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strengejacke merged 2 commits into from
May 18, 2026
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DHARMa version 0.5.0 has changes in default simulations for GLMMs #910

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DHARMa version 0.5.0 has changes in default simulations for GLMMs
strengejacke May 18, 2026
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strengejacke May 18, 2026
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2 changes: 1 addition & 1 deletion DESCRIPTION
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@@ -1,7 +1,7 @@
Type: Package
Package: performance
Title: Assessment of Regression Models Performance
Version: 0.16.0.3
Version: 0.16.0.4
Authors@R:
c(person(given = "Daniel",
family = "Lüdecke",
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7 changes: 6 additions & 1 deletion NEWS.md
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Expand Up @@ -12,9 +12,14 @@
`modelbased::estimate_prediction()` and returns posterior predictive data in
the same format as for other supported models.

* Updated tests to work with the next release of the *DHARMa* package
(version 0.5.0).

## Bug fixes

* Fixed issue in `check_collinearity()` that was causing inflated VIF values
* Fixed issue in `check_collinearity()` for models from the *fixest* package.

* Fixed issue in `check_collinearity()` that was causing inflated VIF values
when applied to clm and clmm models from the ordinal package.
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* Fixed issue in `test_likelihoodratio_ListLavaan()` that was extracting the
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5 changes: 5 additions & 0 deletions R/check_collinearity.R
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Expand Up @@ -447,9 +447,14 @@
}


.check_collinearity <- function(x, component, ci = 0.95, verbose = TRUE) {

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file=R/check_collinearity.R,line=450,col=1,[cyclocomp_linter] Reduce the cyclomatic complexity of this expression from 46 to at most 40. Consider replacing high-complexity sections like loops and branches with helper functions.

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file=R/check_collinearity.R,line=450,col=1,[cyclocomp_linter] Reduce the cyclomatic complexity of this expression from 46 to at most 40. Consider replacing high-complexity sections like loops and branches with helper functions.
v <- .safe(insight::get_varcov(x, component = component, verbose = FALSE))

# fix class for fixest, which returns a "fixest_vcov" here
if (inherits(v, "fixest_vcov")) {
v <- array(v, dim = dim(v), dimnames = dimnames(v))
}

# sanity check
if (is.null(v)) {
if (isTRUE(verbose)) {
Expand Down Expand Up @@ -566,7 +571,7 @@
return(NULL)
}

R <- stats::cov2cor(v)

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file=R/check_collinearity.R,line=574,col=3,[object_overwrite_linter] 'R' is an exported object from package 'tools'. Avoid re-using such symbols.

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file=R/check_collinearity.R,line=574,col=3,[object_overwrite_linter] 'R' is an exported object from package 'tools'. Avoid re-using such symbols.
detR <- det(R)

result <- vector("numeric")
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4 changes: 2 additions & 2 deletions tests/testthat/test-check_autocorrelation_simres.R
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@@ -1,5 +1,5 @@
test_that("check_autocorrelation works with simulated residuals", {
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
skip_if_not_installed("glmmTMB")
skip_if_not(getRversion() >= "4.0.0")

Expand Down Expand Up @@ -30,7 +30,7 @@ test_that("check_autocorrelation works with simulated residuals", {


test_that("check_autocorrelation.DHARMa works", {
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")

# Test that the DHARMa method works
data(mtcars)
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2 changes: 1 addition & 1 deletion tests/testthat/test-check_normality.R
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Expand Up @@ -83,7 +83,7 @@ test_that("check_normality | t-test", {


test_that("check_normality | simulated residuals", {
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
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m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
res <- simulate_residuals(m)
out <- check_normality(res)
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2 changes: 1 addition & 1 deletion tests/testthat/test-check_outliers.R
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Expand Up @@ -415,7 +415,7 @@


test_that("check_outliers with DHARMa", {
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
mt1 <- mtcars[, c(1, 3, 4)]
# create some fake outliers and attach outliers to main df
mt2 <- rbind(
Expand Down Expand Up @@ -462,7 +462,7 @@
test_that("check_outliers numeric, z-score", {
data(mtcars)
out <- check_outliers(mtcars$mpg, method = "zscore", threshold = 2)
expect_equal(which(as.numeric(out) == 1), c(18, 20))

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file=tests/testthat/test-check_outliers.R,line=465,col=3,[expect_identical_linter] Use expect_identical(x, y) by default; resort to expect_equal() only when needed, e.g. when setting ignore_attr= or tolerance=.
})


Expand All @@ -471,9 +471,9 @@
data(Harman.5, package = "psych")
pc <- psych::principal(Harman.5, 2, rotate = "varimax")
out <- check_outliers(pc)
expect_equal(which(as.numeric(out) == 1), 5)

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file=tests/testthat/test-check_outliers.R,line=474,col=3,[expect_identical_linter] Use expect_identical(x, y) by default; resort to expect_equal() only when needed, e.g. when setting ignore_attr= or tolerance=.
out <- check_outliers(pc, threshold = 0.1)
expect_equal(which(as.numeric(out) == 1), integer(0))

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file=tests/testthat/test-check_outliers.R,line=476,col=3,[expect_identical_linter] Use expect_identical(x, y) by default; resort to expect_equal() only when needed, e.g. when setting ignore_attr= or tolerance=.
out <- check_outliers(pc, threshold = 0.02)
expect_equal(which(as.numeric(out) == 1), c(3, 5, 6, 8))

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file=tests/testthat/test-check_outliers.R,line=478,col=3,[expect_identical_linter] Use expect_identical(x, y) by default; resort to expect_equal() only when needed, e.g. when setting ignore_attr= or tolerance=.
})
18 changes: 7 additions & 11 deletions tests/testthat/test-check_overdispersion.R
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Expand Up @@ -77,7 +77,7 @@

test_that("check_overdispersion, zero-inflated and negbin", {
skip_if_not_installed("glmmTMB")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
skip_if_not(getRversion() >= "4.0.0")
data(Salamanders, package = "glmmTMB")

Expand Down Expand Up @@ -141,7 +141,7 @@

test_that("check_overdispersion, MASS::negbin", {
skip_if_not_installed("MASS")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
set.seed(3)
mu <- rpois(500, lambda = 3)
x <- rnorm(500, mu, mu * 3)
Expand Down Expand Up @@ -181,7 +181,7 @@

test_that("check_overdispersion, genpois", {
skip_if_not_installed("glmmTMB")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
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skip_if_not(getRversion() >= "4.0.0")
data(Salamanders, package = "glmmTMB")

Expand All @@ -190,16 +190,12 @@
family = glmmTMB::genpois(),
data = Salamanders
)
expect_equal(

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file=tests/testthat/test-check_overdispersion.R,line=193,col=3,[expect_identical_linter] Use expect_identical(x, y) by default; resort to expect_equal() only when needed, e.g. when setting ignore_attr= or tolerance=.
check_overdispersion(model),
structure(
list(
dispersion_ratio = 0.971975646955856,
p_value = 0.88
),
class = c("check_overdisp", "see_check_overdisp")
),
tolerance = 1e-4,
ignore_attr = TRUE
list(dispersion_ratio = 1.13005481966618, p_value = 0.408),
class = c("check_overdisp", "see_check_overdisp"),
object_name = "model"
)
)
})
2 changes: 1 addition & 1 deletion tests/testthat/test-check_residuals.R
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@@ -1,6 +1,6 @@
test_that("check_residuals and simulate_residuals", {
skip_on_cran()
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
set.seed(123)
dat <- DHARMa::createData(sampleSize = 100, overdispersion = 0.5, family = poisson())
m <- glm(observedResponse ~ Environment1, family = poisson(), data = dat)
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24 changes: 12 additions & 12 deletions tests/testthat/test-check_zeroinflation.R
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Expand Up @@ -22,7 +22,7 @@ test_that("check_zeroinflation", {

test_that("check_zeroinflation, glmmTMB with and without zero-inflation component", {
skip_if_not_installed("glmmTMB")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
set.seed(123)
data(Salamanders, package = "glmmTMB")

Expand Down Expand Up @@ -95,9 +95,9 @@ test_that("check_zeroinflation, glmer.nb", {
list(
predicted.zeros = 153,
observed.zeros = 155L,
ratio = 0.987329032258065,
ratio = 0.986916129032258,
tolerance = 0.1,
p.value = 0.944
p.value = 0.92
),
class = "check_zi"
),
Expand All @@ -108,7 +108,7 @@ test_that("check_zeroinflation, glmer.nb", {

test_that("check_zeroinflation, glmmTMB nbinom", {
skip_if_not_installed("glmmTMB")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
skip_on_cran()

data(Salamanders, package = "glmmTMB")
Expand All @@ -122,11 +122,11 @@ test_that("check_zeroinflation, glmmTMB nbinom", {
check_zeroinflation(m),
structure(
list(
predicted.zeros = 389,
predicted.zeros = 386,
observed.zeros = 387L,
ratio = 1.00635658914729,
ratio = 0.996196382428941,
tolerance = 0.1,
p.value = 0.944
p.value = 0.952
),
class = "check_zi"
),
Expand All @@ -137,7 +137,7 @@ test_that("check_zeroinflation, glmmTMB nbinom", {

test_that("check_zeroinflation, MASS::negbin", {
skip_if_not_installed("MASS")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
set.seed(3)
mu <- rpois(500, lambda = 3)
x <- rnorm(500, mu, mu * 3)
Expand All @@ -164,7 +164,7 @@ test_that("check_zeroinflation, MASS::negbin", {

test_that("check_zeroinflation, genpois", {
skip_if_not_installed("glmmTMB")
skip_if_not_installed("DHARMa")
skip_if_not_installed("DHARMa", minimum_version = "0.5.0")
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skip_if_not(getRversion() >= "4.0.0")
data(Salamanders, package = "glmmTMB")

Expand All @@ -177,11 +177,11 @@ test_that("check_zeroinflation, genpois", {
check_zeroinflation(model),
structure(
list(
predicted.zeros = 386,
predicted.zeros = 382,
observed.zeros = 387L,
ratio = 0.997860465116279,
ratio = 0.98831007751938,
tolerance = 0.1,
p.value = 1
p.value = 0.64
),
class = "check_zi"
),
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