coef table for multinomial

Author: koenderks

R/mlClassificationLogisticMultinomial.R

@@ -147,7 +147,7 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
   table$addColumnInfo(name = "var", title = "", type = "string")
   table$addColumnInfo(name = "coefs", title = gettextf("Coefficient (%s)", "\u03B2"), type = "number")
   table$addColumnInfo(name = "se", title = gettext("Standard Error"), type = "number")
-  table$addColumnInfo(name = "t", title = gettext("t"), type = "number")
+  table$addColumnInfo(name = "z", title = gettext("z"), type = "number")
   table$addColumnInfo(name = "p", title = gettext("p"), type = "pvalue")
   if (options[["coefTableConfInt"]]) {
     overtitle <- gettextf("%1$s%% Confidence interval", round(options[["coefTableConfIntLevel"]] * 100, 3))
@@ -169,10 +169,8 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
   classificationResult <- jaspResults[["classificationResult"]]$object
   model <- classificationResult[["model"]]
   if (classificationResult[["family"]] == "binomial") {
-    coefs <- summary(model)$coefficients
-    conf_int <- confint(model, level = options[["coefTableConfIntLevel"]])
-    coefs <- cbind(coefs, lower = conf_int[, 1], upper = conf_int[, 2])
-    colnames(coefs) <- c("est", "se", "t", "p", "lower", "upper")
+    coefs <- cbind(coef(summary(model)), confint(model, level = options[["coefTableConfIntLevel"]]))
+    colnames(coefs) <- c("est", "se", "z", "p", "lower", "upper")
     vars <- rownames(coefs)
     for (i in seq_along(vars)) {
       if (!(vars[i] %in% options[["predictors"]]) && vars[i] != "(Intercept)") {
@@ -184,8 +182,8 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
     }
     rownames(coefs) <- vars
   } else {
-    coefs <- cbind(model$coefficients, confint(model[["original"]], level = options[["coefTableConfIntLevel"]]))
-    colnames(coefs) <- c("est", "lower", "upper")
+    coefs <- cbind(VGAM::coef(VGAM::summaryvglm(model[["original"]])), confint(model[["original"]], level = options[["coefTableConfIntLevel"]]))
+    colnames(coefs) <- c("est", "se", "z", "p", "lower", "upper")
     vars <- rownames(coefs)
     for (i in seq_along(vars)) {
       for (j in c("(Intercept)", options[["predictors"]])) {
@@ -206,16 +204,9 @@ mlClassificationLogisticMultinomial <- function(jaspResults, dataset, options, .
   }
   table[["var"]] <- rownames(coefs)
   table[["coefs"]] <- as.numeric(coefs[, "est"])
-  if (classificationResult[["family"]] == "binomial") {
-    table[["se"]] <- as.numeric(coefs[, "se"])
-    table[["t"]] <- as.numeric(coefs[, "t"])
-    table[["p"]] <- as.numeric(coefs[, "p"])
-  } else {
-    table[["se"]] <- rep(".", nrow(coefs))
-    table[["t"]] <- rep(".", nrow(coefs))
-    table[["p"]] <- rep(".", nrow(coefs))
-    table$addFootnote(gettext("Standard errors, t-values and p-values are not available in multinomial regression."))
-  }
+  table[["se"]] <- as.numeric(coefs[, "se"])
+  table[["z"]] <- as.numeric(coefs[, "z"])
+  table[["p"]] <- as.numeric(coefs[, "p"])
   if (options[["coefTableConfInt"]]) {
     table[["lower"]] <- coefs[, "lower"]
     table[["upper"]] <- coefs[, "upper"]

tests/testthat/test-mlclassificationlogisticmultinomial.R

@@ -4,6 +4,8 @@ context("Machine Learning Logistic / Multinomial Regression Classification")
 options <- initMlOptions("mlClassificationLogisticMultinomial")
 options$addIndicator <- FALSE
 options$addPredictions <- FALSE
+options$coefTable <- TRUE
+options$coefTableConfInt <- TRUE
 options$classProportionsTable <- TRUE
 options$holdoutData <- "holdoutManual"
 options$modelOptimization <- "manual"
@@ -42,6 +44,32 @@ test_that("Model Summary: Multinomial Regression Classification table results ma
 		list("Multinomial", "Logit", 30, 120, 1))
 })
 
+test_that("Regression Coefficients table results match", {
+	table <- results[["results"]][["coefTable"]][["data"]]
+	jaspTools::expect_equal_tables(table,
+		list(-0.42267961226173, -4222.39134720741, 0.999843438690878, 2154.10522892129,
+			 4221.54598798288, "(Intercept) : setosa", -0.000196220503337896,
+			 17.8212683047785, 0.307905475852188, 0.0461059069011903, 8.93555339132221,
+			 35.3346311337047, "(Intercept) : versicolor", 1.99442245200903,
+			 7.95554619079237, -9967.35637439074, 0.998752813237481, 5089.53837890162,
+			 9983.26746677233, "Sepal.Length : setosa", 0.00156311743787445,
+			 1.89635371704686, -2.10497557567811, 0.352947362657274, 2.04153205073509,
+			 5.89768300977184, "Sepal.Length : versicolor", 0.928887555972511,
+			 4.70317443290632, -2533.61853897752, 0.997102445459767, 1295.08589618604,
+			 2543.02488784333, "Sepal.Width : setosa", 0.00363155405116905,
+			 2.30296713825466, -1.92502652612214, 0.285708816748704, 2.15717926335722,
+			 6.53096080263146, "Sepal.Width : versicolor", 1.0675826424692,
+			 -38.7099474348317, -15129.1258351686, 0.995988491696961, 7699.33325651139,
+			 15051.705940299, "Petal.Length : setosa", -0.00502770124959773,
+			 -14.4906493502573, -31.1295653321292, 0.087838572245474, 8.48939884258976,
+			 2.14826663161466, "Petal.Length : versicolor", -1.7069111275065,
+			 -24.205993975534, -10348.1025945441, 0.996333380037363, 5267.39097350877,
+			 10299.690606593, "Petal.Width : setosa", -0.00459544280978437,
+			 -12.7574125117412, -27.3435062790441, 0.0864846111196134, 7.44202132404277,
+			 1.82868125556169, "Petal.Width : versicolor", -1.7142402522452
+			))
+})
+
 test_that("Confusion Matrix table results match", {
 	table <- results[["results"]][["confusionTable"]][["data"]]
 	jaspTools::expect_equal_tables(table,