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Possible bug: error is thrown saying check_model is not implemented, if model formula contains a ratio #751

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Jul 13, 2024
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2 changes: 1 addition & 1 deletion DESCRIPTION
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
Type: Package
Package: performance
Title: Assessment of Regression Models Performance
Version: 0.12.0.10
Version: 0.12.0.11
Authors@R:
c(person(given = "Daniel",
family = "Lüdecke",
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3 changes: 3 additions & 0 deletions NEWS.md
Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,9 @@
variable that was named like a valid R function name (e.g., `lm(log(lapply) ~ x)`,
when data contained a variable named `lapply`).

* Fixed issue in `check_predictions()` for linear models when response was
transformed as ratio (e.g. `lm(succes/trials ~ x)`).

# performance 0.12.0

## Breaking
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10 changes: 9 additions & 1 deletion R/check_predictions.R
Original file line number Diff line number Diff line change
Expand Up @@ -303,10 +303,18 @@ pp_check.lm <- function(object,
pattern <- "^(scale|exp|expm1|log|log1p|log10|log2|sqrt)"

# check for transformed response, and backtransform simulations
if (!is.null(resp_string) && grepl(paste0(pattern, "\\("), resp_string)) {
if (!is.null(resp_string) && length(resp_string) == 1 && grepl(paste0(pattern, "\\("), resp_string)) {
out <- .backtransform_sims(out, resp_string)
}

# sanity check - do we have a ratio or similar?
if (is.data.frame(response)) {
# get response data, evaluate formula
response <- eval(str2lang(insight::find_response(object)),
envir = insight::get_response(object)
)
}

out$y <- response

attr(out, "check_range") <- check_range
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15 changes: 15 additions & 0 deletions tests/testthat/test-check_predictions.R
Original file line number Diff line number Diff line change
Expand Up @@ -128,3 +128,18 @@ test_that("check_predictions, glm, binomial", {
expect_equal(head(out4$sim_1), c(0, 0, 0, 1, 0, 1), tolerance = 1e-4)
expect_true(attributes(out4)$model_info$is_bernoulli)
})


test_that("check_predictions, lm, ratio-response", {
skip_if_not_installed("lme4")
data(cbpp, package = "lme4")
model1 <- lm(I(incidence / size) ~ period, data = cbpp)
set.seed(123)
out <- check_predictions(model1)
expect_equal(
head(out$y),
c(0.14286, 0.25, 0.44444, 0, 0.13636, 0.05556),
ignore_attr = TRUE,
tolerance = 1e-4
)
})
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