diff --git a/R/testingFunctions.R b/R/testingFunctions.R
index 95ab035..c83d5b6 100644
--- a/R/testingFunctions.R
+++ b/R/testingFunctions.R
@@ -5,9 +5,9 @@
#' @param id `character` \cr
#' Testing id.
#' @param ind_col `character` \cr
-#' Name of column of individuals IDs in testing data.frame.
+#' Name of column of individuals IDs in testing `data.frame`.
#' @param verbose `boolean` \cr
-#' Warning messages will be displayed if set to TRUE.
+#' Warning messages will be displayed if set to `TRUE`.
#' @return
#' A [Testing] object.
#' @export
@@ -25,7 +25,7 @@ createTesting = function (id,
#' @title createTestLayer
#' @description
-#' Creates and store a [TestLayer] on the [Testing] object passed as argument.
+#' Creates and stores a [TestLayer] on the [Testing] object passed as argument.
#'
#' @param testing `Testing` \cr
#' Testing object where the created layer will be stored.
@@ -57,7 +57,7 @@ createTestLayer = function (testing,
#' @title Testing object Summaries
#' @description
-#' Summaries a fuseMLR [Testing] object.
+#' Summaries a `fuseMLR` [Testing] object.
#'
#' @param object `Testing` \cr
#' The [Testing] object of interest.
diff --git a/R/trainingFunctions.R b/R/trainingFunctions.R
index 99ae4de..6d7459e 100644
--- a/R/trainingFunctions.R
+++ b/R/trainingFunctions.R
@@ -44,54 +44,64 @@ createTraining = function (id,
#' selection methods, and a modality-specific learner.
#'
#' @param training `Training` \cr
-#' Training object where the created layer will be stored.
+#' Training object for storing the created layer.
#' @param train_layer_id `character` \cr
#' ID of the [TrainLayer] to be created.
#' @param train_data `data.frame` \cr
-#' Data modality to be stored in [TrainData].
+#' Data modality to be stored on the layer.
#' @param varsel_package `character` \cr
-#' Name of the package containing the function that implements the variable selection algorithm.\cr
+#' Package name containing the variable selection algorithm function.
+#' Defaults to `NULL` if the function exists in the current working environment.\cr
#' @param varsel_fct `character` \cr
-#' Name of variable selection function. Default value is `NULL` for no variable selection.
-#' If specified, the function must allow at least two parameters `x` (of predictors)
-#' and `y` (response values), and return a vector of selected variables. Otherwise use the interface parameters
-#' `x_varsel`, `y_varsel` below to map the original argument names, and `extract_var_fct` indicate how to extract the vector of selected variables.
-#' An exception, however, is made for the Boruta function, for which an internal adjustment is implemented; its use requires no further modifications.
+#' Variable selection function name. Default value is `NULL` for no variable selection.
+#' If specified, the function must accept at least two parameters: `x` (predictors)
+#' and `y` (response values), and return a vector of selected variables.
+#' Alternatively, use the interface parameters `x_varsel` and `y_varsel` to map
+#' the original argument names, and `extract_var_fct` to specify how to extract
+#' the vector of selected variables. An exception is made for the `Boruta` function,
+#' which includes an internal adjustment and requires no additional modifications.
#' @param varsel_param `list` \cr
#' List of arguments to be passed to \code{varsel_fct}.
#' @param lrner_package `character` \cr
-#' Name of the package containing the function that implements the learning algorithm.
-#' Default is `NULL`, if the function is available in the current working environment.
+#' Name of the package containing the learning algorithm function. Defaults to
+#' `NULL` if the function is available in the current working environment.
#' @param lrn_fct `character` \cr
-#' Name of the learning function. The corresponding function must allow at least two parameters `x` (of predictors)
-#' and `y` (response values), and return a model. Otherwise, the interface
-#' parameters `x_lrn` and `y_lrn` below can be used to map these argument name with the original arguments in your function.
-#' The returned model must allow the use of the generic function `predict` (with arguments `object` and `data`) to make
-#' predictions for new data and predictions should be a vector or a `list` containing a vector called
-#' `predictions` with the predicted values. If the arguments `object` and `data` are named differently in your predict
-#' function, use the interface parameters `object` and `data` below to specify the original names. In addition, if
-#' predictions are stored as a `matrix` or a `data.frame` (e.g. predicted probabilities for different classes in dichotomous classification),
-#' only the second column (assumed to be the class `1` probabilities) will be considered. If the predicted values are not returned in one of the formats above,
-#' use the argument `extract_pred_fct` below to specify how the predicted values can be extracted from the predicted object.
+#' Name of the learning function. The function must accept at least two parameters:
+#' `x` (predictors) and `y` (response values) and return a model. Alternatively,
+#' use the interface parameters `x_lrn` and `y_lrn` to map these names to the
+#' original arguments in your function.
+#' The returned model must support the generic `predict` function (with arguments
+#' `object` and `data`) to generate predictions for new data. Predictions should
+#' be either a vector or a `list` containing a vector named `predictions` with
+#' the predicted values.\cr
+#' If the arguments `object` and `data` have different names in your `predict`
+#' function, use the interface parameters below to map them to the original names.
+#' Additionally, if predictions are stored as a `matrix` or `data.frame`
+#' (e.g., predicted probabilities for dichotomous classification), only the second
+#' column (assumed to be class `1` probabilities) will be used.
+#' If the predicted values are not returned in one of the formats mentioned above,
+#' use the `extract_pred_fct` argument below to specify how to extract the predicted
+#' values from the prediction object.
#' @param param_train_list `character` \cr
#' List of arguments to be passed to \code{lrn_fct}.
#' @param param_pred_list `character` \cr
-#' List of arguments to be passed to \code{predict} when computing predictions.
+#' List of arguments to be passed to \code{predict} when generating predictions.
#' @param na_action `character`\cr
-#' Handling of missing values in data a modality during training. Set to "na.keep" to keep missing values, or "na.rm" to remove individuals with missing values. Imputation of missing values in data modalities is not yet handled.
+#' Handling of missing values in data during training. Set to `"na.keep"` to retain
+#' missing values, or `"na.rm"` to remove instances with missing values.
#' @param object `character` \cr
-#' The generic function \code{predict} uses a parameter \code{object} to pass a model.
-#' If the corresponding argument is named differently in your predict function, specify the name.
+#' The generic function `predict` uses the parameter `object` to pass a model.
+#' If the corresponding argument is named differently in your `predict` function, specify its name.
#' @param data `character` \cr
#' The generic function \code{predict} uses a parameter \code{data} to pass new data.
#' If the corresponding argument is named differently in your predict function, specify the name.
#' @param extract_pred_fct `character or function` \cr
-#' If the predict function that is called for the model does not return a vector, then
-#' use this argument to specify a (or a name of a) function that can be used to extract vector of predictions.
-#' Default value is NULL, if predictions are rerturned as vector.
+#' If the `predict` function called for the model does not return a vector,
+#' use this argument to specify a function (or the name of a function) to extract
+#' the vector of predictions. The default value is `NULL` if predictions are returned as a vector.
#' @param extract_var_fct `character or function` \cr
-#' If the variable selection function that is called does not return a vector, then
-#' use this argument to specify a (or a name of a) function that can be used to extract vector of selected variables.
+#' If the variable selection function does not return a vector, use this argument
+#' to specify a function (or the name of a function) to extract the vector of selected variables.
#' @param x_varsel `character` \cr
#' If the name of the argument used by the provided original variable selection function to pass
#' the matrix of independent variable is not \code{x}, use this argument to specify how it is called in the provided function.
@@ -184,32 +194,40 @@ createTrainLayer = function (training,
#' base models.
#'
#' @param training `Training` \cr
-#' Training object where the created layer will be stored.
+#' Training object for storing the created meta-layer.
#' @param meta_layer_id `character` \cr
#' ID of the layer to be created.
#' @param lrner_package `character` \cr
-#' Name of the package containing the function that implements the learning algorithm.
-#' Default is `NULL`, if the function is available in the current working environment.
+#' Package name containing the variable selection algorithm function.
+#' Defaults to `NULL` if the function exists in the current working environment.
#' @param lrn_fct `character` \cr
-#' Name of the learning function. The corresponding function must allow at least two parameters `x` (of predictors)
-#' and `y` (response values), and return a model. If not, the interface
-#' parameters `x_lrn` and `y_lrn` below can be used to map these argument name with the original arguments in your function.
-#' The returned model must allow the use of the generic function `predict` (with arguments `object` and `data`) to make
-#' predictions for new data and predictions should be a vector or a `list` containing a vector called
-#' `predictions` with the predicted values. See the details below about meta-learners. If the arguments `object` and `data` are named differently in your predict
-#' function, use the interface parameters `object` and `data` below to specify the original names.
+#' Name of the learning function. The function must accept at least two
+#' parameters: `x` (predictors) and `y` (response values), and return a model.
+#' If not, use the interface parameters `x_lrn` and `y_lrn` below to map these
+#' argument names to the original arguments in your function. The returned model
+#' must support the generic `predict` function (with arguments `object` and `data`)
+#' to make predictions for new data, and the predictions should be a vector or
+#' a `list` containing a vector called `predictions` with the predicted values.
+#' If the arguments `object` and `data` are named differently in your predict
+#' function, use the interface parameters `object` and `data` below to specify
+#' the original names. See the details below about meta-learners.
#' @param param_train_list `character` \cr
#' List of arguments to be passed to \code{lrn_fct}.
#' @param param_pred_list `list` \cr
#' List of arguments to be passed to \code{predict} when computing predictions.
#' @param na_action `character`\cr
-#' Handling of missing values in modality-specific predictions during training. Set to "na.keep" to keep missing values, "na.rm" to remove individuals with missing values or "na.impute" to impute missing values in modality-specific predictions. Only median and mode based imputations are actually handled. With the "na.keep" option, ensure that the provided meta-learner can handle missing values.
+#' Handling of missing values in modality-specific predictions during training.
+#' Set to `"na.keep"` to keep missing values, `"na.rm"` to remove individuals
+#' with missing values or `"na.impute"` to impute missing values in modality-specific
+#' predictions. Only median and mode based imputations are actually handled.
+#' With the `"na.keep"` option, ensure that the provided meta-learner can handle missing values.
#' @param x_lrn `character` \cr
-#' If the name of the argument used by the provided original functions to pass
-#' the matrix of independent variable is not \code{x}, use this argument to specify how it is called in the provided function.
+#' If the argument name used by the provided original function to pass the matrix
+#' of independent variables is not `x`, use this argument to specify the name used
+#' in the function.
#' @param y_lrn `character` \cr
-#' If the name of the argument used by the provided original functions to pass
-#' the target variable is not \code{y}, use this argument to specify how it is called in the provided function.
+#' If the argument name used by the provided original function to pass the target
+#' variable is not `y`, use this argument to specify the name used in the function.
#' @param object `character` \cr
#' The generic function \code{predict} uses a parameter \code{object} to pass a model.
#' If the corresponding argument is named differently in your predict function, specify the name.
@@ -219,7 +237,7 @@ createTrainLayer = function (training,
#' @param extract_pred_fct `character or function` \cr
#' If the predict function that is called for the model does not return a vector, then
#' use this argument to specify a (or a name of a) function that can be used to extract vector of predictions.
-#' Default value is NULL, if predictions are in a vector.
+#' Defaults to NULL, if predictions are a vector.
#' @details
#'
#' Internal meta-learners are available in the package.
@@ -283,10 +301,10 @@ createTrainMetaLayer = function (training,
#' @title varSelection
#' @description
-#' Variable selection on the current training object.
+#' Variable selection on the training object passed as argument.
#'
#' @param training `Training` \cr
-#' Training object where the created layer will be stored.
+#' Training object for storing the created layer.
#' @param ind_subset `vector` \cr
#' ID subset of individuals to be used for variable selection.
#'
@@ -316,14 +334,16 @@ varSelection = function (training,
#' object which can be used for predictions.
#'
#' @param training `Training` \cr
-#' Training object where the created layer will be stored.
+#' Training object for storing training layers.
#' @param ind_subset `vector` \cr
#' ID subset to be used for training.
#' @param use_var_sel `boolean` \cr
-#' If TRUE and no variable selection has been performed for the provide training object, then a variable selection will proceed the training.
+#' If `TRUE` and no variable selection has been performed for the provide training object,
+#' then a variable selection will proceed the training.
#' Otherwise, if variable selection has been previously performed, the selected variables will be used for training.
#' @param resampling_method `function` \cr
-#' Function for internal validation. If not specify, the \code{resampling} function from the package \code{caret} is used for a 10-folds cross-validation.
+#' Function for internal validation. If not specify, the \code{resampling} function
+#' from the package \code{caret} is used for a 10-folds cross-validation.
#' @param resampling_arg `list` \cr
#' List of arguments to be passed to the function.
#' @param seed `integer` \cr
@@ -361,14 +381,14 @@ fusemlr = function (training,
#' Computes predictions for the [Testing] object passed as argument.
#'
#' @param object `Training` \cr
-#' Training object to be used to compute predictions.
+#' A trained Training object to be used to compute predictions.
#' @param testing `Testing` \cr
#' A new testing object to be predicted.
#' @param ind_subset `vector` \cr
#' Vector of IDs to be predicted.
#'
#' @return
-#' The predicted object. All layers and the meta layer are predicted. This is the final predicted object.
+#' The final predicted object. All layers and the meta layer are predicted.
#' @export
#' @method predict Training
predict.Training = function (object,
@@ -411,7 +431,7 @@ extractData = function (object) {
#' @title Training object Summaries
#' @description
-#' Summaries a fuseMLR [Training] object.
+#' Summaries a `fuseMLR` [Training] object.
#'
#' @param object `Training` \cr
#' The [Training] object of interest.
diff --git a/doc/fuseMLR.html b/doc/fuseMLR.html
index 2a96f94..5a4953b 100644
--- a/doc/fuseMLR.html
+++ b/doc/fuseMLR.html
@@ -471,7 +471,7 @@ C.1 - Creating a training
param_train_list = list(probability = TRUE,
mtry = 1L),
param_pred_list = list(),
- na_action = "na.rm")
+ na_action = "na.keep")
#> Training : training
#> Problem type : classification
#> Status : Not trained
@@ -479,7 +479,7 @@ C.1 - Creating a training
#> Layers trained : 0
#> p : 131
#> n : 50
-#> na.action : na.rm
+#> na.action : na.keep
# Create gene protein abundance layer
createTrainLayer(training = training,
train_layer_id = "proteinexpr",
@@ -502,7 +502,7 @@ C.1 - Creating a training
#> Layers trained : 0
#> p : 131 | 160
#> n : 50 | 50
-#> na.action : na.rm | na.keep
# Create methylation layer
createTrainLayer(training = training,
train_layer_id = "methylation",
@@ -525,7 +525,7 @@ C.1 - Creating a training
#> Layers trained : 0
#> p : 131 | 160 | 367
#> n : 50 | 50 | 50
-#> na.action : na.rm | na.keep | na.keep
Also add a meta-layer. We use the weighted mean (internal function to
fuseMLR) as meta-learner. Similarly to learners, a
meta-learner should allow at least the arguments x and
@@ -567,7 +567,7 @@
C.1 - Creating a training
#> Layers trained : 0
#> p : 131 | 160 | 367
#> n : 50 | 50 | 50
-#> na.action : na.rm | na.keep | na.keep
+#> na.action : na.keep | na.keep | na.keep
print(training)
#> Training : training
#> Problem type : classification
@@ -576,7 +576,7 @@ C.1 - Creating a training
#> Layers trained : 0
#> p : 131 | 160 | 367
#> n : 50 | 50 | 50
-#> na.action : na.rm | na.keep | na.keep
+#> na.action : na.keep | na.keep | na.keep
Function upsetplot() is available to generate an upset
of the training data, i.e. an overview how patients overlap across
layers.
@@ -664,7 +664,7 @@ C.2 - Variable selection
#> Layers trained : 0
#> p : 19 | 1 | 35
#> n : 50 | 50 | 50
-#> na.action : na.rm | na.keep | na.keep
+#> na.action : na.keep | na.keep | na.keep
For each layer, the variable selection results show the chosen
variables.
@@ -718,7 +718,7 @@ C.2 - Train
#> Var. sel. used : Yes
#> p : 19 | 1 | 35 | 3
#> n : 50 | 50 | 50 | 26
-#> na.action : na.rm | na.keep | na.keep | na.rm
+#> na.action : na.keep | na.keep | na.keep | na.rm
We can also display a summary of training to see more
details on layer levels. Information about the training data modality,
the variable selection method and the learner stored at each layer are
@@ -734,7 +734,7 @@
C.2 - Train
#> Var. sel. used : Yes
#> p : 19 | 1 | 35 | 3
#> n : 50 | 50 | 50 | 26
-#> na.action : na.rm | na.keep | na.keep | na.rm
+#> na.action : na.keep | na.keep | na.keep | na.rm
#> ----------------
#>
#> Layer geneexpr
diff --git a/man/createTestLayer.Rd b/man/createTestLayer.Rd
index cb23751..e414d1f 100644
--- a/man/createTestLayer.Rd
+++ b/man/createTestLayer.Rd
@@ -20,5 +20,5 @@ Data modality to be stored in \link{TestData}.}
The updated \link{Testing} object (with the new layer) is returned.
}
\description{
-Creates and store a \link{TestLayer} on the \link{Testing} object passed as argument.
+Creates and stores a \link{TestLayer} on the \link{Testing} object passed as argument.
}
diff --git a/man/createTesting.Rd b/man/createTesting.Rd
index 57c18d0..1a32641 100644
--- a/man/createTesting.Rd
+++ b/man/createTesting.Rd
@@ -11,10 +11,10 @@ createTesting(id, ind_col, verbose = TRUE)
Testing id.}
\item{ind_col}{\code{character} \cr
-Name of column of individuals IDs in testing data.frame.}
+Name of column of individuals IDs in testing \code{data.frame}.}
\item{verbose}{\code{boolean} \cr
-Warning messages will be displayed if set to TRUE.}
+Warning messages will be displayed if set to \code{TRUE}.}
}
\value{
A \link{Testing} object.
diff --git a/man/createTrainLayer.Rd b/man/createTrainLayer.Rd
index 46e94e1..2b52dd0 100644
--- a/man/createTrainLayer.Rd
+++ b/man/createTrainLayer.Rd
@@ -28,51 +28,61 @@ createTrainLayer(
}
\arguments{
\item{training}{\code{Training} \cr
-Training object where the created layer will be stored.}
+Training object for storing the created layer.}
\item{train_layer_id}{\code{character} \cr
ID of the \link{TrainLayer} to be created.}
\item{train_data}{\code{data.frame} \cr
-Data modality to be stored in \link{TrainData}.}
+Data modality to be stored on the layer.}
\item{varsel_package}{\code{character} \cr
-Name of the package containing the function that implements the variable selection algorithm.\cr}
+Package name containing the variable selection algorithm function.
+Defaults to \code{NULL} if the function exists in the current working environment.\cr}
\item{varsel_fct}{\code{character} \cr
-Name of variable selection function. Default value is \code{NULL} for no variable selection.
-If specified, the function must allow at least two parameters \code{x} (of predictors)
-and \code{y} (response values), and return a vector of selected variables. Otherwise use the interface parameters
-\code{x_varsel}, \code{y_varsel} below to map the original argument names, and \code{extract_var_fct} indicate how to extract the vector of selected variables.
-An exception, however, is made for the Boruta function, for which an internal adjustment is implemented; its use requires no further modifications.}
+Variable selection function name. Default value is \code{NULL} for no variable selection.
+If specified, the function must accept at least two parameters: \code{x} (predictors)
+and \code{y} (response values), and return a vector of selected variables.
+Alternatively, use the interface parameters \code{x_varsel} and \code{y_varsel} to map
+the original argument names, and \code{extract_var_fct} to specify how to extract
+the vector of selected variables. An exception is made for the \code{Boruta} function,
+which includes an internal adjustment and requires no additional modifications.}
\item{varsel_param}{\code{list} \cr
List of arguments to be passed to \code{varsel_fct}.}
\item{lrner_package}{\code{character} \cr
-Name of the package containing the function that implements the learning algorithm.
-Default is \code{NULL}, if the function is available in the current working environment.}
+Name of the package containing the learning algorithm function. Defaults to
+\code{NULL} if the function is available in the current working environment.}
\item{lrn_fct}{\code{character} \cr
-Name of the learning function. The corresponding function must allow at least two parameters \code{x} (of predictors)
-and \code{y} (response values), and return a model. Otherwise, the interface
-parameters \code{x_lrn} and \code{y_lrn} below can be used to map these argument name with the original arguments in your function.
-The returned model must allow the use of the generic function \code{predict} (with arguments \code{object} and \code{data}) to make
-predictions for new data and predictions should be a vector or a \code{list} containing a vector called
-\code{predictions} with the predicted values. If the arguments \code{object} and \code{data} are named differently in your predict
-function, use the interface parameters \code{object} and \code{data} below to specify the original names. In addition, if
-predictions are stored as a \code{matrix} or a \code{data.frame} (e.g. predicted probabilities for different classes in dichotomous classification),
-only the second column (assumed to be the class \code{1} probabilities) will be considered. If the predicted values are not returned in one of the formats above,
-use the argument \code{extract_pred_fct} below to specify how the predicted values can be extracted from the predicted object.}
+Name of the learning function. The function must accept at least two parameters:
+\code{x} (predictors) and \code{y} (response values) and return a model. Alternatively,
+use the interface parameters \code{x_lrn} and \code{y_lrn} to map these names to the
+original arguments in your function.
+The returned model must support the generic \code{predict} function (with arguments
+\code{object} and \code{data}) to generate predictions for new data. Predictions should
+be either a vector or a \code{list} containing a vector named \code{predictions} with
+the predicted values.\cr
+If the arguments \code{object} and \code{data} have different names in your \code{predict}
+function, use the interface parameters below to map them to the original names.
+Additionally, if predictions are stored as a \code{matrix} or \code{data.frame}
+(e.g., predicted probabilities for dichotomous classification), only the second
+column (assumed to be class \code{1} probabilities) will be used.
+If the predicted values are not returned in one of the formats mentioned above,
+use the \code{extract_pred_fct} argument below to specify how to extract the predicted
+values from the prediction object.}
\item{param_train_list}{\code{character} \cr
List of arguments to be passed to \code{lrn_fct}.}
\item{param_pred_list}{\code{character} \cr
-List of arguments to be passed to \code{predict} when computing predictions.}
+List of arguments to be passed to \code{predict} when generating predictions.}
\item{na_action}{\code{character}\cr
-Handling of missing values in data a modality during training. Set to "na.keep" to keep missing values, or "na.rm" to remove individuals with missing values. Imputation of missing values in data modalities is not yet handled.}
+Handling of missing values in data during training. Set to \code{"na.keep"} to retain
+missing values, or \code{"na.rm"} to remove instances with missing values.}
\item{x_varsel}{\code{character} \cr
If the name of the argument used by the provided original variable selection function to pass
@@ -91,21 +101,21 @@ If the name of the argument used by the provided original learning function to p
the target variable is not \code{y}, use this argument to specify how it is called in the provided function.}
\item{object}{\code{character} \cr
-The generic function \code{predict} uses a parameter \code{object} to pass a model.
-If the corresponding argument is named differently in your predict function, specify the name.}
+The generic function \code{predict} uses the parameter \code{object} to pass a model.
+If the corresponding argument is named differently in your \code{predict} function, specify its name.}
\item{data}{\code{character} \cr
The generic function \code{predict} uses a parameter \code{data} to pass new data.
If the corresponding argument is named differently in your predict function, specify the name.}
\item{extract_pred_fct}{\verb{character or function} \cr
-If the predict function that is called for the model does not return a vector, then
-use this argument to specify a (or a name of a) function that can be used to extract vector of predictions.
-Default value is NULL, if predictions are rerturned as vector.}
+If the \code{predict} function called for the model does not return a vector,
+use this argument to specify a function (or the name of a function) to extract
+the vector of predictions. The default value is \code{NULL} if predictions are returned as a vector.}
\item{extract_var_fct}{\verb{character or function} \cr
-If the variable selection function that is called does not return a vector, then
-use this argument to specify a (or a name of a) function that can be used to extract vector of selected variables.}
+If the variable selection function does not return a vector, use this argument
+to specify a function (or the name of a function) to extract the vector of selected variables.}
}
\value{
The updated \link{Training} object (with the new layer) is returned.
diff --git a/man/createTrainMetaLayer.Rd b/man/createTrainMetaLayer.Rd
index 9f7d855..906e8f0 100644
--- a/man/createTrainMetaLayer.Rd
+++ b/man/createTrainMetaLayer.Rd
@@ -21,23 +21,26 @@ createTrainMetaLayer(
}
\arguments{
\item{training}{\code{Training} \cr
-Training object where the created layer will be stored.}
+Training object for storing the created meta-layer.}
\item{meta_layer_id}{\code{character} \cr
ID of the layer to be created.}
\item{lrner_package}{\code{character} \cr
-Name of the package containing the function that implements the learning algorithm.
-Default is \code{NULL}, if the function is available in the current working environment.}
+Package name containing the variable selection algorithm function.
+Defaults to \code{NULL} if the function exists in the current working environment.}
\item{lrn_fct}{\code{character} \cr
-Name of the learning function. The corresponding function must allow at least two parameters \code{x} (of predictors)
-and \code{y} (response values), and return a model. If not, the interface
-parameters \code{x_lrn} and \code{y_lrn} below can be used to map these argument name with the original arguments in your function.
-The returned model must allow the use of the generic function \code{predict} (with arguments \code{object} and \code{data}) to make
-predictions for new data and predictions should be a vector or a \code{list} containing a vector called
-\code{predictions} with the predicted values. See the details below about meta-learners. If the arguments \code{object} and \code{data} are named differently in your predict
-function, use the interface parameters \code{object} and \code{data} below to specify the original names.}
+Name of the learning function. The function must accept at least two
+parameters: \code{x} (predictors) and \code{y} (response values), and return a model.
+If not, use the interface parameters \code{x_lrn} and \code{y_lrn} below to map these
+argument names to the original arguments in your function. The returned model
+must support the generic \code{predict} function (with arguments \code{object} and \code{data})
+to make predictions for new data, and the predictions should be a vector or
+a \code{list} containing a vector called \code{predictions} with the predicted values.
+If the arguments \code{object} and \code{data} are named differently in your predict
+function, use the interface parameters \code{object} and \code{data} below to specify
+the original names. See the details below about meta-learners.}
\item{param_train_list}{\code{character} \cr
List of arguments to be passed to \code{lrn_fct}.}
@@ -46,15 +49,20 @@ List of arguments to be passed to \code{lrn_fct}.}
List of arguments to be passed to \code{predict} when computing predictions.}
\item{na_action}{\code{character}\cr
-Handling of missing values in modality-specific predictions during training. Set to "na.keep" to keep missing values, "na.rm" to remove individuals with missing values or "na.impute" to impute missing values in modality-specific predictions. Only median and mode based imputations are actually handled. With the "na.keep" option, ensure that the provided meta-learner can handle missing values.}
+Handling of missing values in modality-specific predictions during training.
+Set to \code{"na.keep"} to keep missing values, \code{"na.rm"} to remove individuals
+with missing values or \code{"na.impute"} to impute missing values in modality-specific
+predictions. Only median and mode based imputations are actually handled.
+With the \code{"na.keep"} option, ensure that the provided meta-learner can handle missing values.}
\item{x_lrn}{\code{character} \cr
-If the name of the argument used by the provided original functions to pass
-the matrix of independent variable is not \code{x}, use this argument to specify how it is called in the provided function.}
+If the argument name used by the provided original function to pass the matrix
+of independent variables is not \code{x}, use this argument to specify the name used
+in the function.}
\item{y_lrn}{\code{character} \cr
-If the name of the argument used by the provided original functions to pass
-the target variable is not \code{y}, use this argument to specify how it is called in the provided function.}
+If the argument name used by the provided original function to pass the target
+variable is not \code{y}, use this argument to specify the name used in the function.}
\item{object}{\code{character} \cr
The generic function \code{predict} uses a parameter \code{object} to pass a model.
@@ -67,7 +75,7 @@ If the corresponding argument is named differently in your predict function, spe
\item{extract_pred_fct}{\verb{character or function} \cr
If the predict function that is called for the model does not return a vector, then
use this argument to specify a (or a name of a) function that can be used to extract vector of predictions.
-Default value is NULL, if predictions are in a vector.}
+Defaults to NULL, if predictions are a vector.}
}
\value{
The updated \link{Training} object (with the new layer) is returned.
diff --git a/man/fusemlr.Rd b/man/fusemlr.Rd
index 87bc95e..87c4cb7 100644
--- a/man/fusemlr.Rd
+++ b/man/fusemlr.Rd
@@ -15,17 +15,19 @@ fusemlr(
}
\arguments{
\item{training}{\code{Training} \cr
-Training object where the created layer will be stored.}
+Training object for storing training layers.}
\item{ind_subset}{\code{vector} \cr
ID subset to be used for training.}
\item{use_var_sel}{\code{boolean} \cr
-If TRUE and no variable selection has been performed for the provide training object, then a variable selection will proceed the training.
+If \code{TRUE} and no variable selection has been performed for the provide training object,
+then a variable selection will proceed the training.
Otherwise, if variable selection has been previously performed, the selected variables will be used for training.}
\item{resampling_method}{\code{function} \cr
-Function for internal validation. If not specify, the \code{resampling} function from the package \code{caret} is used for a 10-folds cross-validation.}
+Function for internal validation. If not specify, the \code{resampling} function
+from the package \code{caret} is used for a 10-folds cross-validation.}
\item{resampling_arg}{\code{list} \cr
List of arguments to be passed to the function.}
diff --git a/man/predict.Training.Rd b/man/predict.Training.Rd
index 9a4bf37..e1df049 100644
--- a/man/predict.Training.Rd
+++ b/man/predict.Training.Rd
@@ -8,7 +8,7 @@
}
\arguments{
\item{object}{\code{Training} \cr
-Training object to be used to compute predictions.}
+A trained Training object to be used to compute predictions.}
\item{testing}{\code{Testing} \cr
A new testing object to be predicted.}
@@ -17,7 +17,7 @@ A new testing object to be predicted.}
Vector of IDs to be predicted.}
}
\value{
-The predicted object. All layers and the meta layer are predicted. This is the final predicted object.
+The final predicted object. All layers and the meta layer are predicted.
}
\description{
Computes predictions for the \link{Testing} object passed as argument.
diff --git a/man/summary.Testing.Rd b/man/summary.Testing.Rd
index 278fbca..878f6f7 100644
--- a/man/summary.Testing.Rd
+++ b/man/summary.Testing.Rd
@@ -14,5 +14,5 @@ The \link{Testing} object of interest.}
Further arguments.}
}
\description{
-Summaries a fuseMLR \link{Testing} object.
+Summaries a \code{fuseMLR} \link{Testing} object.
}
diff --git a/man/summary.Training.Rd b/man/summary.Training.Rd
index 0060a5e..5c41e42 100644
--- a/man/summary.Training.Rd
+++ b/man/summary.Training.Rd
@@ -14,5 +14,5 @@ The \link{Training} object of interest.}
Further arguments.}
}
\description{
-Summaries a fuseMLR \link{Training} object.
+Summaries a \code{fuseMLR} \link{Training} object.
}
diff --git a/man/varSelection.Rd b/man/varSelection.Rd
index 6ef631a..24f369c 100644
--- a/man/varSelection.Rd
+++ b/man/varSelection.Rd
@@ -8,7 +8,7 @@ varSelection(training, ind_subset = NULL)
}
\arguments{
\item{training}{\code{Training} \cr
-Training object where the created layer will be stored.}
+Training object for storing the created layer.}
\item{ind_subset}{\code{vector} \cr
ID subset of individuals to be used for variable selection.}
@@ -17,7 +17,7 @@ ID subset of individuals to be used for variable selection.}
A \code{data.frame} with two columns: layer and selected variables.
}
\description{
-Variable selection on the current training object.
+Variable selection on the training object passed as argument.
}
\references{
Fouodo C.J.K, Bleskina M. and Szymczak (2024). fuseMLR: An R package for integrative prediction modeling of multi-omics data, paper submitted. \cr
diff --git a/vignettes/fuseMLR.Rmd b/vignettes/fuseMLR.Rmd
index c24d1e1..ab058ad 100644
--- a/vignettes/fuseMLR.Rmd
+++ b/vignettes/fuseMLR.Rmd
@@ -86,7 +86,7 @@ createTrainLayer(training = training,
param_train_list = list(probability = TRUE,
mtry = 1L),
param_pred_list = list(),
- na_action = "na.rm")
+ na_action = "na.keep")
```
```{r proteinexpr, include=TRUE, eval=TRUE}