bug fixes for ordinal models and removed mxnet from docs

topepo · Jun 10, 2016 · d639b3e · d639b3e
1 parent 88765e9
commit d639b3e
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Showing 6 changed files with 4 additions and 31 deletions.
diff --git a/models/files/vglmAdjCat.R b/models/files/vglmAdjCat.R
@@ -67,7 +67,7 @@ modelInfo <- list(
     predict(modelFit, newdata = newdata, type = "response")
   },
   varImp = NULL,
-  predictors = function(x, ...) predictors(terms(x)),
+  predictors = function(x, ...) caret:::predictors.terms(x@terms$terms),
   levels = function(x)
     if (any(names(x) == "obsLevels")) x$obsLevels else NULL,
   tags = c("Logistic Regression", "Linear Classifier", "Accepts Case Weights", "Ordinal Outcomes"),

diff --git a/models/files/vglmContRatio.R b/models/files/vglmContRatio.R
@@ -71,7 +71,7 @@ modelInfo <- list(
     out
   },
   varImp = NULL,
-  predictors = function(x, ...) predictors(terms(x)),
+  predictors = function(x, ...) caret:::predictors.terms(x@terms$terms),
   levels = function(x)
     if (any(names(x) == "obsLevels")) x$obsLevels else NULL,
   tags = c("Logistic Regression", "Linear Classifier", "Accepts Case Weights", "Ordinal Outcomes"),

diff --git a/models/files/vglmCumulative.R b/models/files/vglmCumulative.R
@@ -67,7 +67,7 @@ modelInfo <- list(
     predict(modelFit, newdata = newdata, type = "response")
   },
   varImp = NULL,
-  predictors = function(x, ...) predictors(terms(x)),
+  predictors = function(x, ...) caret:::predictors.terms(x@terms$terms),
   levels = function(x)
     if (any(names(x) == "obsLevels")) x$obsLevels else NULL,
   tags = c("Logistic Regression", "Linear Classifier", "Accepts Case Weights", "Ordinal Outcomes"),

diff --git a/models/parseModels.R b/models/parseModels.R
@@ -1,6 +1,6 @@
 setwd("~/Code/github/caret/models/files")
 modelFiles <- list.files(pattern = "\\.R$")
-modelFiles <- modelFiles[!grepl("(rknn)|(mxnet)", modelFiles)]
+modelFiles <- modelFiles[!grepl("(rknn)|([mM]xnet)", modelFiles)]
 
 
 models <- vector(mode = "list", length = length(modelFiles))

diff --git a/pkg/caret/inst/models/models.RData b/pkg/caret/inst/models/models.RData
diff --git a/pkg/caret/man/models.Rd b/pkg/caret/man/models.Rd
@@ -926,20 +926,6 @@ For classification using package \pkg{bnclassify} with tuning parameters:
 }
 
 
-\strong{Model Averaged Neural Network} (\code{method = 'avMxnet'})
-
-For classification using package \pkg{mxnet} with tuning parameters:
-\itemize{
-\item Number of Hidden Units in Layer 1 (\code{layer1}, numeric)
-\item Number of Hidden Units in Layer 2 (\code{layer2}, numeric)
-\item Number of Hidden Units in Layer 3 (\code{layer3}, numeric)
-\item Learning Rate (\code{learning.rate}, numeric)
-\item Momentum (\code{momentum}, numeric)
-\item Dropout Rate (\code{dropout}, numeric)
-\item Number of Models (\code{repeats}, numeric)
-}
-
-
 \strong{Model Averaged Neural Network} (\code{method = 'avNNet'})
 
 For classification and regression using package \pkg{nnet} with tuning parameters:
@@ -1073,19 +1059,6 @@ For classification using package \pkg{pamr} with tuning parameters:
 }
 
 
-\strong{Neural Network} (\code{method = 'mxnet'})
-
-For classification using package \pkg{mxnet} with tuning parameters:
-\itemize{
-\item Number of Hidden Units in Layer 1 (\code{layer1}, numeric)
-\item Number of Hidden Units in Layer 2 (\code{layer2}, numeric)
-\item Number of Hidden Units in Layer 3 (\code{layer3}, numeric)
-\item Learning Rate (\code{learning.rate}, numeric)
-\item Momentum (\code{momentum}, numeric)
-\item Dropout Rate (\code{dropout}, numeric)
-}
-
-
 \strong{Neural Network} (\code{method = 'neuralnet'})
 
 For regression using package \pkg{neuralnet} with tuning parameters: