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Improve correlation tests. #287

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Apr 3, 2024
Merged

Improve correlation tests. #287

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dff9e07
Improve correlation tests.
plietar Mar 26, 2024
2aca8d5
Use explicit probability formulas
plietar Mar 27, 2024
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18 changes: 9 additions & 9 deletions R/correlation.R
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Original file line number Diff line number Diff line change
Expand Up @@ -24,11 +24,11 @@ CorrelationParameters <- R6::R6Class(
public = list(

#' @description initialise correlation parameters
#' @param parameters model parameters
initialize = function(parameters) {
# Find a list of enabled interventions
enabled <- vlapply(INTS, function(name) parameters[[name]])
private$interventions <- INTS[enabled]
#' @param population popularion size
#' @param interventions character vector with the name of enabled interventions
initialize = function(population, interventions) {
private$population <- population
private$interventions <- interventions

# Initialise a rho matrix for our interventions
n_ints <- private$n_ints()
Expand All @@ -38,9 +38,6 @@ CorrelationParameters <- R6::R6Class(
ncol = n_ints,
dimnames = list(private$interventions, private$interventions)
)

# Store population for mvnorm draws
private$population <- parameters$human_population
},

#' @description Add rho between rounds
Expand Down Expand Up @@ -183,7 +180,10 @@ CorrelationParameters <- R6::R6Class(
#'
#' # You can now pass the correlation parameters to the run_simulation function
get_correlation_parameters <- function(parameters) {
CorrelationParameters$new(parameters)
# Find a list of enabled interventions
enabled <- vlapply(INTS, function(name) parameters[[name]])

CorrelationParameters$new(parameters$human_population, INTS[enabled])
}

#' @title Sample a population to intervene in given the correlation parameters
Expand Down
159 changes: 95 additions & 64 deletions tests/testthat/test-correlation.R
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Original file line number Diff line number Diff line change
@@ -1,105 +1,136 @@
test_that('1 correlation between rounds gives sensible samples', {
pop <- 1e6
target <- seq(pop)
vaccine_coverage <- .2
parameters <- get_parameters(list(
human_population = pop,
pev = TRUE
))
correlations <- get_correlation_parameters(parameters)

coverage_1 <- .2
coverage_2 <- .4

correlations <- CorrelationParameters$new(pop, c('pev'))
correlations$inter_round_rho('pev', 1)
round_1 <- sample_intervention(target, 'pev', vaccine_coverage, correlations)
round_2 <- sample_intervention(target, 'pev', vaccine_coverage, correlations)
expect_equal(sum(round_1), pop * .2, tolerance=1e2)
expect_equal(sum(round_2), pop * .2, tolerance=1e2)
expect_equal(sum(round_1 & round_2), pop * .2, tolerance=1e2)

round_1 <- sample_intervention(target, 'pev', coverage_1, correlations)
round_2 <- sample_intervention(target, 'pev', coverage_2, correlations)

expect_equal(sum(round_1), pop * coverage_1, tolerance=.1)
expect_equal(sum(round_2), pop * coverage_2, tolerance=.1)

expect_equal(
sum(round_1 & round_2),
pop * min(coverage_1, coverage_2),
tolerance=.1)

expect_equal(
sum(round_1 | round_2),
pop * max(coverage_1, coverage_2),
tolerance=.1)
})

test_that('0 correlation between rounds gives sensible samples', {
pop <- 1e6
target <- seq(pop)
vaccine_coverage <- .5
parameters <- get_parameters(list(
human_population = pop,
pev = TRUE
))
correlations <- get_correlation_parameters(parameters)

coverage_1 <- .2
coverage_2 <- .4

correlations <- CorrelationParameters$new(pop, c('pev'))
correlations$inter_round_rho('pev', 0)
round_1 <- sample_intervention(target, 'pev', vaccine_coverage, correlations)
round_2 <- sample_intervention(target, 'pev', vaccine_coverage, correlations)

round_1 <- sample_intervention(target, 'pev', coverage_1, correlations)
round_2 <- sample_intervention(target, 'pev', coverage_2, correlations)

expect_equal(sum(round_1), pop * coverage_1, tolerance=.1)
expect_equal(sum(round_2), pop * coverage_2, tolerance=.1)

expect_equal(
length(intersect(which(round_1), which(round_2))),
pop * .5,
tolerance=1e2
)
expect_equal(sum(round_1), sum(round_2), tolerance=1e2)
expect_equal(sum(round_1), pop * .5, tolerance=1e2)
sum(round_1 & round_2),
pop * coverage_1 * coverage_2,
tolerance=.1)

expect_equal(
sum(round_1 | round_2),
pop * (coverage_1 + coverage_2 - (coverage_1 * coverage_2)),
tolerance=.1)
})

test_that('1 correlation between interventions gives sensible samples', {
pop <- 1e6
target <- seq(pop)
vaccine_coverage <- .2
mda_coverage <- .2
parameters <- get_parameters(list(
human_population = pop,
pev = TRUE,
mda = TRUE
))
correlations <- get_correlation_parameters(parameters)

pev_coverage <- .2
mda_coverage <- .4

correlations <- CorrelationParameters$new(pop, c('pev', 'mda'))
correlations$inter_round_rho('pev', 1)
correlations$inter_round_rho('mda', 1)
correlations$inter_intervention_rho('pev', 'mda', 1)
vaccine_sample <- sample_intervention(target, 'pev', vaccine_coverage, correlations)

pev_sample <- sample_intervention(target, 'pev', pev_coverage, correlations)
mda_sample <- sample_intervention(target, 'mda', mda_coverage, correlations)

expect_equal(sum(vaccine_sample), pop * .2, tolerance=1e2)
expect_equal(sum(mda_sample), pop * .2, tolerance=1e2)
expect_equal(sum(vaccine_sample & mda_sample), pop * .2, tolerance=1e2)
expect_equal(sum(pev_sample), pop * pev_coverage, tolerance=.1)
expect_equal(sum(mda_sample), pop * mda_coverage, tolerance=.1)

expect_equal(
sum(pev_sample & mda_sample),
pop * min(pev_coverage, mda_coverage),
tolerance=.1)

expect_equal(
sum(pev_sample | mda_sample),
pop * max(pev_coverage, mda_coverage),
tolerance=.1)
})

test_that('0 correlation between interventions gives sensible samples', {
pop <- 1e6
target <- seq(pop)
vaccine_coverage <- .2
mda_coverage <- .2
parameters <- get_parameters(list(
human_population = pop,
pev = TRUE,
mda = TRUE
))
correlations <- get_correlation_parameters(parameters)

pev_coverage <- .2
mda_coverage <- .4

correlations <- CorrelationParameters$new(pop, c('pev', 'mda'))
correlations$inter_round_rho('pev', 1)
correlations$inter_round_rho('mda', 1)
correlations$inter_intervention_rho('pev', 'mda', 0)
vaccine_sample <- sample_intervention(target, 'pev', vaccine_coverage, correlations)

pev_sample <- sample_intervention(target, 'pev', pev_coverage, correlations)
mda_sample <- sample_intervention(target, 'mda', mda_coverage, correlations)

expect_equal(sum(pev_sample), pop * pev_coverage, tolerance=.1)
expect_equal(sum(mda_sample), pop * mda_coverage, tolerance=.1)

expect_equal(
length(intersect(which(vaccine_sample), which(mda_sample))),
pop * .5,
tolerance=1e2
)
expect_equal(sum(vaccine_sample), sum(mda_sample), tolerance=1e2)
expect_equal(sum(vaccine_sample), pop * .5, tolerance=1e2)
sum(pev_sample & mda_sample),
pop * pev_coverage * mda_coverage,
tolerance=.1)

expect_equal(
sum(pev_sample | mda_sample),
pop * (pev_coverage + mda_coverage - (pev_coverage * mda_coverage)),
tolerance=.1)
})

test_that('-1 correlation between interventions gives sensible samples', {
pop <- 1e6
target <- seq(pop)
vaccine_coverage <- .2
mda_coverage <- .2
parameters <- get_parameters(list(
human_population = pop,
pev = TRUE,
mda = TRUE
))
correlations <- get_correlation_parameters(parameters)

pev_coverage <- .2
mda_coverage <- .4

correlations <- CorrelationParameters$new(pop, c('pev', 'mda'))
correlations$inter_round_rho('pev', 1)
correlations$inter_round_rho('mda', 1)
correlations$inter_intervention_rho('pev', 'mda', -1)
vaccine_sample <- sample_intervention(target, 'pev', vaccine_coverage, correlations)

pev_sample <- sample_intervention(target, 'pev', pev_coverage, correlations)
mda_sample <- sample_intervention(target, 'mda', mda_coverage, correlations)
expect_equal(length(intersect(which(vaccine_sample), which(mda_sample))), 0)
expect_equal(sum(vaccine_sample), .2 * pop, tolerance=1e2)
expect_equal(sum(mda_sample), .2 * pop, tolerance=1e2)

expect_equal(sum(pev_sample), pop * pev_coverage, tolerance=.1)
expect_equal(sum(mda_sample), pop * mda_coverage, tolerance=.1)

expect_equal(sum(pev_sample & mda_sample), 0, tolerance=.1)
expect_equal(
sum(pev_sample | mda_sample),
pop * (pev_coverage + mda_coverage),
tolerance=.1)
})
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