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Support for nestedLogit models (#633)
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strengejacke authored Oct 4, 2023
1 parent 198578d commit 32199f2
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5 changes: 3 additions & 2 deletions DESCRIPTION
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Type: Package
Package: performance
Title: Assessment of Regression Models Performance
Version: 0.10.5.5
Version: 0.10.5.6
Authors@R:
c(person(given = "Daniel",
family = "Lüdecke",
Expand Down Expand Up @@ -117,6 +117,7 @@ Suggests:
mgcv,
mlogit,
multimode,
nestedLogit,
nlme,
nonnest2,
ordinal,
Expand Down Expand Up @@ -149,4 +150,4 @@ Config/Needs/website:
r-lib/pkgdown,
easystats/easystatstemplate
Config/rcmdcheck/ignore-inconsequential-notes: true
Remotes: easystats/see, easystats/parameters
Remotes: easystats/see, easystats/parameters, easystats/insight
6 changes: 6 additions & 0 deletions NAMESPACE
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Expand Up @@ -180,6 +180,7 @@ S3method(model_performance,model_fit)
S3method(model_performance,multinom)
S3method(model_performance,negbinirr)
S3method(model_performance,negbinmfx)
S3method(model_performance,nestedLogit)
S3method(model_performance,plm)
S3method(model_performance,poissonirr)
S3method(model_performance,poissonmfx)
Expand Down Expand Up @@ -371,6 +372,7 @@ S3method(r2,model_fit)
S3method(r2,multinom)
S3method(r2,negbinirr)
S3method(r2,negbinmfx)
S3method(r2,nestedLogit)
S3method(r2,ols)
S3method(r2,phylolm)
S3method(r2,plm)
Expand Down Expand Up @@ -413,6 +415,7 @@ S3method(r2_coxsnell,mclogit)
S3method(r2_coxsnell,multinom)
S3method(r2_coxsnell,negbinirr)
S3method(r2_coxsnell,negbinmfx)
S3method(r2_coxsnell,nestedLogit)
S3method(r2_coxsnell,poissonirr)
S3method(r2_coxsnell,poissonmfx)
S3method(r2_coxsnell,polr)
Expand Down Expand Up @@ -468,6 +471,7 @@ S3method(r2_nagelkerke,mclogit)
S3method(r2_nagelkerke,multinom)
S3method(r2_nagelkerke,negbinirr)
S3method(r2_nagelkerke,negbinmfx)
S3method(r2_nagelkerke,nestedLogit)
S3method(r2_nagelkerke,poissonirr)
S3method(r2_nagelkerke,poissonmfx)
S3method(r2_nagelkerke,polr)
Expand All @@ -479,6 +483,8 @@ S3method(r2_posterior,BFBayesFactor)
S3method(r2_posterior,brmsfit)
S3method(r2_posterior,stanmvreg)
S3method(r2_posterior,stanreg)
S3method(r2_tjur,default)
S3method(r2_tjur,nestedLogit)
S3method(residuals,BFBayesFactor)
S3method(residuals,check_normality_numeric)
S3method(residuals,iv_robust)
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4 changes: 4 additions & 0 deletions NEWS.md
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# performance (development version)

## General

* Support for `nestedLogit` models.

## Changes to functions

* `check_outliers()` for method `"ics"` now detects number of available cores
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8 changes: 3 additions & 5 deletions R/check_itemscale.R
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Expand Up @@ -40,8 +40,6 @@
#' - Briggs SR, Cheek JM (1986) The role of factor analysis in the development
#' and evaluation of personality scales. Journal of Personality, 54(1),
#' 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x
#' - Trochim WMK (2008) Types of Reliability.
#' ([web](https://conjointly.com/kb/types-of-reliability/))
#'
#' @examplesIf require("parameters") && require("psych")
#' # data generation from '?prcomp', slightly modified
Expand Down Expand Up @@ -82,9 +80,9 @@ check_itemscale <- function(x) {
Mean = vapply(items, mean, numeric(1), na.rm = TRUE),
SD = vapply(items, stats::sd, numeric(1), na.rm = TRUE),
Skewness = vapply(items, function(i) as.numeric(datawizard::skewness(i)), numeric(1)),
"Difficulty" = item_difficulty(items)$Difficulty,
"Discrimination" = .item_discr,
"alpha if deleted" = .item_alpha,
Difficulty = item_difficulty(items)$Difficulty,
Discrimination = .item_discr,
`alpha if deleted` = .item_alpha,
stringsAsFactors = FALSE,
check.names = FALSE
)
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22 changes: 19 additions & 3 deletions R/model_performance.lm.R
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Expand Up @@ -125,10 +125,10 @@ model_performance.lm <- function(model, metrics = "all", verbose = TRUE, ...) {
if (("LOGLOSS" %in% toupper(metrics)) && isTRUE(info$is_binomial)) {
out$Log_loss <- .safe({
.logloss <- performance_logloss(model, verbose = verbose)
if (!is.na(.logloss)) {
.logloss
} else {
if (is.na(.logloss)) {
NULL
} else {
.logloss
}
})
}
Expand Down Expand Up @@ -253,6 +253,22 @@ model_performance.zeroinfl <- model_performance.lm
#' @export
model_performance.zerotrunc <- model_performance.lm

#' @export
model_performance.nestedLogit <- function(model, metrics = "all", verbose = TRUE, ...) {
mp <- lapply(model$models, model_performance.lm, metrics = metrics, verbose = verbose, ...)
out <- cbind(
data.frame(Response = names(mp), stringsAsFactors = FALSE),
do.call(rbind, mp)
)
# need to handle R2 separately
if (any(c("ALL", "R2") %in% toupper(metrics))) {
out$R2 <- unlist(r2_tjur(model))
}

row.names(out) <- NULL
class(out) <- unique(c("performance_model", class(out)))
out
}



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