Nothing
context("Tests for log_lik helper functions")
test_that("log_lik for location shift models works as expected", {
ns <- 25
prep <- structure(list(), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(ns * 2), ncol = 2),
sigma = rchisq(ns, 3), nu = rgamma(ns, 4)
)
prep$family <- gaussian()
prep$family$fun <- "gaussian"
prep$data <- list(Y = rnorm(ns))
ll_gaussian <- dnorm(
x = prep$data$Y[1], mean = prep$dpars$mu[, 1],
sd = prep$dpars$sigma, log = TRUE
)
ll <- brms:::log_lik_gaussian(1, prep = prep)
expect_equal(ll, ll_gaussian)
ll_student <- dstudent_t(
x = prep$data$Y[2], df = prep$dpars$nu,
mu = prep$dpars$mu[, 2],
sigma = prep$dpars$sigma, log = TRUE
)
ll <- brms:::log_lik_student(2, prep = prep)
expect_equal(ll, ll_student)
# also test weighting
prep$data$weights <- sample(1:10, ns, replace = TRUE)
ll <- brms:::log_lik_gaussian(1, prep = prep)
expect_equal(ll, ll_gaussian * prep$data$weights[1])
})
test_that("log_lik for various skewed normal models works as expected", {
ns <- 50
prep <- structure(list(), class = "brmsprep")
prep$dpars <- list(
sigma = rchisq(ns, 3), beta = rchisq(ns, 3),
mu = matrix(rnorm(ns*2), ncol = 2),
alpha = rnorm(ns), ndt = 1
)
prep$data <- list(Y = rlnorm(ns))
ll_lognormal <- dlnorm(
x = prep$data$Y[1], mean = prep$dpars$mu[, 1],
sd = prep$dpars$sigma, log = TRUE
)
ll <- brms:::log_lik_lognormal(1, prep = prep)
expect_equal(ll, ll_lognormal)
ll_shifted_lognormal <- dshifted_lnorm(
x = prep$data$Y[1], mean = prep$dpars$mu[, 1],
sd = prep$dpars$sigma, shift = prep$dpars$ndt, log = TRUE
)
ll <- brms:::log_lik_shifted_lognormal(1, prep = prep)
expect_equal(ll, ll_shifted_lognormal)
ll_exgaussian <- dexgaussian(
x = prep$data$Y[1], mu = prep$dpars$mu[, 1],
sigma = prep$dpars$sigma, beta = prep$dpars$beta, log = TRUE
)
ll <- brms:::log_lik_exgaussian(1, prep = prep)
expect_equal(ll, ll_exgaussian)
ll_skew_normal <- dskew_normal(
x = prep$data$Y[1], mu = prep$dpars$mu[, 1],
sigma = prep$dpars$sigma, alpha = prep$dpars$alpha, log = TRUE
)
ll <- as.numeric(brms:::log_lik_skew_normal(1, prep = prep))
expect_equal(ll, ll_skew_normal)
})
test_that("log_lik of aysm_laplace models runs without errors", {
ns <- 50
prep <- structure(list(), class = "brmsprep")
prep$dpars <- list(
sigma = rchisq(ns, 3),
quantile = rbeta(ns, 2, 1),
mu = matrix(rnorm(ns*2), ncol = 2),
zi = rbeta(ns, 10, 10)
)
prep$data <- list(Y = brms:::rasym_laplace(ns))
ll <- brms:::log_lik_asym_laplace(1, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_zero_inflated_asym_laplace(1, prep = prep)
expect_equal(length(ll), ns)
})
test_that("log_lik for multivariate linear models runs without errors", {
ns <- 10
nvars <- 3
ncols <- 4
nobs <- nvars * ncols
prep <- structure(list(), class = "mvbrmsprep")
Sigma = array(cov(matrix(rnorm(300), ncol = 3)), dim = c(3, 3, 10))
prep$mvpars <- list(
Mu = array(rnorm(ns*nobs*nvars), dim = c(ns, nobs, nvars)),
Sigma = aperm(Sigma, c(3, 1, 2))
)
prep$dpars <- list(nu = rgamma(ns, 5))
prep$ndraws <- ns
prep$data <- list(Y = matrix(rnorm(nobs), ncol = nvars))
ll <- brms:::log_lik_gaussian_mv(1, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_student_mv(2, prep = prep)
expect_equal(length(ll), ns)
})
test_that("log_lik for ARMA models runs without errors", {
ns <- 20
nobs <- 15
prep <- structure(list(ndraws = ns), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(ns*nobs), ncol = nobs),
sigma = rchisq(ns, 3),
nu = rgamma(ns, 5) + 15
)
prep$ac <- list(
ar = matrix(rbeta(ns, 0.5, 0.5), ncol = 1),
ma = matrix(rbeta(ns, 0.2, 1), ncol = 1),
begin_tg = 2, end_tg = 5
)
prep$data <- list(Y = rnorm(nobs), se = rgamma(ns, 10))
prep$family$fun <- "gaussian_time"
ll <- brms:::log_lik_gaussian_time(1, prep = prep)
expect_equal(dim(ll), c(ns, 4))
prep$family$fun <- "student_time"
ll <- brms:::log_lik_student_time(1, prep = prep)
expect_equal(dim(ll), c(ns, 4))
})
test_that("log_lik for SAR models runs without errors", {
prep <- structure(list(ndraws = 3, nobs = 10), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(30), nrow = 3),
nu = rep(10, 3),
sigma = rep(10, 3)
)
prep$ac <- list(
lagsar = matrix(c(0.3, 0.5, 0.7)),
Msar = diag(10)
)
prep$data <- list(Y = rnorm(10))
ll <- brms:::log_lik_gaussian_lagsar(1, prep = prep)
expect_equal(dim(ll), c(3, 10))
ll <- brms:::log_lik_student_lagsar(1, prep = prep)
expect_equal(dim(ll), c(3, 10))
prep$ac$errorsar <- prep$ac$lagsar
prep$ac$lagsar <- NULL
ll <- brms:::log_lik_gaussian_errorsar(1, prep = prep)
expect_equal(dim(ll), c(3, 10))
ll <- brms:::log_lik_student_errorsar(1, prep = prep)
expect_equal(dim(ll), c(3, 10))
})
test_that("log_lik for FCOR models runs without errors", {
ns <- 3
nobs <- 10
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(nobs * ns), nrow = ns),
sigma = rep(1, ns),
nu = rep(10, ns)
)
prep$ac <- list(Mfcor = diag(nobs))
prep$data$Y <- rnorm(nobs)
ll <- brms:::log_lik_gaussian_fcor(1, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
ll <- brms:::log_lik_student_fcor(1, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
})
test_that("log_lik for count and survival models works correctly", {
ns <- 25
nobs <- 10
trials <- sample(10:30, nobs, replace = TRUE)
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
eta = matrix(rnorm(ns*nobs), ncol = nobs),
shape = rgamma(ns, 4),
xi = runif(ns, -1, 0.5),
phi = rgamma(ns, 1)
)
prep$dpars$sigma <- 1 / prep$dpars$shape
prep$dpars$nu <- prep$dpars$shape + 1
prep$data <- list(
Y = rbinom(nobs, size = trials, prob = rbeta(nobs, 1, 1)),
trials = trials
)
i <- sample(nobs, 1)
prep$dpars$mu <- brms:::inv_logit(prep$dpars$eta)
ll_binom <- dbinom(
x = prep$data$Y[i], prob = prep$dpars$mu[, i],
size = prep$data$trials[i], log = TRUE
)
ll <- brms:::log_lik_binomial(i, prep = prep)
expect_equal(ll, ll_binom)
ll_beta_binom <- dbeta_binomial(
x = prep$data$Y[i], size = prep$data$trials[i],
mu = prep$dpars$mu[, i], phi = prep$dpars$phi, log = TRUE
)
ll <- brms:::log_lik_beta_binomial(i, prep = prep)
expect_equal(ll, ll_beta_binom)
# don't test the actual values as they will be -Inf for this data
ll <- brms:::log_lik_discrete_weibull(i, prep = prep)
expect_equal(length(ll), ns)
prep$dpars$mu <- exp(prep$dpars$eta)
ll_pois <- dpois(
x = prep$data$Y[i], lambda = prep$dpars$mu[, i], log = TRUE
)
ll <- brms:::log_lik_poisson(i, prep = prep)
expect_equal(ll, ll_pois)
ll_nbinom <- dnbinom(
x = prep$data$Y[i], mu = prep$dpars$mu[, i],
size = prep$dpars$shape, log = TRUE
)
ll <- brms:::log_lik_negbinomial(i, prep = prep)
expect_equal(ll, ll_nbinom)
ll <- brms:::log_lik_negbinomial2(i, prep = prep)
expect_equal(ll, ll_nbinom)
ll_geo <- dnbinom(
x = prep$data$Y[i], mu = prep$dpars$mu[, i],
size = 1, log = TRUE
)
ll <- brms:::log_lik_geometric(i, prep = prep)
expect_equal(ll, ll_geo)
ll_com_pois <- brms:::dcom_poisson(
x = prep$data$Y[i], mu = prep$dpars$mu[, i],
shape = prep$dpars$shape, log = TRUE
)
ll <- brms:::log_lik_com_poisson(i, prep = prep)
expect_equal(ll, ll_com_pois)
ll_exp <- dexp(
x = prep$data$Y[i], rate = 1 / prep$dpars$mu[, i], log = TRUE
)
ll <- brms:::log_lik_exponential(i, prep = prep)
expect_equal(ll, ll_exp)
ll_gamma <- dgamma(
x = prep$data$Y[i], shape = prep$dpars$shape,
scale = prep$dpars$mu[, i] / prep$dpars$shape,
log = TRUE
)
ll <- brms:::log_lik_gamma(i, prep = prep)
expect_equal(ll, ll_gamma)
scale <- prep$dpars$mu[, i] / gamma(1 - 1 / prep$dpars$nu)
ll_frechet <- dfrechet(
x = prep$data$Y[i], shape = prep$dpars$nu,
scale = scale, log = TRUE
)
ll <- brms:::log_lik_frechet(i, prep = prep)
expect_equal(ll, ll_frechet)
ll_invgauss <- dinv_gaussian(
x = prep$data$Y[i], shape = prep$dpars$shape,
mu = prep$dpars$mu[, i], log = TRUE
)
ll <- brms:::log_lik_inverse.gaussian(i, prep = prep)
expect_equal(ll, ll_invgauss)
ll_weibull <- dweibull(
x = prep$data$Y[i], shape = prep$dpars$shape,
scale = prep$dpars$mu[, i] / gamma(1 + 1 / prep$dpars$shape),
log = TRUE
)
ll <- brms:::log_lik_weibull(i, prep = prep)
expect_equal(ll, c(ll_weibull))
# keep test at the end
prep$family$link <- "identity"
prep$data$Y[i] <- 0
ll_gen_extreme_value <- SW(dgen_extreme_value(
x = prep$data$Y[i], mu = prep$dpars$mu[, i],
sigma = prep$dpars$sigma, xi = prep$dpars$xi, log = TRUE
))
ll <- SW(brms:::log_lik_gen_extreme_value(i, prep = prep))
expect_equal(ll, ll_gen_extreme_value)
})
test_that("log_lik for bernoulli and beta models works correctly", {
ns <- 15
nobs <- 10
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = brms:::inv_logit(matrix(rnorm(ns * nobs * 2), ncol = nobs * 2)),
phi = rgamma(ns, 4)
)
prep$data <- list(Y = sample(0:1, nobs, replace = TRUE))
i <- sample(1:nobs, 1)
ll_bern <- dbinom(
x = prep$data$Y[i], prob = prep$dpars$mu[, i], size = 1, log = TRUE
)
ll <- brms:::log_lik_bernoulli(i, prep = prep)
expect_equal(ll, ll_bern)
prep$data <- list(Y = rbeta(nobs, 1, 1))
ll_beta <- dbeta(
x = prep$data$Y[i], shape1 = prep$dpars$mu[, i] * prep$dpars$phi,
shape2 = (1 - prep$dpars$mu[, i]) * prep$dpars$phi, log = TRUE
)
ll <- brms:::log_lik_beta(i, prep = prep)
expect_equal(ll, ll_beta)
})
test_that("log_lik for circular models runs without errors", {
ns <- 15
nobs <- 10
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = 2 * atan(matrix(rnorm(ns * nobs * 2), ncol = nobs * 2)),
kappa = rgamma(ns, 4)
)
prep$data <- list(Y = runif(nobs, -pi, pi))
i <- sample(seq_len(nobs), 1)
ll <- brms:::log_lik_von_mises(i, prep = prep)
expect_equal(length(ll), ns)
prep$data$cens <- sample(-1:1, nobs, TRUE)
ll <- brms:::log_lik_von_mises(i, prep = prep)
expect_equal(length(ll), ns)
})
test_that("log_lik for zero-inflated and hurdle models runs without erros", {
ns <- 50
nobs <- 8
trials <- sample(10:30, nobs, replace = TRUE)
resp <- rbinom(nobs, size = trials, prob = rbeta(nobs, 1, 1))
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
eta = matrix(rnorm(ns*nobs), ncol = nobs),
shape = rgamma(ns, 4),
phi = rgamma(ns, 1),
zi = rbeta(ns, 1, 1),
coi = rbeta(ns, 5, 7)
)
prep$dpars$hu <- prep$dpars$zoi <- prep$dpars$zi
prep$data <- list(Y = c(resp, rep(0, 4)), trials = trials)
prep$dpars$mu <- exp(prep$dpars$eta)
ll <- brms:::log_lik_hurdle_poisson(1, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_hurdle_negbinomial(5, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_hurdle_gamma(2, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_hurdle_gamma(8, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_zero_inflated_poisson(3, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_zero_inflated_negbinomial(6, prep = prep)
expect_equal(length(ll), ns)
prep$dpars$mu <- brms:::inv_logit(prep$dpars$eta)
ll <- brms:::log_lik_zero_inflated_binomial(4, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_zero_inflated_beta_binomial(7, prep = prep)
expect_equal(length(ll), ns)
prep$data$Y[1:nobs] <- rbeta(nobs / 2, 0.5, 4)
ll <- brms:::log_lik_zero_inflated_beta(6, prep = prep)
expect_equal(length(ll), ns)
ll <- brms:::log_lik_zero_one_inflated_beta(7, prep = prep)
expect_equal(length(ll), ns)
})
test_that("log_lik for ordinal models runs without erros", {
ns <- 50
nobs <- 8
nthres <- 3
ncat <- nthres + 1
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = array(rnorm(ns * nobs), dim = c(ns, nobs)),
disc = rexp(ns),
hu = rbeta(ns, 1, 1)
)
prep$thres$thres <- array(0, dim = c(ns, nthres))
prep$data <- list(Y = rep(1:ncat, 2), ncat = ncat)
prep$family$link <- "logit"
ll <- sapply(1:nobs, brms:::log_lik_cumulative, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
ll <- sapply(1:nobs, brms:::log_lik_sratio, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
ll <- sapply(1:nobs, brms:::log_lik_cratio, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
ll <- sapply(1:nobs, brms:::log_lik_acat, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$family$link <- "probit"
ll <- sapply(1:nobs, brms:::log_lik_acat, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
ll <- brms:::log_lik_hurdle_cumulative(3, prep = prep)
expect_equal(length(ll), ns)
})
test_that("log_lik for categorical and related models runs without erros", {
ns <- 50
nobs <- 8
ncat <- 3
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu1 = array(rnorm(ns*nobs), dim = c(ns, nobs)),
mu2 = array(rnorm(ns*nobs), dim = c(ns, nobs))
)
prep$data <- list(Y = rep(1:ncat, 2), ncat = ncat)
prep$family <- categorical()
prep$refcat <- 1
ll <- sapply(1:nobs, brms:::log_lik_categorical, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$data$Y <- matrix(
sample(1:20, nobs * ncat, TRUE),
nrow = nobs, ncol = ncat
)
prep$data$trials <- sample(1:20, nobs)
prep$family <- multinomial()
ll <- sapply(1:nobs, brms:::log_lik_multinomial, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$data$Y <- prep$data$Y / rowSums(prep$data$Y)
prep$dpars$phi <- rexp(ns, 10)
prep$family <- dirichlet()
ll <- sapply(1:nobs, brms:::log_lik_dirichlet, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$family <- brmsfamily("dirichlet2")
prep$dpars$mu1 <- rexp(ns, 10)
prep$dpars$mu2 <- rexp(ns, 10)
prep$dpars$mu3 <- rexp(ns, 10)
ll <- sapply(1:nobs, brms:::log_lik_dirichlet2, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$family <- brmsfamily("logistic_normal")
prep$dpars <- list(
mu2 = rnorm(ns),
mu3 = rnorm(ns),
sigma2 = rexp(ns, 10),
sigma3 = rexp(ns, 10)
)
prep$lncor <- rbeta(ns, 2, 1)
ll <- sapply(1:nobs, brms:::log_lik_logistic_normal, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
})
test_that("censored and truncated log_lik run without errors", {
ns <- 30
nobs <- 3
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(ns * nobs), ncol = nobs),
sigma = rchisq(ns, 3)
)
prep$data <- list(Y = rnorm(ns), cens = c(-1,0,1))
ll <- sapply(1:nobs, brms:::log_lik_gaussian, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
prep$data <- list(Y = sample(-3:3, nobs), lb = -4, ub = 5)
ll <- sapply(1:nobs, brms:::log_lik_gaussian, prep = prep)
expect_equal(dim(ll), c(ns, nobs))
})
test_that("log_lik for the wiener diffusion model runs without errors", {
ns <- 5
nobs <- 3
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rnorm(ns * nobs), ncol = nobs),
bs = rchisq(ns, 3), ndt = rep(0.5, ns),
bias = rbeta(ns, 1, 1)
)
prep$data <- list(Y = abs(rnorm(ns)) + 0.5, dec = c(1, 0, 1))
i <- sample(1:nobs, 1)
expect_equal(length(brms:::log_lik_wiener(i, prep)), ns)
})
test_that("log_lik_custom runs without errors", {
ns <- 15
nobs <- 10
prep <- structure(list(ndraws = ns, nobs = nobs), class = "brmsprep")
prep$dpars <- list(
mu = matrix(rbeta(ns * nobs * 2, 1, 1), ncol = nobs * 2)
)
prep$data <- list(
Y = sample(0:1, nobs, replace = TRUE),
trials = rep(1, nobs)
)
prep$family <- custom_family(
"beta_binomial2", dpars = c("mu", "tau"),
links = c("logit", "log"), lb = c(NA, 0),
type = "int", vars = "trials[n]"
)
log_lik_beta_binomial2 <- function(i, prep) {
mu <- prep$dpars$mu[, i]
dbinom(prep$data$Y[i], size = prep$data$trials[i], prob = mu)
}
expect_equal(length(brms:::log_lik_custom(sample(1:nobs, 1), prep)), ns)
})
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