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Test simulate data #5

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217e13d
change delay_mean and delay_cv to mean_delay and cv_delay
rebeccanash Sep 10, 2025
913d783
Delete old files
rebeccanash Sep 15, 2025
20929db
Merge with main
rebeccanash Sep 15, 2025
b05326d
Add some tests for simulate_data
rebeccanash Sep 15, 2025
1fa03a0
Merge branch 'main' into test-simulate-data
edknock Nov 6, 2025
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12 changes: 6 additions & 6 deletions R/simulate_data.R
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Original file line number Diff line number Diff line change
Expand Up @@ -6,8 +6,8 @@
#' @param delay_map A data frame that defines the delays between events. It must
#' contain the columns `from` (character), `to` (character), and `group` (list
#' of numeric group IDs).
#' @param delay_params A data frame containing the parameters (`delay_mean`,
#' `delay_cv`) for each delay. Consider combining delay_map and delay_params?
#' @param delay_params A data frame containing the parameters (`mean_delay`,
#' `cv_delay`) for each delay. Consider combining delay_map and delay_params?
#' @param error_params A list containing `prop_missing_data` and `prob_error`.
#' @param range_dates A vector of two integer dates for the simulation range.
#' @param simul_error Boolean. If TRUE, simulates missing and erroneous data.
Expand Down Expand Up @@ -39,8 +39,8 @@
#' "hospitalisation", "onset", "hospitalisation"),
#' to = c("report", "report", "report", "report", "death", "hospitalisation",
#' "discharge", "hospitalisation", "death"),
#' delay_mean = c(10, 10, 10, 10, 15, 7, 20, 7, 12),
#' delay_cv = c(0.3, 0.3, 0.3, 0.3, 0.4, 0.2, 0.5, 0.2, 0.3)
#' mean_delay = c(10, 10, 10, 10, 15, 7, 20, 7, 12),
#' cv_delay = c(0.3, 0.3, 0.3, 0.3, 0.4, 0.2, 0.5, 0.2, 0.3)
#' )
#'
#' # Define other parameters
Expand Down Expand Up @@ -114,8 +114,8 @@ simulate_data <- function(n_per_group,
)

# Sample the delay
shape <- (1 / params$delay_cv)^2
scale <- params$delay_mean / shape
shape <- (1 / params$cv_delay)^2
scale <- params$mean_delay / shape
delay <- pmax(0, round(rgamma(1, shape = shape, scale = scale)))
true_data[i, to_event] <- true_data[i, from_event] + delay
}
Expand Down
8 changes: 4 additions & 4 deletions man/simulate_data.Rd
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88 changes: 88 additions & 0 deletions tests/testthat/test-simulate-data.R
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Original file line number Diff line number Diff line change
@@ -0,0 +1,88 @@
delay_map <- data.frame(
from = c("onset", "onset", "onset", "hospitalisation", "onset",
"hospitalisation"),
to = c("report", "death", "hospitalisation", "discharge", "hospitalisation",
"death"),
group = I(list(1:4, 2, 3, 3, 4, 4))
)

delay_params <- data.frame(
group = c(1:4, 2, 3, 3, 4, 4),
from = c("onset", "onset", "onset", "onset", "onset", "onset",
"hospitalisation", "onset", "hospitalisation"),
to = c("report", "report", "report", "report", "death", "hospitalisation",
"discharge", "hospitalisation", "death"),
mean_delay = c(10, 10, 10, 10, 15, 7, 20, 7, 12),
cv_delay = c(0.3, 0.3, 0.3, 0.3, 0.4, 0.2, 0.5, 0.2, 0.3)
)

test_that("simulate_data returns correct structure and dimensions", {

n_per_group <- rep(10, max(delay_params$group))
total_n <- sum(n_per_group)
error_params <- list(prop_missing_data = 0.2, prob_error = 0.05)
range_dates <- as.integer(as.Date(c("2025-03-01", "2025-09-01")))

set.seed(1)
sim_result <- simulate_data(
n_per_group = n_per_group,
delay_map = delay_map,
delay_params = delay_params,
error_params = error_params,
range_dates = range_dates,
simul_error = TRUE
)

expect_type(sim_result, "list")
expect_named(sim_result, c("true_data", "observed_data", "error_indicators"))

expect_equal(nrow(sim_result$true_data), total_n)
expect_equal(nrow(sim_result$observed_data), total_n)
expect_equal(nrow(sim_result$error_indicators), total_n)

expected_cols <- c("id", "group", "onset", "report", "death",
"hospitalisation", "discharge")
expect_true(all(expected_cols %in% names(sim_result$true_data)))
expect_true(all(expected_cols %in% names(sim_result$observed_data)))
})

test_that("error_params as expected in simulated data", {

n_per_group <- rep(100, max(delay_params$group))
total_n <- sum(n_per_group)
error_params <- list(prop_missing_data = 0.2, prob_error = 0.05)
range_dates <- as.integer(as.Date(c("2025-03-01", "2025-09-01")))

set.seed(1)
sim_result <- simulate_data(
n_per_group = n_per_group,
delay_map = delay_map,
delay_params = delay_params,
error_params = error_params,
range_dates = range_dates,
simul_error = TRUE
)

# just the date columns from the true data and error indicators
date_cols <- setdiff(names(sim_result$true_data), c("id", "group"))
true_dates <- sim_result$true_data[, date_cols]
error_indic <- sim_result$error_indicators[, date_cols]

# identify date entries that should be observed in the groups
eligible_dates <- !is.na(true_dates)
n_eligible_points <- sum(eligible_dates)
eligible_indicators <- error_indic[eligible_dates]

# test the proportion of missing data
observed_prop_missing <- sum(is.na(eligible_indicators)) / n_eligible_points
expect_equal(observed_prop_missing, error_params$prop_missing_data,
tolerance = 0.05)

# test the probability of an error
non_missing_indicators <- na.omit(eligible_indicators)
observed_prob_error <- sum(non_missing_indicators) /
length(non_missing_indicators)
expect_equal(observed_prob_error, error_params$prob_error, tolerance = 0.05)
})