Update docs for Cran package

R/h2o-package/man/H2OGLMModelList-class.Rd

@@ -15,8 +15,9 @@ Objects can be created by calls of the form \code{new("H2OGLMModelList", ...)}.
 }
 \section{Slots}{
   \describe{
-    \item{\code{models}:}{Object of class \code{"list"} containing \code{"H2OGLMModel"} objects representing the models returned from the lambda search }
+    \item{\code{models}:}{Object of class \code{"list"} containing \code{"H2OGLMModel"} objects representing the models returned from the lambda search. }
     \item{\code{best_model}:}{Object of class \code{"numeric"} indicating the index of the model with the optimal lambda value in the above list. }
+    \item{\code{lambdas}:}{Object of class \code{"numeric"} indicating the optimal lambda value from the lambda search. }
   }
 }
 \section{Methods}{

R/h2o-package/man/h2o.SpeeDRF.Rd

@@ -8,7 +8,7 @@ Performs single-node random forest classification on a data set.
 }
 \usage{
 h2o.SpeeDRF(x, y, data, key = "", classification = TRUE, nfolds = 0, validation, 
-  mtry = -1, ntree = 50, depth = 50, sample.rate = 2/3, oobee = TRUE, 
+  mtry = -1, ntree = 50, depth = 20, sample.rate = 2/3, oobee = TRUE, 
   importance = FALSE, nbins = 1024, seed = -1, stat.type = "ENTROPY", 
   balance.classes = FALSE, verbose = FALSE)
 }

R/h2o-package/man/h2o.deeplearning.Rd

@@ -7,8 +7,8 @@ H2O: Deep Learning Neural Networks
 Performs Deep Learning neural networks on an \code{\linkS4class{H2OParsedData}} object.
 }
 \usage{
-h2o.deeplearning(x, y, data, key = "", checkpoint = "", classification = TRUE, nfolds = 0,
-    validation, autoencoder, use_all_factor_levels,
+h2o.deeplearning(x, y, data, key = "",override_with_best_model, classification = TRUE,
+    nfolds = 0, validation, checkpoint = "", autoencoder, use_all_factor_levels, 
     activation, hidden, epochs, train_samples_per_iteration, seed, adaptive_rate,
     rho, epsilon, rate, rate_annealing, rate_decay, momentum_start,
     momentum_ramp, momentum_stable, nesterov_accelerated_gradient,
@@ -30,10 +30,10 @@ h2o.deeplearning(x, y, data, key = "", checkpoint = "", classification = TRUE, n
         \item{data}{ An \code{\linkS4class{H2OParsedData}} object containing the variables in the model. }
         \item{key}{ (Optional) The unique hex key assigned to the resulting model. If none is given, a key will automatically be generated.}
         \item{override_with_best_model}{ If enabled, override the final model with the best model found during training. Defaults to true.}
-        \item{checkpoint}{"Model checkpoint (either key or H2ODeepLearningModel) to resume training with."}
         \item{classification}{ (Optional) A logical value indicating whether the algorithm should conduct classification. }
         \item{nfolds}{(Optional) Number of folds for cross-validation. If \code{nfolds >= 2}, then \code{validation} must remain empty.}
         \item{validation}{(Optional) An \code{\linkS4class{H2OParsedData}} object indicating the validation dataset used to construct confusion matrix. If left blank, this defaults to the training data when \code{nfolds = 0}.}
+        \item{checkpoint}{"Model checkpoint (either key or H2ODeepLearningModel) to resume training with."}
         \item{activation}{A string indicating the activation function to use. Must be either "Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout" or "MaxoutWithDropout".}
         \item{hidden}{ Hidden layer sizes (e.g. c(100,100)}
 

R/h2o-package/man/h2o.saveModel.Rd

@@ -2,12 +2,13 @@
 \alias{h2o.saveModel}
 \title{Save a H2OModel object to disk.}
 \description{Save a H2OModel object to a disk and can be loaded back into H2O using h2o.loadModel.}
-\usage{h2o.saveModel(object, dir, name, force=FALSE)}
+\usage{h2o.saveModel(object, dir="", name="", filename = "", force=FALSE)}
 
 \arguments{
   \item{object}{ An \code{\linkS4class{H2OModel}} object.}
   \item{dir}{ Directory the model file will be written to.}
   \item{name}{ Name of the file being saved.}
+  \item{filename}{ Full path of directory and name of file being saved. Will override \code{dir} and \code{name} parameters if also given. }
   \item{force}{ (Optional) If \code{force = TRUE} any existing file will be overwritten. Otherwise if the file already exists the operation will fail.}
  }