, -60.612070627521, 39.7077292541117, -392.479426842096,
-56.9682489814125, -122.648313278795, -61.1500142701836, 40.0136668260889,
-392.22338837038, -56.8408148336886, -122.606104605566, -60.2839517830592,
39.2480247625376, -392.18196111966, -57.3643209886408, -122.842869938855,
-60.6144281969126, 39.318896556351, -391.96412326862, -58.2522589647493,
-122.937081174793, -60.2349380259446, 39.8190141476524, -392.804713313324,
-57.4908996116367, -123.119864582451, -59.9457632987439, 39.6263672306684,
-393.281928924691, -57.2491227354529, -121.817165126546, -60.5036494028121,
40.0627357991879, -392.763620510632, -57.5839193077747, -122.203321986351,
-60.8981042810281, 39.4895627486883, -393.324808486894, -57.3779456480065,
-121.663611791363, -61.3113829006442, 39.6328434409623, -393.024662798763,
-57.2696373119026, -123.271343734168, -60.8774635432193, 39.0004464405458,
-392.849117627945, -57.7003152860741, -122.984915898157, -61.3292473474853,
40.0181654211569, -393.774194535259, -57.190624548492, -123.232777826485,
-60.7606083470565, 40.0155343783855, -393.903686731769, -56.6548894549038,
-121.840152000119, -61.19896403139, 40.1899474948187, -392.012852186997,
-56.6234001532473, -122.191028705712, -60.9443032879474, 40.09359206011,
-391.518640711352, -56.4954005554619, -121.425221309155, -60.6257775082322,
40.0249994841223, -391.81236836863, -57.0039761294853, -122.241007759687,
-60.6430066671999, 39.5641785215922, -392.175208329291, -56.7290358758157,
-121.624222825937, -60.129249363516, 38.6383427486633, -392.520102685267,
-56.5936616078684, -121.721498740753, -60.4308218400916, 38.6149297524634,
-392.815226321366, -57.1687659113176, -122.001005697577, -60.6851104202997,
39.5219416675945, -392.454420608275, -57.881447554133, -122.409328914603,
-60.0655062468979, 39.9173367741305, -393.202001082612, -57.4859641848568,
-122.207312876731, -60.5215138567257, 39.7343404410406, -393.194270148619,
-57.3495388706531, -122.598018999049, -60.7075827705102, 39.1956813600119,
-393.643208764506, -58.2071800421475, -122.144980387017, -60.5919103764103,
38.6268229837141, -393.886697270697, -57.4661203020432, -122.626236696765,
-61.0703786565365, 39.4574826266922, -392.984569583799, -57.5347572482781,
-123.297456522613, -61.3354797737867, 38.9017095015526, -393.759879068954,
-57.1826019076194, -123.55653542416, -60.8913526728113, 39.7900259901191,
-394.208707199429, -57.3554739846146, -121.559959129097, -61.0206933794756,
40.0275292636325, -392.270110033558, -56.692369800454, -122.621339554488,
-60.7707104188249, 39.9365471696956, -392.041328989158, -56.843936888702,
-122.28592734223, -60.8037478170418, 39.854608934565, -392.422734545917,
-57.1407298693731, -122.051139759028, -61.3205160930725, 40.2793080544455,
-392.879012891288, -56.4752071306908, -121.911970316099, -60.8187034966285,
39.0020735676659, -392.683353427027, -56.6601622294836, -121.612048163518,
-60.9143614437433, 38.9477005318394, -392.99217148055, -56.9203812035697,
-121.407455764501, -60.5916792133593, 39.5097530573285, -392.838456917664,
-57.1237256702909, -121.73241833012, -59.5961307950848, 39.9456937112319,
-393.066330109017, -57.0312432740228, -122.435126005992, -60.5585684955294,
39.5897477100166, -393.494753401824, -56.7974751424876, -122.960105788321,
-60.5660700871251, 39.2060127220491, -393.56532531253, -56.8758904248612,
-122.9317324795, -60.3556795480481, 38.9304630368855, -393.477787864323,
-57.7840733863102, -122.466182796342, -60.7189590956987, 39.0053326450422,
-393.138234622652, -57.172028361569, -123.003363009292, -61.0679864051791,
38.9807551766558, -393.546158835272, -57.1929999767468, -123.352850382368,
-61.6181519249724, 39.5142183087557, -393.643721694827, -57.2315623110534,
-122.065110247245, -60.7583264201527, 40.6854229703303, -393.551985971907,
-57.0726812126323, -122.039608714183, -60.8395753938078, 40.2350397742928,
-392.793165661315, -57.5702378542138, -121.999777351128, -61.0271306048749,
39.4868410644438, -393.177705755174, -57.0624311977852, -122.823946244428,
-60.9872181956851, 39.8618937567479, -392.843953203553, -56.6715184449334,
-121.612309398754, -60.7843309107288, 39.7905832436454, -392.878286131171,
-57.0269279884332, -121.904882231476, -60.8754987734228, 39.55165924184,
-393.287924808833, -56.9636876696692, -121.640597720208, -61.2486857984522,
40.0113928583189, -393.193587200574, -57.5972232290888, -122.672040802689,
-60.370568053928, 39.9735313867751, -393.390521885768, -57.7991153839552,
-122.580517742677, -60.4218598078979, 39.595221095069, -393.482130230155,
-57.4099141294825, -123.117008103142, -60.1000305769294, 39.6840354214756,
-393.898502704665, -57.6510960418328, -122.905166436477, -60.5914170778744,
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42.2789424289535, 90.3992284318595, 45.0023135696646, -29.0163035012765,
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90.9155694152958, 45.0528344369342, -27.1372903541449, 290.446595337488,
41.4812541120621, 89.8934921314107, 44.7099497272462, -26.7177313503607,
291.004628722112, 40.8935205044811, 89.1365101039165, 44.7409953879722,
-27.5218225543609, 290.323724318071, 41.6317163107707, 89.8364274209589,
44.8071399354407, -27.754891088401, 290.995456193176, 41.772617604047,
87.904276203224, 42.5952233943216, -29.0106420633883, 287.43066595906,
41.9651726561615, 88.8457907049987, 43.3294229816432, -29.357717428449,
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-29.7525144526853, 287.100225748793, 41.5563983121041, 89.7987379652421,
42.954867081451, -29.7418471682704, 288.232585797571, 41.6037484465877,
90.0497736697814, 44.3540120837664, -29.0363991893347, 287.438342094317,
42.5973085544504, 90.8160988103736, 44.7676479902984, -29.4117629043429,
286.925222649755, 42.2250659136255, 91.1237417754796, 44.2752409584568,
-29.4503347454545, 287.471579322834, 42.5176431000113, 90.3242716922536,
45.0582080303674, -28.6959443501187, 286.415116716921, 42.1216469500531,
90.1973832588169, 45.5806756878268, -28.4205348398918, 288.417875260666,
42.5424720619725, 90.7036867811677, 44.0882883302818, -27.0165461703629,
288.828204303, 43.0924233951718, 91.1114665907629, 44.9641981948466,
-27.7454681902188, 289.758016705061, 43.2408360089426, 90.9753466602826,
44.3472091865875, -27.4527938765916, 289.520549961924, 42.8473410625725,
90.9174763274371, 44.6567020575438, -27.9521304650399, 289.513889577836,
42.5607870969512, 90.1955873529093, 44.727269001786, -27.7772059991158,
289.430071284493, 42.0431863217333, 89.4541638438763, 45.0218174474881,
-27.1627081559249, 288.96886704886, 42.3624049219412, 89.2323865969743,
44.8466015263249, -26.315195860045, 290.382237134637, 41.5396441785647,
89.7816359143923, 44.4356673548052, -25.7921955464756, 289.473683325902,
41.3660855804197, 89.7217849436109, 45.4310133877225, -27.9027794328434,
289.718681719603), .Dim = c(5L, 520L)))
expect_equal(
recover_alpha(out, n_message = 1, constraint = "resolution"),
structure(c(-0.0590065558990034, 0.898836915156284, 0.232153019528937,
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-0.188217876616164, 0.456930788782017, -1.56128759040399, -0.865808698754677,
-1.33485493675594, -0.182915404194489, 1.16335549882447, -1.07067846399082,
-0.329229431365576, -1.00565024843392, 0.293257431829971, 1.25734860675354,
-0.753815285433944, 0.0846227388065017, -1.06188202351251, -0.275668911814364,
0.0572553903388666, -0.784550285687004, 1.10805475800635, -0.4810831817496,
-0.533923403636322, 0.623638687117051, -0.507971938558569, 0.485554928775684,
-0.272012007795311, -1.16923255594463, 0.185953144334434, 0.758304992878891,
1.48566600804006, -0.182101770139635, 0.0300025354698903, -0.247174539033722,
0.823153308712563, 1.04014517792123, -0.30196309481024, -0.854946685924574,
-0.164007170592271, 1.30907004850616, 1.03326901177303, 0.0257889379678993,
-1.00502796460361, -1.57571967026519, 1.10036422855717, 0.964178672493574,
0.390514507593675, -0.880728238222815, -1.32109854611895, 0.538405885119147,
0.620578436774252, 0.645247876007037, -0.118148050093765, -0.866217515446806,
0.396490157618842, 0.359896409920538, 1.00828993007394, -0.440460952183031,
-0.613797342174024, 0.188987851326772, 0.685068449655034, 0.0855077448629338,
-0.41673442162076, -0.81780556862509, 0.37671688739232, -0.331998808655158,
0.321965908363815, -0.163946080327605, -1.11301222814947, -0.223384566885585,
0.131484161199609, 0.0373428658519437, -0.193101174672194, -0.386849310475696,
-0.474418794608454, -0.284751012326836, -0.212056859310636, -0.851652038844435,
-0.0891301555192285, -1.15607535654718, -0.489507376188669, -0.355860680640262,
-1.03957295605258, 0.902943064101976, -0.471091050564127, -0.545676200088252,
-0.887877386425252, -0.894054846258172, 0.848299184780672, -1.17878522817267,
0.139011779585445, -0.533956362799213, -0.576078410501282, 0.758561302751588,
-1.18127255970339, -0.702658634833483, -1.05027076497971, -0.479744877584864,
1.2602667242783, -1.36561255150102, -0.685018931872548, -1.11509344933721,
-0.0135890286835831, 0.313777297840431, -2.27115180929206, 0.215212105439235,
-1.18692801740119, -0.182448992210652, -0.940130633926856, -0.978150404204428,
0.774826959661624, -1.35294932657882, -0.247499546548255, 0.198633332525731,
-1.25862920890484, 0.462575395342299, -0.598635263251634, -0.825827219628252,
-0.143588061114713, -0.47159237353992, 1.21015150173378, 0.178972116523234,
-0.0920072508409646, -1.06062834133746, 2.60462947315494, 1.07318740157663,
1.02050551404063, -1.42976268627939, -0.807242133204227, 1.6952338601032,
0.569989618724179, 0.972951621625725, -0.75512139933079, -0.718866545922566,
1.7700824002813, 0.694398190197433, 0.682014688998429, -0.0560211817035281,
-0.640014891093557, 1.60122100337028, 0.742708153401736, 0.551155987585528,
-0.276518271983264, -0.162154885576541, 1.00632221591351, 1.30195440980947,
1.12435882833288, -0.635377524382534, -0.42673343536805, 0.634367159764025,
1.20996115879123, 0.728328345132812, 0.227620819560968, 0.749977374741292,
1.30355516715872, -0.0206757398025559, 0.917547942853009, -0.0441373071143687,
0.210265062959081, 0.98711673104404, -0.338682229990354, -0.220713746266544,
-0.608381638799301, 0.222698436575826, 0.956116971899888, -0.0724079182652417,
-0.00279966914293439, -0.462278248523177, 1.150800694059, 0.274703089385156,
0.0104135580047569, -0.229800582996635, -0.500937395887121, 0.660921500392277,
-0.111173874910293, 0.0517512546748833, -0.360796032446586, -0.867175327197174,
0.248208002917238, -0.217650857489559, 0.337223855506807, -0.610812536394768,
-0.914656633443741, -0.0531929552185488, -1.57445216227393, -0.607936325547513,
-1.35113545326963, -0.938524959141859, 0.0144087690620438, -0.966035418694901,
0.241207431808704, -1.01005327048578, -0.155890591369626, -0.869618051964892,
-1.12190110972753, 0.438617570588832, -1.12354273800673, -0.504432995851914,
0.257907950696989, -0.852860756641604, 0.591331402634893, -1.63420526933004,
-0.40984989921958, -1.01640092068078, 0.0672496573932619, 0.724541048149433,
-1.11914619132559, -1.34896429931234, -0.841129049504246, 0.379642193003718,
1.15987585301559, 0.432827017253683, -0.0704807094820126, -1.18894471893296,
2.98981039740966, 1.71370705887315, 0.312680989789737, -0.271675234949385,
-0.246772661741034, 3.17536982692309, 0.485314939042723, 0.446152097042827,
-0.133883483207569, -1.2998787496972, 2.61524247642902, 0.509658530376555,
1.14076365514336, -0.0129961296911745, -0.568233837961657, 2.48222878006004,
0.474591140479532, 0.758651575904299, 0.332622630675104, -0.707093296962455,
1.68257442807794, 0.673779157793632, 1.0177535021429, 0.35581349064779,
1.14471236819821, 2.24059453000891, 0.4956048430777, 0.89332464108179,
0.834250013395177, -0.157878639078973, 1.45431768477559, 0.305099245457683,
0.325204548641523, 1.28048052599897, -0.718526473731544, 1.15764753989993,
-0.241095257737356, 0.249833593814166, 0.738056868437525, 0.700008472041901,
1.20467819336602, -0.297923852008466, -0.416971888206902, 0.54194980677255,
1.18582739816711, 1.23988857152059, -0.180841328291564, -0.0786234163794113,
0.00615287143983778, 0.659089104598991, -0.387961348423914, -0.011247536108101,
-0.595003382872594, -0.276147616131738, 0.138952913820617, -0.587593575433146,
-0.0701986385299165, -0.920310765498634, -0.678045987127206,
-0.0548015032268836, 0.0956197842231177, -0.406672858409138,
-0.788089162136171, -0.475918302946411, -0.0767468440641892,
-0.506052412951988, -0.018162929946584, -0.75612011089396, -0.549895624752054,
0.505908830030108, -0.690780760615922, -0.170334048991208, -0.641417294988699,
-0.956107762107536, 0.16732473262249, -0.399599463902433, 0.0647530105175917,
-0.322952536101919, -0.476828442398357, -0.185620055898454, -0.0442891759009569,
0.744039068251737, -1.3260441418014, -0.596417418679351, -0.201836931621585,
0.423541386390497, 0.743373223670915, 0.428870019332724, -0.21355002769775,
-1.60811748366871, 1.98204710853031, 1.26160596137106, -0.154921107695071,
-0.549371186922343, -0.82292072214716, 2.53775755878647, 2.07444320862774,
-0.403696336248061, -0.677926985173571, -1.19332459149689, 2.99055153693121,
1.90095887408268, 0.549779366780569, -0.629016163534061, -0.633720717565382,
2.56279943766489, 1.23820872416761, 0.878306916243979, 0.117756434999279,
-0.656160468537422, 1.64506467555279, 0.582072770448747, 0.973543849086298,
1.46100287712721, 0.506166332122746, 0.966363704592283, 1.12664205103624,
0.508768251489258, 1.36392519055252, 0.451144489882154, 1.05302552542315,
1.00559152431634, 0.0408412406092609, 0.965826083134061, 0.171706992013924,
1.37950114427301, -0.142232908809717, 0.0524502885615981, 0.476995581089966,
0.602694049319439, 0.839643071513734, -0.642680085744274, -0.0139812760225979,
0.174353407866448, 0.154058076782036, 0.994560689457614, 0.227598619026338,
0.122808707275777, 0.545633388715004, 0.161071619312452, -0.379705189617226,
-1.04661700973352, -0.509292067860887, 0.336672319673454, -0.0524042907718183,
0.145535795144468, -0.130278177392967, -0.449992003323537, 0.0799677648682575,
-0.427851945724939, 0.515420552930721, -0.0311054361585832, -0.401146940562274,
-0.0111838860377986, 0.464810040997406, -1.14451001481737, -0.298289572584395,
-0.808039902200122, -0.361925171725403, 0.491178763273155, -0.494950757798136,
-0.513601443358397, -0.524699484240635, -1.02769897715515, 0.806825800924372,
0.509685549242391, -0.290455195308226, 0.134083008869055, -0.901632603732992,
-0.129550073981562, 0.304558723466982, 0.508127811148427, 0.120807738475452,
-0.774611102070139, 0.216569102964712, -0.554951970866682), .Dim = c(5L,
520L)))
expect_error(recover_alpha(out, n_message = -5), "n_message must be a positive integer")
expect_error(recover_alpha(out, n_message = "aa"), "n_message must be a positive integer")
expect_error(recover_alpha(out, n_message = TRUE), "n_message must be a positive integer")
expect_error(recover_alpha(out, constraint = "adfads"), "constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"")
expect_error(recover_alpha(out, constraint = 1), "constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"")
expect_error(recover_alpha(out, constraint = "predicted"), constraint = "constraint = \"predicted\" is not currently supported -- developer note: add W_pred to function call to enable this in future results")
class(out) <- "mcmc_mra"
expect_error(recover_alpha(out), "out must be of class \"mcmc_mra_integrated\" which is the output of the function mcmc_mra_integrated()")
})
# unscale_estimates ------------------------------------------------------------
test_that("unscale_estimates", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
unscale_beta(out),
structure(c(-6.11420734746121, -6.90052639635516, -7.07895510496375,
-4.46692474389574, -6.13571374777994, -0.354422064854689, -0.403503410506908,
-0.424436653906349, -0.258765608581638, -0.365787493819805), .Dim = c(5L, 2L)))
expect_equal(
unscale_sigma2(out),
c(2.02176705952465, 0.798706141860752, 1.01269594272481, 0.535766835394645,
0.889899599677774))
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
unscale_beta(out),
structure(c(-0.669013535873582, -0.669013535873582, -0.669013535873582,
-0.669013535873582, -0.669013535873582, 0, 0, 0, 0, 0), .Dim = c(5L, 2L)))
expect_equal(
unscale_sigma2(out),
c(1.04217141145773, 1.04037731093668, 0.99985058907344, 0.781924338781402,
0.781924338781402))
class(out) <- "aaa"
expect_error(unscale_beta(out), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
expect_error(unscale_sigma2(out), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
})
jtipton25/BayesMRA documentation built on Feb. 28, 2024, 1:27 p.m.
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context("mcmc functions")
# mcmc_mra ---------------------------------------------------------------------
test_that("mcmc_mra", {
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_s3_class(out, "mcmc_mra")
expect_s3_class(out$MRA, "mra_wendland_2d")
expect_error(mcmc_mra(y, X, locs, params, M = -3), "the number of resolutions M must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_coarse_grid = -3), "n_coarse_grid must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_neighbors = -3), "n_neighbors must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_padding = -3), "n_padding must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = -3), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = 1:4), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = NA), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_cores = NULL), "n_cores must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = 1:4), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = NA), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, n_chain = NULL), "n_chain must be a positive integer")
expect_error(mcmc_mra(y, X, locs, params, use_spam = FALSE), "The Matrix package is not currently supported")
expect_error(mcmc_mra(y, X), 'argument "locs" is missing, with no default')
expect_error(mcmc_mra(y, X, locs), 'argument "params" is missing, with no default')
y <- matrix(1:10, 5, 2)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(NA, 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep("aaa", 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(TRUE, 10)
expect_error(mcmc_mra(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rnorm(10)
X <- array(0, dim=c(2, 2, 2))
expect_error(mcmc_mra(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- matrix(0, 5, 3)
expect_error(mcmc_mra(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- array(0, dim=c(10, 2, 2))
expect_error(mcmc_mra(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(NA, 10, 3)
expect_error(mcmc_mra(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(1:20, 10, 2)
locs <- matrix(0, 10, 3)
expect_error(mcmc_mra(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(NA, 10, 2)
expect_error(mcmc_mra(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(1:20, 10, 2)
params$n_mcmc <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 500
params$n_adapt <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 500
params$n_thin <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 1
params$n_message <- -10
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NA
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NULL
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- "aaa"
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- 50.5
expect_error(mcmc_mra(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message = 100
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
priors$mu_beta <- rep(0, ncol(X) + 1)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(NA, ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep("aa", ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- matrix(rep(0, ncol(X)))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(0, ncol(X))
priors$Sigma_beta <- diag(ncol(X) + 1)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(1, ncol(X), ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigam_beta <- diag(NA, ncol(X))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(rep(0, ncol(X), ncol(X)))
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- diag(ncol(X))
priors$alpha_sigma2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 1
priors$beta_sigma2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 1
priors$alpha_tau2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 1
priors$beta_tau2 <- rep(1, 2)
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- -1
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- "aa"
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 0
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- NA
expect_error(mcmc_mra(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 1
priors <- NULL
expect_error(mcmc_mra(y, X, locs, params, use_spam = "aaa"), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, use_spam = 3), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = "aaa"), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = 3), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, verbose = NA), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = "TRUE")), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = NA)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_beta = 3)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = "TRUE")), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = NA)), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_tau2 = 3)), "If specified, sample_tau2 must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = "TRUE")), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = NA)), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra(y, X, locs, params, config = list(sample_lambda = 3)), "If specified, sample_lambda must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = "TRUE")), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = NA)), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_sigma2 = 3)), "If specified, sample_sigma2 must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = "TRUE")), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = NA)), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, config = list(sample_alpha = 3)), "If specified, sample_alpha must be TRUE or FALSE")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = 3)), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep("aaa", ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep(NA, ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = matrix(1:ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = 1:3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = matrix(1:ncol(X)))), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = NA)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = -3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = 2:ncol(out$alpha))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = matrix(1:ncol(out$alpha)))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(alpha = c(2:ncol(out$alpha), NA))), "initial value for alpha must be positive numeric vector of length equal to the number of all grid points")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1:3)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = matrix(1:ncol(X)))), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = NA)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = -3)), "initial value for tau2 must be a positive numeric vector of length M")
# mcmc_mra(y, X, locs, params, priors, M = 2, n_coarse_grid = 4)
# expect_error(mcmc_mra(y, X, params), 'argument "priors" is missing, with no default')
# priors <- default_priors_pg_lm(y, X)
# out <- pg_lm(y, X, params, priors)
# expect_true(class(out) == "pg_lm")
})
# mcmc_mra_integrated ----------------------------------------------------------
test_that("mcmc_mra_integrated", {
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma = 1,
beta_sigma = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_s3_class(out, "mcmc_mra_integrated")
expect_s3_class(out$MRA, "mra_wendland_2d")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = -3), "the number of resolutions M must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_coarse_grid = -3), "n_coarse_grid must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_neighbors = -3), "n_neighbors must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_padding = -3), "n_padding must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = -3), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = 1:4), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = NA), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_cores = NULL), "n_cores must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = 1:4), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = NA), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, n_chain = NULL), "n_chain must be a positive integer")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = FALSE), "The Matrix package is not currently supported")
expect_error(mcmc_mra_integrated(y, X), 'argument "locs" is missing, with no default')
expect_error(mcmc_mra_integrated(y, X, locs), 'argument "params" is missing, with no default')
y <- matrix(1:10, 5, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(NA, 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep("aaa", 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rep(TRUE, 10)
expect_error(mcmc_mra_integrated(y, X, locs, params), "y must be a numeric vector of length N.")
y <- rnorm(10)
X <- array(0, dim=c(2, 2, 2))
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- matrix(0, 5, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must have the same number of rows as the length of y.")
X <- array(0, dim=c(10, 2, 2))
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(NA, 10, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "X must be a numeric matrix with N rows.")
X <- matrix(1:20, 10, 2)
locs <- matrix(0, 10, 3)
expect_error(mcmc_mra_integrated(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(NA, 10, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params), "locs must be a numeric matrix with N rows and 2 columns.")
locs <- matrix(1:20, 10, 2)
params$n_mcmc <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_mcmc.")
params$n_mcmc <- 10
params$n_adapt <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_adapt.")
params$n_adapt <- 10
params$n_thin <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_thin.")
params$n_thin <- 1
params$n_message <- -10
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- "aaa"
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message <- 50.5
expect_error(mcmc_mra_integrated(y, X, locs, params), "params must contain a positive integer n_message.")
params$n_message = 100
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
priors$mu_beta <- rep(0, ncol(X) + 1)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(NA, ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep("aa", ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- matrix(rep(0, ncol(X)))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter mu_beta in priors must be a numeric vector of length equal to the number of columns of X.")
priors$mu_beta <- rep(0, ncol(X))
priors$Sigma_beta <- diag(ncol(X) + 1)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(1, ncol(X), ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigam_beta <- diag(NA, ncol(X))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- matrix(rep(0, ncol(X), ncol(X)))
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter Sigma_beta in priors must be a symmetric positive definite matrix with rows and columns equal to the number of columns of X.")
priors$Sigma_beta <- diag(ncol(X))
priors$alpha_sigma2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_sigma2 in priors must be a positive numeric value.")
priors$alpha_sigma2 <- 1
priors$beta_sigma2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_sigma2 in priors must be a positive numeric value.")
priors$beta_sigma2 <- 1
priors$alpha_tau2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter alpha_tau2 in priors must be a positive numeric value.")
priors$alpha_tau2 <- 1
priors$beta_tau2 <- rep(1, 2)
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- -1
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- "aa"
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 0
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- NA
expect_error(mcmc_mra_integrated(y, X, locs, params, priors), "If specified, the parameter beta_tau2 in priors must be a positive numeric value.")
priors$beta_tau2 <- 1
priors <- NULL
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = "aaa"), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = 3), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, use_spam = NA), "use_spam must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = "aaa"), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = 3), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, verbose = NA), "verbose must be either TRUE or FALSE.")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = "TRUE")), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = NA)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_beta = 3)), "If specified, sample_beta must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = "TRUE")), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = NA)), "If specified, sample_tau2 must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_tau2 = 3)), "If specified, sample_tau2 must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = "TRUE")), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = NA)), "If specified, sample_lambda must be TRUE or FALSE")
# expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_lambda = 3)), "If specified, sample_lambda must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = "TRUE")), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = NA)), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, config = list(sample_sigma = 3)), "If specified, sample_sigma must be TRUE or FALSE")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = 3)), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep("aaa", ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = rep(NA, ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(beta = matrix(1:ncol(X)))), "initial value for beta must be a numeric vector of length p")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = 1:3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = matrix(1:ncol(X)))), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = NA)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(sigma2 = -3)), "initial value for sigma2 must be a positive numeric value")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1:3)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = 1)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = matrix(1:ncol(X)))), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = NA)), "initial value for tau2 must be a positive numeric vector of length M")
expect_error(mcmc_mra_integrated(y, X, locs, params, M = 2, n_coarse = 4, inits = list(tau2 = -3)), "initial value for tau2 must be a positive numeric vector of length M")
# mcmc_mra_integrated(y, X, locs, params, priors, M = 2, n_coarse_grid = 4)
# expect_error(mcmc_mra_integrated(y, X, params), 'argument "priors" is missing, with no default')
# priors <- default_priors_pg_lm(y, X)
# out <- pg_lm(y, X, params, priors)
# expect_true(class(out) == "pg_lm")
})
# update_tuning ----------------------------------------------------------------
test_that("update_tuning", {
# update_tuning
expect_equal(
update_tuning(50, 0.6, 0.5),
list(accept = 0, tune = 0.575954955084454))
expect_equal(
update_tuning(250, 0.6, 0.5),
list(accept = 0, tune = 0.532644196047304))
expect_equal(
update_tuning(50, 0.3, 0.5),
list(accept = 0, tune = 0.434061722697292))
expect_equal(
update_tuning(50, 0.3, 0.1),
list(accept = 0, tune = 0.0868123445394585))
# update_tuning_mv
set.seed(111)
k <- 50
accept <- 0.6
lambda <- 0.2
batch_samples <- matrix(rnorm(150), 50, 3)
Sigma_tune <- stats::rWishart(1, 5, diag(3))[, , 1]
Sigma_tune_chol <- chol(Sigma_tune)
expect_equal(
update_tuning_mv(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = 0, lambda = 0.668921761531105,
batch_samples = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), dim = c(50L, 3L)),
Sigma_tune = structure(c(2.97876608285749,
0.412648437574117, 0.432599330050228, 0.412648437574117, 3.87407264820285,
0.587518320732799, 0.432599330050228, 0.587518320732799, 4.70641828672913
), dim = c(3L, 3L)),
Sigma_tune_chol = structure(c(1.72591021865492,
0, 0, 0.239090326434078, 1.95369098477945, 0, 0.250649961611196,
0.270047998225292, 2.13791182281402), dim = c(3L, 3L))))
accept <- 0.1
expect_equal(
update_tuning_mv(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = 0, lambda = 0.12854540862658,
batch_samples = structure(c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), dim = c(50L, 3L)),
Sigma_tune = structure(c(2.97876608285749,
0.412648437574117, 0.432599330050228, 0.412648437574117, 3.87407264820285,
0.587518320732799, 0.432599330050228, 0.587518320732799, 4.70641828672913
), dim = c(3L, 3L)),
Sigma_tune_chol = structure(c(1.72591021865492,
0, 0, 0.239090326434078, 1.95369098477945, 0, 0.250649961611196,
0.270047998225292, 2.13791182281402), dim = c(3L, 3L))))
# update_tuning_vec
expect_equal(
update_tuning_vec(50, c(0.1, 0.6), c(0.5, 0.5)),
list(accept = c(0, 0), tune = c(0.434061722697292, 0.575954955084454)))
expect_equal(
update_tuning_vec(250, c(0.1, 0.6), c(0.25, 0.75)),
list(accept = c(0, 0), tune = c(0.234678235354129, 0.798966294070957)))
expect_equal(
update_tuning_vec(50, c(0.6, 0.1), c(0.5, 0.5)),
list(accept = c(0, 0), tune = c(0.575954955084454, 0.434061722697292)))
expect_equal(
update_tuning_vec(250, c(0.6, 0.1), c(0.25, 0.75)),
list(accept = c(0, 0), tune = c(0.266322098023652, 0.704034706062386)))
# update_tuning_mat
expect_equal(
update_tuning_mat(50, matrix(c(0.1, 0.6, 0.6, 0.1), 2, 2), matrix(c(0.5, 0.25, 1, 0.5), 2, 2)),
list(accept = structure(c(0, 0, 0, 0), .Dim = c(2L, 2L)), tune = structure(c(0.434061722697292,
0.287977477542227, 1.15190991016891, 0.434061722697292), .Dim = c(2L,
2L))))
# update_tuning_mv_mat
set.seed(111)
k <- 50
accept <- c(0.1, 0.6, 0.6)
lambda <- c(0.1, 0.2, 0.5)
batch_samples <- array(rnorm(50*3*4), dim = c(50, 3, 4))
Sigma_tune <- stats::rWishart(4, 5, diag(3))
Sigma_tune_chol <- Sigma_tune
for (j in 1:4) {
Sigma_tune_chol[, , j] <- chol(Sigma_tune[, , j])
}
expect_equal(
update_tuning_mv_mat(k, accept, lambda, batch_samples, Sigma_tune, Sigma_tune_chol),
list(accept = c(0, 0, 0), lambda = c(0.0483971455170536, 0.50369599627459, 1.25923999068647),
batch_samples = structure(c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), .Dim = c(50L, 3L, 4L)),
Sigma_tune = structure(c(0.861843460847109,
0.0350297118298129, -0.205904229686601, 0.0350297118298129, 5.23938194426575,
0.113512965023776, -0.205904229686601, 0.113512965023776, 1.11610391509234,
12.819834315426, 0.0307410097016759, -3.91159618091204, 0.0307410097016759,
0.606063359369128, 0.0753128883635706, -3.91159618091204, 0.0753128883635706,
3.74553987550437, 3.58825485621149, 1.11700532441496, 0.294659706303476,
1.11700532441496, 6.08365678995394, 2.20167448094668, 0.294659706303476,
2.20167448094668, 6.46870007854512, 5.0065339819057, 2.07428171851721,
-3.28335619632131, 2.07428171851721, 2.42602278480844, -0.181070805667884,
-3.28335619632131, -0.181070805667884, 4.16265859287012), .Dim = c(3L, 3L, 4L)),
Sigma_tune_chol = structure(c(0.928355244961275, 0,
0, 0.0377330898058039, 2.28865859363066, 0, -0.221794653290498,
0.0532547593310908, 1.03154009978899, 3.5804796208645, 0, 0,
0.00858572396908477, 0.778453367076702, 0, -1.09247826970388,
0.108795990628847, 1.59379877592884, 1.89426895033717, 0, 0,
0.5896762042244, 2.39498199661823, 0, 0.155553257762594, 0.880987176243409,
2.38083280761989, 2.23752854326055, 0, 0, 0.927041455969336,
1.25164568617588, 0, -1.46740304440397, 0.94217769624022, 1.05909777020423
), .Dim = c(3L, 3L, 4L))))
})
# predict_mra ------------------------------------------------------------------
test_that("predict_mra", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
new_data <- list(locs_pred = locs, X_pred = X)
# test equality here
# expect_snapshot(predict_mra(out, new_data))
# expect_equal(predict_mra(out, new_data),
new_data <- list(locs_pred = locs, X_pred = NULL)
expect_error(predict_mra(out, new_data), "new_data must be a list that contains a n_pred by p matrix of covariates in the variable X_pred which are used to make predictions")
new_data <- list(locs_pred = NULL, X_pred = X)
expect_error(predict_mra(out, new_data), "new_data must be a list that contains a n_pred by 2 matrix of locations in the variable locs_pred at which to make predictions")
new_data <- list(locs_pred = locs, X_pred = cbind(X, 1))
expect_error(predict_mra(out, new_data), "The columns of X_pred in the list new_data must be the same number as X used to fit the model")
new_data <- list(locs_pred = cbind(locs, 1), X_pred = X)
expect_error(predict_mra(out, new_data), "locs_pred must be a numeric matrix with n_pred rows and 2 columns")
new_data <- list(locs_pred = locs, X_pred = rbind(X, 1))
expect_error(predict_mra(out, new_data), "The number of rows in locs_pred in the list new_data must be equal to the number of rows in X_pred in the list new_data")
new_data <- list(locs_pred = rbind(locs, 1), X_pred = X)
expect_error(predict_mra(out, new_data), "The number of rows in locs_pred in the list new_data must be equal to the number of rows in X_pred in the list new_data")
new_data <- list(locs_pred = locs, X_pred = X)
class(out) <- "adsfads"
expect_error(predict_mra(out, new_data), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
})
# recover_alpha ----------------------------------------------------------------
test_that("mcmc_mra_integrated", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
recover_alpha(out),
structure(c(-55.7413786355926, -120.397997808002, -60.5073216554941,
40.2287465535353, -391.52340363644, -55.3071576106442, -122.06358370527,
-60.8181089915441, 41.4601513129057, -391.526798263586, -54.9296863741474,
-122.116270095828, -61.0835728999231, 40.1437395471594, -392.196851077704,
-55.6916168505625, -122.127248091819, -61.4280435091201, 40.5199697179713,
-391.905384235902, -55.7901911047978, -122.161131564182, -61.190095935052,
40.3221659849157, -392.617192170157, -55.8635555549914, -122.216548960483,
-61.345927054694, 40.6304866315159, -392.958756995231, -56.4363063530628,
-122.596423051482, -60.6971236398901, 39.8418154467378, -392.6677611016,
-57.3412518194609, -122.201538968865, -60.7619747774417, 39.4618417222475,
-392.786002151808, -57.3136943073188, -122.611427917883, -61.397726312415,
39.9426183334513, -393.398335778229, -57.6060035567961, -123.168455878075,
-62.0237382460533, 40.4983262403619, -393.776345949422, -56.7961736828601,
-121.735264447158, -60.5973404032608, 39.6977984710337, -393.414095390959,
-56.7970038511444, -120.784074139994, -59.9286986812442, 39.1937857359592,
-392.924066148986, -57.4161435785065, -121.825171683805, -60.909711245295,
38.4689700923321, -393.351106673855, -57.6660881012975, -121.029305009045,
-59.9729311242236, 38.3777796358401, -393.42345922259, -56.5144596938562,
-121.582535006642, -60.0736252229986, 40.3637117466486, -391.206590608003,
-55.8173752434951, -121.24480497185, -60.5533285258018, 41.0872850269738,
-391.727086402727, -56.3937905872642, -122.005181333795, -60.1937860689367,
39.8841017985187, -391.959256928297, -56.5049496636514, -122.337780360058,
-61.0326467477304, 40.4201702135741, -391.739778106998, -56.0554384768086,
-122.114331084741, -61.4388914075504, 40.5588103286877, -392.566478425026,
-56.6606712675465, -121.976204908192, -60.957100057498, 40.3149781747688,
-392.257655203355, -57.2491008291326, -122.29464579614, -61.2494000057945,
39.76817389636, -392.701816453718, -57.6969889710255, -122.641799764267,
-61.6881055887692, 39.0872435271544, -393.010121852504, -57.2131158081545,
-122.242500630303, -61.251390929922, 40.1000202984715, -392.199539892977,
-56.9103294931197, -122.013944620863, -61.3808974892973, 40.3708922513825,
-393.464404440682, -57.2106425200307, -122.110955945347, -60.7176609536074,
39.3896937650642, -393.451999029011, -57.7036638101613, -121.627796754553,
-61.0618271348651, 38.9499788573248, -392.676963045824, -57.992214496565,
-122.326174541915, -60.4175054890317, 38.9742143977396, -393.196086930595,
-57.7833312192903, -121.556731010002, -60.845007674096, 38.7821426498925,
-392.843045636403, -56.7210564549941, -121.505839367115, -61.1093041207737,
39.1799868835695, -391.88985308275, -56.2811046614376, -121.858181456847,
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44.2570552948075, -28.7744443986328, 289.147081072408, 40.9404393518271,
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42.2049345634514, 89.5065160989765, 43.9695318727516, -28.6588525595538,
287.520666769271, 41.7986452212667, 89.6098941160021, 44.291288353136,
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44.7341664705098, -28.899920225161, 287.626188032235, 41.7022751282514,
89.35190993102, 43.7767213415923, -29.6970315911398, 287.834856102149,
41.7969825131045, 88.9755347391008, 44.0548680346983, -29.9701996161593,
286.99065898773, 42.0019621502001, 89.3206401693718, 44.3047381743666,
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44.3412804159053, -29.2070961074218, 287.477437538075, 42.4583550496007,
90.2087251716765, 43.9287210532202, -29.04654678816, 288.799622499371,
41.8588246664774, 90.6424490099416, 44.1905653171006, -28.5988624191791,
289.627938041152, 41.6388754872571, 89.9950175089138, 44.5787842968474,
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44.1627426200205, -28.5586805573901, 289.025725155731, 41.1990598693954,
89.646698731141, 44.5501288610717, -29.1607723949443, 288.295100281249,
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90.210214585859, 44.7038451069404, -29.1560828464024, 288.155639866779,
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289.211350070425, 42.3846361426724, 89.5119927514928, 44.9360115231215,
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89.5704569609676, 44.3746663791863, -28.9055341026452, 288.834905920022,
42.2268269750588, 89.2811651562903, 44.3865186205971, -28.8327404166371,
288.933170090272, 42.1275863842134, 88.8640888247894, 44.949604212256,
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44.148174468347, -29.2232326570498, 289.491232005268, 42.044625478467,
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288.850152429945, 42.8062026005639, 88.6683614097796, 44.0406595689658,
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88.9391261741453, 43.3944124480623, -29.9352333920221, 286.573137220636,
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287.75677471085, 43.1912897673557, 89.694140719575, 44.3377476320943,
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288.689615281874, 42.0056564875712, 88.6818596641482, 44.0261134434258,
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43.3366265865803, -27.8127272328093, 288.937825933568, 42.2496426953755,
88.5628443438997, 44.0216316268108, -29.0482428426523, 286.802249004378,
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287.182161138531, 41.9925768403935, 89.526477480548, 43.9382528157959,
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43.8340292163382, -30.5966120040507, 286.110765104719, 42.1791052152751,
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288.073428625559, 42.2547261422719, 89.8034893060336, 44.2795994085175,
-29.926816016284, 287.127888889439, 42.6599696541103, 89.5494370461459,
44.8329144356885, -29.5653436225176, 287.984051079098, 42.6442803038019,
90.3889569255547, 44.6916313330266, -29.8262835776579, 287.778058247382,
42.6771936293793, 89.3541505445801, 45.3832546577624, -29.1381374873573,
287.896628591824, 42.3110416777691, 89.9844389225496, 44.3452091272422,
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44.5853821492529, -29.4084055321829, 288.029269420557, 42.2402945763274,
89.1765243600442, 44.7725865232393, -28.4933292820007, 288.902343876505,
42.8275393096528, 88.9637913201072, 44.5759755060267, -28.708920557485,
289.762637567775, 42.3663380592327, 89.6500810658328, 45.0280666327485,
-29.1674262331136, 289.626404376376, 41.5319447502563, 89.379239441623,
44.3739405433209, -28.468139330185, 289.254711086597, 40.9201261634033,
89.1548619394247, 44.2818388240819, -27.9015647543453, 289.628287332552,
41.8727290025952, 89.5626028287539, 43.9191395115073, -27.6357122000307,
290.131448237165, 42.1842705371361, 88.7760600574368, 43.6186801691212,
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44.0431672068023, -29.2372597627718, 287.68919971302, 42.1640837175571,
89.5876148010596, 43.5691364512267, -29.8148481658529, 287.735923926077,
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287.051163178478, 42.2436176402524, 90.3511726843322, 43.9479115294517,
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44.5610374001677, -29.1478941081661, 286.627958814678, 42.27283779101,
90.6526624295439, 44.3509515524041, -29.140144742216, 287.175614592777,
42.2789424289535, 90.3992284318595, 45.0023135696646, -29.0163035012765,
288.006848424896, 42.1778957662214, 90.1521001123269, 44.8200872042973,
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44.560922502648, -28.4384056180071, 288.990594880762, 42.8581942552914,
89.4199847692768, 44.1349878248502, -27.800697930469, 289.297724292411,
42.6881334863859, 89.7464724632007, 44.631842040248, -27.7347582731838,
288.546796203899, 42.7385224379141, 89.5705338884016, 44.6863564002427,
-27.5462737615351, 289.096826792161, 42.2306562368247, 89.8724412442723,
44.329000383186, -27.44467225096, 290.076567459037, 41.7569837854926,
90.9155694152958, 45.0528344369342, -27.1372903541449, 290.446595337488,
41.4812541120621, 89.8934921314107, 44.7099497272462, -26.7177313503607,
291.004628722112, 40.8935205044811, 89.1365101039165, 44.7409953879722,
-27.5218225543609, 290.323724318071, 41.6317163107707, 89.8364274209589,
44.8071399354407, -27.754891088401, 290.995456193176, 41.772617604047,
87.904276203224, 42.5952233943216, -29.0106420633883, 287.43066595906,
41.9651726561615, 88.8457907049987, 43.3294229816432, -29.357717428449,
288.672768053031, 41.5175654214072, 89.8216366303271, 42.8328973287908,
-29.7525144526853, 287.100225748793, 41.5563983121041, 89.7987379652421,
42.954867081451, -29.7418471682704, 288.232585797571, 41.6037484465877,
90.0497736697814, 44.3540120837664, -29.0363991893347, 287.438342094317,
42.5973085544504, 90.8160988103736, 44.7676479902984, -29.4117629043429,
286.925222649755, 42.2250659136255, 91.1237417754796, 44.2752409584568,
-29.4503347454545, 287.471579322834, 42.5176431000113, 90.3242716922536,
45.0582080303674, -28.6959443501187, 286.415116716921, 42.1216469500531,
90.1973832588169, 45.5806756878268, -28.4205348398918, 288.417875260666,
42.5424720619725, 90.7036867811677, 44.0882883302818, -27.0165461703629,
288.828204303, 43.0924233951718, 91.1114665907629, 44.9641981948466,
-27.7454681902188, 289.758016705061, 43.2408360089426, 90.9753466602826,
44.3472091865875, -27.4527938765916, 289.520549961924, 42.8473410625725,
90.9174763274371, 44.6567020575438, -27.9521304650399, 289.513889577836,
42.5607870969512, 90.1955873529093, 44.727269001786, -27.7772059991158,
289.430071284493, 42.0431863217333, 89.4541638438763, 45.0218174474881,
-27.1627081559249, 288.96886704886, 42.3624049219412, 89.2323865969743,
44.8466015263249, -26.315195860045, 290.382237134637, 41.5396441785647,
89.7816359143923, 44.4356673548052, -25.7921955464756, 289.473683325902,
41.3660855804197, 89.7217849436109, 45.4310133877225, -27.9027794328434,
289.718681719603), .Dim = c(5L, 520L)))
expect_equal(
recover_alpha(out, n_message = 1, constraint = "resolution"),
structure(c(-0.0590065558990034, 0.898836915156284, 0.232153019528937,
-0.0506496962755136, -0.140352558040234, 0.872336720886778, 0.571905351822149,
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-0.788089162136171, -0.475918302946411, -0.0767468440641892,
-0.506052412951988, -0.018162929946584, -0.75612011089396, -0.549895624752054,
0.505908830030108, -0.690780760615922, -0.170334048991208, -0.641417294988699,
-0.956107762107536, 0.16732473262249, -0.399599463902433, 0.0647530105175917,
-0.322952536101919, -0.476828442398357, -0.185620055898454, -0.0442891759009569,
0.744039068251737, -1.3260441418014, -0.596417418679351, -0.201836931621585,
0.423541386390497, 0.743373223670915, 0.428870019332724, -0.21355002769775,
-1.60811748366871, 1.98204710853031, 1.26160596137106, -0.154921107695071,
-0.549371186922343, -0.82292072214716, 2.53775755878647, 2.07444320862774,
-0.403696336248061, -0.677926985173571, -1.19332459149689, 2.99055153693121,
1.90095887408268, 0.549779366780569, -0.629016163534061, -0.633720717565382,
2.56279943766489, 1.23820872416761, 0.878306916243979, 0.117756434999279,
-0.656160468537422, 1.64506467555279, 0.582072770448747, 0.973543849086298,
1.46100287712721, 0.506166332122746, 0.966363704592283, 1.12664205103624,
0.508768251489258, 1.36392519055252, 0.451144489882154, 1.05302552542315,
1.00559152431634, 0.0408412406092609, 0.965826083134061, 0.171706992013924,
1.37950114427301, -0.142232908809717, 0.0524502885615981, 0.476995581089966,
0.602694049319439, 0.839643071513734, -0.642680085744274, -0.0139812760225979,
0.174353407866448, 0.154058076782036, 0.994560689457614, 0.227598619026338,
0.122808707275777, 0.545633388715004, 0.161071619312452, -0.379705189617226,
-1.04661700973352, -0.509292067860887, 0.336672319673454, -0.0524042907718183,
0.145535795144468, -0.130278177392967, -0.449992003323537, 0.0799677648682575,
-0.427851945724939, 0.515420552930721, -0.0311054361585832, -0.401146940562274,
-0.0111838860377986, 0.464810040997406, -1.14451001481737, -0.298289572584395,
-0.808039902200122, -0.361925171725403, 0.491178763273155, -0.494950757798136,
-0.513601443358397, -0.524699484240635, -1.02769897715515, 0.806825800924372,
0.509685549242391, -0.290455195308226, 0.134083008869055, -0.901632603732992,
-0.129550073981562, 0.304558723466982, 0.508127811148427, 0.120807738475452,
-0.774611102070139, 0.216569102964712, -0.554951970866682), .Dim = c(5L,
520L)))
expect_error(recover_alpha(out, n_message = -5), "n_message must be a positive integer")
expect_error(recover_alpha(out, n_message = "aa"), "n_message must be a positive integer")
expect_error(recover_alpha(out, n_message = TRUE), "n_message must be a positive integer")
expect_error(recover_alpha(out, constraint = "adfads"), "constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"")
expect_error(recover_alpha(out, constraint = 1), "constraint must be either \"unconstrained\", \"overall\", \"resolution\", or \"predicted\"")
expect_error(recover_alpha(out, constraint = "predicted"), constraint = "constraint = \"predicted\" is not currently supported -- developer note: add W_pred to function call to enable this in future results")
class(out) <- "mcmc_mra"
expect_error(recover_alpha(out), "out must be of class \"mcmc_mra_integrated\" which is the output of the function mcmc_mra_integrated()")
})
# unscale_estimates ------------------------------------------------------------
test_that("unscale_estimates", {
set.seed(111)
y <- rnorm(10)
X <- matrix(1:20, 10, 2)
params <- list(
n_mcmc = 5,
n_adapt = 5,
n_thin = 1,
n_message = 5
)
locs <- matrix(1:20, 10, 2)
priors <- list(
mu_beta = rep(0, ncol(X)),
Sigma_beta = diag(ncol(X)),
alpha_sigma2 = 1,
beta_sigma2 = 1,
alpha_tau2 = 1,
beta_tau2 = 1
)
inits <- list(
beta = rep(0, ncol(X)),
sigma2 = 1,
alpha = rep(0, 520), ## use M = 2 and n_coarse_grid = 4
tau2 = 1:2
)
out <- mcmc_mra(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
unscale_beta(out),
structure(c(-6.11420734746121, -6.90052639635516, -7.07895510496375,
-4.46692474389574, -6.13571374777994, -0.354422064854689, -0.403503410506908,
-0.424436653906349, -0.258765608581638, -0.365787493819805), .Dim = c(5L, 2L)))
expect_equal(
unscale_sigma2(out),
c(2.02176705952465, 0.798706141860752, 1.01269594272481, 0.535766835394645,
0.889899599677774))
out <- mcmc_mra_integrated(y, X, locs, params, priors, inits = inits, M = 2, n_coarse_grid = 4)
expect_equal(
unscale_beta(out),
structure(c(-0.669013535873582, -0.669013535873582, -0.669013535873582,
-0.669013535873582, -0.669013535873582, 0, 0, 0, 0, 0), .Dim = c(5L, 2L)))
expect_equal(
unscale_sigma2(out),
c(1.04217141145773, 1.04037731093668, 0.99985058907344, 0.781924338781402,
0.781924338781402))
class(out) <- "aaa"
expect_error(unscale_beta(out), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
expect_error(unscale_sigma2(out), "out must be of class \"mcmc_mra\" or \"mcmc_mra_integrated\"")
})
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