Nothing
if (interactive()) options(mc.cores = parallel::detectCores())
ITER <- 600
CHAINS <- 2
SEED <- 9999
TOL <- 0.2 # %
TOL_df <- .25 # %
gp_sigma <- 0.2
sigma <- 0.1
df <- 1000
gp_theta <- 1.2
n_draws <- 15
nknots <- 8
n_data_points <- 50
test_that("predict.glmmfields works", {
skip_on_cran()
skip_on_travis()
skip_on_appveyor()
set.seed(SEED)
s <- sim_glmmfields(
df = df, n_draws = n_draws, gp_theta = gp_theta,
gp_sigma = gp_sigma, sd_obs = sigma, n_knots = nknots, n_data_points = n_data_points
)
suppressWarnings({
m <- glmmfields(y ~ 0,
data = s$dat, time = "time",
lat = "lat", lon = "lon", nknots = nknots,
iter = ITER, chains = CHAINS, seed = SEED,
estimate_df = FALSE, fixed_df_value = df
)
})
p <- predict(m)
p_newdata <- predict(m, newdata = s$dat, offset = rep(0, nrow(s$dat)))
p_newdata2 <- predict(m, newdata = m$data, offset = rep(0, nrow(s$dat)))
plot(s$dat$y, p$estimate)
plot(s$dat$y, p_newdata$estimate)
plot(s$dat$y, p_newdata2$estimate)
expect_identical(p, p_newdata)
expect_gte(cor(s$dat$y, p$estimate), 0.75)
expect_gte(cor(s$dat$y, p_newdata$estimate), 0.75)
expect_gte(cor(s$dat$y, p_newdata2$estimate), 0.75)
# with a subset of data
random_subset <- sample(seq_len(nrow(s$dat)), size = 200)
p_newdata <- predict(m, newdata = s$dat[random_subset, ],
offset = rep(0, nrow(s$dat[random_subset, ])))
plot(s$dat$y[random_subset], p_newdata$estimate)
expect_gte(cor(s$dat$y[random_subset], p_newdata$estimate), 0.75)
nd <- s$dat
nd$y <- NULL
p <- predict(m, newdata = nd, offset = rep(0, nrow(nd)))
})
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