r2

R/model_performance.lm.R

@@ -260,6 +260,11 @@ model_performance.nestedLogit <- function(model, metrics = "all", verbose = TRUE
     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

R/r2.R

@@ -49,11 +49,8 @@ r2 <- function(model, ...) {
 }
 
 
-
-
 # Default models -----------------------------------------------
 
-
 #' @rdname r2
 #' @export
 r2.default <- function(model, ci = NULL, verbose = TRUE, ...) {
@@ -115,6 +112,8 @@ r2.lm <- function(model, ci = NULL, ...) {
 #' @export
 r2.phylolm <- r2.lm
 
+# helper -------------
+
 .r2_lm <- function(model_summary, ci = NULL) {
   out <- list(
     R2 = model_summary$r.squared,
@@ -140,7 +139,6 @@ r2.phylolm <- r2.lm
 }
 
 
-
 #' @export
 r2.summary.lm <- function(model, ci = NULL, ...) {
   if (!is.null(ci) && !is.na(ci)) {
@@ -150,7 +148,6 @@ r2.summary.lm <- function(model, ci = NULL, ...) {
 }
 
 
-
 #' @export
 r2.systemfit <- function(model, ...) {
   out <- lapply(summary(model)$eq, function(model_summary) {
@@ -198,16 +195,13 @@ r2.ols <- function(model, ...) {
   structure(class = "r2_generic", out)
 }
 
-
-
 #' @export
 r2.lrm <- r2.ols
 
 #' @export
 r2.cph <- r2.ols
 
 
-
 #' @export
 r2.mhurdle <- function(model, ...) {
   resp <- insight::get_response(model, verbose = FALSE)
@@ -230,7 +224,6 @@ r2.mhurdle <- function(model, ...) {
 }
 
 
-
 #' @export
 r2.aov <- function(model, ci = NULL, ...) {
   if (!is.null(ci) && !is.na(ci)) {
@@ -252,7 +245,6 @@ r2.aov <- function(model, ci = NULL, ...) {
 }
 
 
-
 #' @export
 r2.mlm <- function(model, ...) {
   model_summary <- summary(model)
@@ -276,7 +268,6 @@ r2.mlm <- function(model, ...) {
 }
 
 
-
 #' @export
 r2.glm <- function(model, ci = NULL, verbose = TRUE, ...) {
   if (!is.null(ci) && !is.na(ci)) {
@@ -312,9 +303,13 @@ r2.glm <- function(model, ci = NULL, verbose = TRUE, ...) {
 #' @export
 r2.glmx <- r2.glm
 
+
 #' @export
 r2.nestedLogit <- function(model, ci = NULL, verbose = TRUE, ...) {
-  lapply(r2, model$models, ci = ci, verbose = verbose, ...)
+  out <- list("R2_Tjur" = r2_tjur(model, ...))
+  attr(out, "model_type") <- "Logistic"
+  class(out) <- c("r2_pseudo", class(out))
+  out
 }
 
 

R/r2_coxsnell.R

@@ -88,6 +88,26 @@ r2_coxsnell.glm <- function(model, verbose = TRUE, ...) {
 #' @export
 r2_coxsnell.BBreg <- r2_coxsnell.glm
 
+
+#' @export
+r2_coxsnell.nestedLogit <- function(model, ...) {
+  n <- insight::n_obs(model, disaggregate = TRUE)
+  stats::setNames(
+    lapply(names(model$models), function(i) {
+      m <- model$models[[i]]
+      # if no deviance, return NA
+      if (is.null(m$deviance)) {
+        return(NA)
+      }
+      r2_coxsnell <- (1 - exp((m$deviance - m$null.deviance) / n[[i]]))
+      names(r2_coxsnell) <- "Cox & Snell's R2"
+      r2_coxsnell
+    }),
+    names(model$models)
+  )
+}
+
+
 #' @export
 r2_coxsnell.mclogit <- function(model, ...) {
   insight::check_if_installed("mclogit", reason = "to calculate R2")

R/r2_nagelkerke.R

@@ -77,6 +77,26 @@ r2_nagelkerke.glm <- function(model, verbose = TRUE, ...) {
 #' @export
 r2_nagelkerke.BBreg <- r2_nagelkerke.glm
 
+
+#' @export
+r2_nagelkerke.nestedLogit <- function(model, ...) {
+  n <- insight::n_obs(model, disaggregate = TRUE)
+  stats::setNames(
+    lapply(names(model$models), function(i) {
+      m <- model$models[[i]]
+      # if no deviance, return NA
+      if (is.null(m$deviance)) {
+        return(NA)
+      }
+      r2_nagelkerke <- (1 - exp((m$deviance - m$null.deviance) / n[[i]])) / (1 - exp(-m$null.deviance / n[[i]]))
+      names(r2_nagelkerke) <- "Nagelkerke's R2"
+      r2_nagelkerke
+    }),
+    names(model$models)
+  )
+}
+
+
 #' @export
 r2_nagelkerke.bife <- function(model, ...) {
   r2_nagelkerke <- r2_coxsnell(model) / (1 - exp(-model$null_deviance / insight::n_obs(model)))

R/r2_tjur.R

@@ -23,6 +23,11 @@
 #'
 #' @export
 r2_tjur <- function(model, ...) {
+  UseMethod("r2_tjur")
+}
+
+#' @export
+r2_tjur.default <- function(model, ...) {
   info <- list(...)$model_info
   if (is.null(info)) {
     info <- suppressWarnings(insight::model_info(model, verbose = FALSE))
@@ -50,3 +55,30 @@ r2_tjur <- function(model, ...) {
   names(tjur_d) <- "Tjur's R2"
   tjur_d
 }
+
+#' @export
+r2_tjur.nestedLogit <- function(model, ...) {
+  resp <- insight::get_response(model, dichotomies = TRUE, verbose = FALSE)
+
+  stats::setNames(
+    lapply(names(model$models), function(i) {
+      y <- resp[[i]]
+      m <- model$models[[i]]
+      pred <- stats::predict(m, type = "response")
+      # delete pred for cases with missing residuals
+      if (anyNA(stats::residuals(m))) {
+        pred <- pred[!is.na(stats::residuals(m))]
+      }
+      categories <- unique(y)
+      m1 <- mean(pred[which(y == categories[1])], na.rm = TRUE)
+      m2 <- mean(pred[which(y == categories[2])], na.rm = TRUE)
+
+      tjur_d <- abs(m2 - m1)
+
+      names(tjur_d) <- "Tjur's R2"
+      tjur_d
+    }
+    ),
+    names(model$models)
+  )
+}

tests/testthat/_snaps/nestedLogit.md

@@ -5,8 +5,8 @@
     Output
       # Indices of model performance
       
-      Response |     AIC |     BIC |  RMSE | Sigma
-      --------------------------------------------
-      work     | 325.733 | 336.449 | 0.456 | 1.000
-      full     | 110.495 | 118.541 | 0.398 | 1.000
+      Response |     AIC |     BIC |  RMSE | Sigma |    R2
+      ----------------------------------------------------
+      work     | 325.733 | 336.449 | 0.456 | 1.000 | 0.138
+      full     | 110.495 | 118.541 | 0.398 | 1.000 | 0.333
 

tests/testthat/test-nestedLogit.R

@@ -13,6 +13,53 @@ mnl <- nestedLogit::nestedLogit(
   data = Womenlf
 )
 
+test_that("r2", {
+  out <- r2(mnl)
+  expect_equal(
+    out,
+    list(R2_Tjur = list(
+      work = c(`Tjur's R2` = 0.137759452521642),
+      full = c(`Tjur's R2` = 0.332536937208286)
+    )),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+
+  out <- r2_tjur(mnl)
+  expect_equal(
+    out,
+    list(R2_Tjur = list(
+      work = c(`Tjur's R2` = 0.137759452521642),
+      full = c(`Tjur's R2` = 0.332536937208286)
+    )),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+
+  out <- r2_coxsnell(mnl)
+  expect_equal(
+    out,
+    list(
+      work = c(`Cox & Snell's R2` = 0.129313084315599),
+      full = c(`Cox & Snell's R2` = 0.308541455410686)
+    ),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+
+  out <- r2_nagelkerke(mnl)
+  expect_equal(
+    out,
+    list(
+      work = c(`Nagelkerke's R2` = 0.174313365512442),
+      full = c(`Nagelkerke's R2` = 0.418511411473948)
+    ),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+})
+
+
 test_that("model_performance", {
   expect_snapshot(model_performance(mnl))
 })