context("Prediction")
#### simulate example dataset for testing ####
library(dplyr)
## set design elements
set.seed(1)
I = 100
p = 50
## coefficient functions
beta1 = function(t) { sin(2*t*pi) }
beta2 = function(t) { cos(2*t*pi) }
beta3 = function(t) { 1 }
## FPC basis functions
psi1 = function(t) { sin(2*t*pi) }
psi2 = function(t) { cos(2*t*pi) }
## generate subjects, observation times, and FPC scores
time.data = sapply(1:I, function(u) {
ji = sample(10:15, 1)
rbind(runif(ji, 0, 1) %>% sort,
rep(u, ji),
rep(rnorm(1, 0, 3), ji),
rep(rnorm(1, 0, 1), ji))
}) %>% unlist() %>% matrix(ncol = 4, byrow = TRUE)
colnames(time.data) = c("time", "subj", "c_i1", "c_i2")
time.data = as.data.frame(time.data)
## generate predictor data
predictor.data = matrix(rnorm(dim(time.data)[1] * p, sd = 4), dim(time.data)[1], p)
colnames(predictor.data) = paste0("Cov_", 1:p)
## combine and generate responses
concurrent.data = cbind(time.data, predictor.data)
concurrent.data =
mutate(concurrent.data,
Y = Cov_1 * beta1(time) + ## fixed effects
Cov_2 * beta2(time) +
Cov_3 * beta3(time) +
c_i1 * psi1(time) + ## pca effects
c_i2 * psi2(time) +
rnorm(dim(concurrent.data)[1])) ## measurement error
pred.list = paste("Cov", 1:p, sep = "_")
formula = as.formula( paste("Y ~", paste(pred.list, collapse = "+"), "| time") )
#### tests ####
test_that("the standardize function works", {
fit.vbvs = vbvs_concurrent(formula, id.var = "subj", data = concurrent.data, standardized = FALSE,
t.min = 0, t.max = 1)
fit.vb = vb_concurrent(formula, id.var = "subj", data = concurrent.data, standardized = FALSE,
t.min = 0, t.max = 1)
expect_equal(var(concurrent.data$Cov_1), 16, tolerance = 1)
expect_equal(var(fit.vbvs$data.model$Cov_1), 1, tolerance = 1e-1)
expect_equal(var(fit.vb$data.model$Cov_1), 1, tolerance = 1e-1)
expect_equal(fit.vb$data.model, fit.vbvs$data.model, tolerance = 1e-1)
tf = terms.formula(fit.vb$formula.model, specials = NULL)
trmstrings = attr(tf, "term.labels")
data.stan.filtered = standardize_variables(data.original = concurrent.data, data.new = filter(concurrent.data, subj == 1),
trmstrings = trmstrings, time.var = fit.vb$time.var)
expect_equal(data.stan.filtered$Cov_1, filter(fit.vb$data.model, subj == 1)$Cov_1)
})
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