Nothing
context("LD stats")
test_that("calculate_average_LD", {
testthat::skip_on_os("solaris")
pop_size <- 100
number_of_founders <- 2
run_time <- 1000
morgan <- 1
pop1 <- simulate_admixture(module = ancestry_module(number_of_founders =
number_of_founders,
morgan = morgan),
pop_size = pop_size,
total_runtime = run_time)$population
markers <- c(0.01, 0.99)
outcome <- calculate_ld(pop1,
markers = markers)
testthat::expect_message(
calculate_ld(pop1,
markers = markers,
verbose = TRUE)
)
testthat::expect_equal(mean(outcome$ld_matrix, na.rm = T), 0.0)
pop_size <- 1000
number_of_founders <- 2
run_time <- 1
morgan <- 1
pop1 <- simulate_admixture(module = ancestry_module(number_of_founders =
number_of_founders,
morgan = morgan),
pop_size = pop_size,
total_runtime = run_time)$population
markers <- c(0.5, 0.5 + 1e-4)
outcome <- calculate_ld(pop1,
markers = markers)
testthat::expect_equal(mean(outcome$ld_matrix, na.rm = T), 1.0)
})
test_that("calculate_LD_matrix", {
testthat::skip_on_os("solaris")
pop_size <- 100
number_of_founders <- 2
sampled_individuals <- pop_size
run_time <- 1
morgan <- 1
pop1 <- simulate_admixture(module = ancestry_module(number_of_founders =
number_of_founders,
morgan = morgan),
pop_size = pop_size,
total_runtime = run_time)
testthat::expect_true(verify_population(pop1))
vv <- calculate_ld(pop1, sampled_individuals,
markers = 10)
vv1 <- as.vector(vv$ld_matrix[!is.na(vv$ld_matrix)])
vv2 <- as.vector(vv$dist_matrix[!is.na(vv$dist_matrix)])
linear_model <- lm(vv1 ~ vv2)
# it should at least be negative
testthat::expect_lt(linear_model$coefficients[[2]], 0.0)
})
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