minor fixes in texinfo

inst/Classification/ClassificationDiscriminant.m

@@ -48,41 +48,41 @@
 ## training data and various parameters for the discriminant analysis model,
 ## which can be accessed in the following fields:
 ##
-## @multitable @columnfractions 0.28 0.02 0.7
+## @multitable @columnfractions 0.23 0.02 0.75
 ## @headitem @var{Field} @tab @tab @var{Description}
 ##
-## @item @qcode{obj.X} @tab @tab Unstandardized predictor data, specified as a
+## @item @qcode{X} @tab @tab Unstandardized predictor data, specified as a
 ## numeric matrix.  Each column of @var{X} represents one predictor (variable),
 ## and each row represents one observation.
 ##
-## @item @qcode{obj.Y} @tab @tab Class labels, specified as a logical,
+## @item @qcode{Y} @tab @tab Class labels, specified as a logical,
 ## numeric vector, or cell array of character vectors. Each value in @var{Y}
 ## is the observed class label for the corresponding row in @var{X}.
 ##
-## @item @qcode{obj.NumObservations} @tab @tab Number of observations used in
+## @item @qcode{NumObservations} @tab @tab Number of observations used in
 ## training the ClassificationDiscriminant model, specified as a positive
 ## integer scalar.
 ##
-## @item @qcode{obj.RowsUsed} @tab @tab Rows of the original training data
+## @item @qcode{RowsUsed} @tab @tab Rows of the original training data
 ## used in fitting the ClassificationDiscriminant model, specified as a
 ## numerical vector.
 ##
-## @item @qcode{obj.PredictorNames} @tab @tab Predictor variable names,
+## @item @qcode{PredictorNames} @tab @tab Predictor variable names,
 ## specified as a cell array of character vectors. The variable names are in
 ## the same order in which they appear in the training data @var{X}.
 ##
-## @item @qcode{obj.ResponseName} @tab @tab Response variable name, specified
+## @item @qcode{ResponseName} @tab @tab Response variable name, specified
 ## as a character vector.
 ##
-## @item @qcode{obj.ClassNames} @tab @tab Names of the classes in the training
+## @item @qcode{ClassNames} @tab @tab Names of the classes in the training
 ## data @var{Y} with duplicates removed, specified as a cell array of character
 ## vectors.
 ##
-## @item @qcode{obj.Prior} @tab @tab Prior probabilities for each class,
+## @item @qcode{Prior} @tab @tab Prior probabilities for each class,
 ## specified as a numeric vector.  The order of the elements in @qcode{Prior}
 ## corresponds to the order of the classes in @qcode{ClassNames}.
 ##
-## @item @qcode{obj.Cost} @tab @tab Cost of the misclassification of a point,
+## @item @qcode{Cost} @tab @tab Cost of the misclassification of a point,
 ## specified as a square matrix. @qcode{Cost(i,j)} is the cost of classifying a
 ## point into class @qcode{j} if its true class is @qcode{i} (that is, the rows
 ## correspond to the true class and the columns correspond to the predicted
@@ -93,34 +93,34 @@
 ## @qcode{i = j}.  In other words, the cost is 0 for correct classification and
 ## 1 for incorrect classification.
 ##
-## @item @qcode{obj.Sigma} @tab @tab Within-class covariance matrix, specified
+## @item @qcode{Sigma} @tab @tab Within-class covariance matrix, specified
 ## as a numeric matrix. For 'linear' discriminant type matrix is of size
 ## @math{pxp}, where p is the number of predictors.
 ##
-## @item @qcode{obj.Mu} @tab @tab Class means, specified as a @math{Kxp}
+## @item @qcode{Mu} @tab @tab Class means, specified as a @math{Kxp}
 ## real matrix. K is the number of classes, and p is the number of
 ## predictors.
 ##
-## @item @qcode{obj.Coeffs} @tab @tab Coefficient matrices, specified as a
+## @item @qcode{Coeffs} @tab @tab Coefficient matrices, specified as a
 ## struct array.
 ##
-## @item @qcode{obj.Delta} @tab @tab Threshold for linear discriminant model,
+## @item @qcode{Delta} @tab @tab Threshold for linear discriminant model,
 ## specified as a numeric scalar.
 ##
-## @item @qcode{obj.DiscrimType} @tab @tab Discriminant type, specified as a
+## @item @qcode{DiscrimType} @tab @tab Discriminant type, specified as a
 ## character vector.
 ##
-## @item @qcode{obj.Gamma} @tab @tab Gamma regularization parameter, specified
+## @item @qcode{Gamma} @tab @tab Gamma regularization parameter, specified
 ## as a numeric scalar.
 ##
-## @item @qcode{obj.MinGamma} @tab @tab Minimum value of Gamma so that the
+## @item @qcode{MinGamma} @tab @tab Minimum value of Gamma so that the
 ## correlation matrix is invertible, specified as nonnegative scalar.
 ##
-## @item @qcode{obj.LogDetSigma} @tab @tab Logarithm of the determinant of the
+## @item @qcode{LogDetSigma} @tab @tab Logarithm of the determinant of the
 ## within-class covariance matrix. For linear discriminant analysis it is
 ## specified as a numeric scalar.
 ##
-## @item @qcode{obj.XCentered} @tab @tab X data with class means
+## @item @qcode{XCentered} @tab @tab X data with class means
 ## subtracted, returned as a real matrix.
 ##
 ## @end multitable

inst/Classification/ClassificationGAM.m

@@ -49,57 +49,57 @@
 ## data and various parameters for the Generalized Additive Model (GAM) for
 ## classification, which can be accessed in the following fields:
 ##
-## @multitable @columnfractions 0.28 0.02 0.7
+## @multitable @columnfractions 0.23 0.02 0.75
 ## @headitem @var{Field} @tab @tab @var{Description}
 ##
-## @item @qcode{obj.X} @tab @tab Predictor data, specified as a
+## @item @qcode{X} @tab @tab Predictor data, specified as a
 ## numeric matrix. Each column of @var{X} represents one predictor (variable),
 ## and each row represents one observation.
 ##
-## @item @qcode{obj.Y} @tab @tab Class labels, specified as numeric vector
+## @item @qcode{Y} @tab @tab Class labels, specified as numeric vector
 ## of 0's and 1's. Each value in @var{Y} is the observed class
 ## label for the corresponding row in @var{X}.
 ##
-## @item @qcode{obj.BaseModel} @tab @tab A structure containing the parameters
+## @item @qcode{BaseModel} @tab @tab A structure containing the parameters
 ## of the base model without any interaction terms. The base model represents
 ## the generalized additive model (GAM) with only the main effects (predictor
 ## terms) included.
 ##
-## @item @qcode{obj.ModelwInt} @tab @tab A structure containing the parameters
+## @item @qcode{ModelwInt} @tab @tab A structure containing the parameters
 ## of the model that includes interaction terms. This model extends the base
 ## model by adding interaction terms between predictors, as specified by the
 ## @qcode{Interactions} property.
 ##
-## @item @qcode{obj.IntMatrix} @tab @tab A logical matrix or a matrix of
+## @item @qcode{IntMatrix} @tab @tab A logical matrix or a matrix of
 ## column indices that describes the interaction terms applied to the predictor
 ## data.
 ##
-## @item @qcode{obj.NumObservations} @tab @tab Number of observations used in
+## @item @qcode{NumObservations} @tab @tab Number of observations used in
 ## training the ClassificationGAM model, specified as a positive integer scalar.
 ## This number can be less than the number of rows in the training data because
 ## rows containing @qcode{NaN} values are not part of the fit.
 ##
-## @item @qcode{obj.RowsUsed} @tab @tab Rows of the original training data
+## @item @qcode{RowsUsed} @tab @tab Rows of the original training data
 ## used in fitting the ClassificationGAM model, specified as a numerical vector.
 ## If you want to use this vector for indexing the training data in @var{X}, you
 ## have to convert it to a logical vector, i.e
 ## @qcode{X = obj.X(logical (obj.RowsUsed), :);}
 ##
-## @item @qcode{obj.NumPredictors} @tab @tab The number of predictors
+## @item @qcode{NumPredictors} @tab @tab The number of predictors
 ## (variables) in @var{X}.
 ##
-## @item @qcode{obj.PredictorNames} @tab @tab Predictor variable names,
+## @item @qcode{PredictorNames} @tab @tab Predictor variable names,
 ## specified as a cell array of character vectors. The variable names are in
 ## the same order in which they appear in the training data @var{X}.
 ##
-## @item @qcode{obj.ResponseName} @tab @tab Response variable name, specified
+## @item @qcode{ResponseName} @tab @tab Response variable name, specified
 ## as a character vector.
 ##
-## @item @qcode{obj.ClassNames} @tab @tab Names of the classes in the training
+## @item @qcode{ClassNames} @tab @tab Names of the classes in the training
 ## data @var{Y} with duplicates removed, specified as a cell array of character
 ## vectors.
 ##
-## @item @qcode{obj.Cost} @tab @tab Cost of the misclassification of a point,
+## @item @qcode{Cost} @tab @tab Cost of the misclassification of a point,
 ## specified as a square matrix. @qcode{Cost(i,j)} is the cost of classifying a
 ## point into class @qcode{j} if its true class is @qcode{i} (that is, the rows
 ## correspond to the true class and the columns correspond to the predicted
@@ -110,7 +110,7 @@
 ## @qcode{i = j}.  In other words, the cost is 0 for correct classification and
 ## 1 for incorrect classification.
 ##
-## @item @qcode{obj.Formula} @tab @tab A model specification given as a string
+## @item @qcode{Formula} @tab @tab A model specification given as a string
 ## in the form @qcode{"Y ~ terms"} where @qcode{Y} represents the reponse
 ## variable and @qcode{terms} the predictor variables. The formula can be used
 ## to specify a subset of variables for training model. For example:
@@ -125,7 +125,7 @@
 ## @var{obj} class object as a matrix containing the column indexes for each
 ## term including both the predictors and the interactions used.
 ##
-## @item @qcode{obj.Interactions} @tab @tab A logical matrix, a positive integer
+## @item @qcode{Interactions} @tab @tab A logical matrix, a positive integer
 ## scalar, or the string @qcode{"all"} for defining the interactions between
 ## predictor variables.  When given a logical matrix, it must have the same
 ## number of columns as @var{X} and each row corresponds to a different
@@ -138,21 +138,21 @@
 ## appended to @var{X} are referenced in the @qcode{IntMatrix} field of the
 ## @var{obj} class object.
 ##
-## @item @qcode{obj.Knots} @tab @tab A scalar or a row vector with the same
+## @item @qcode{Knots} @tab @tab A scalar or a row vector with the same
 ## columns as @var{X}.  It defines the knots for fitting a polynomial when
 ## training the GAM.  As a scalar, it is expanded to a row vector.  The default
 ## value is 5, hence expanded to @qcode{ones (1, columns (X)) * 5}.  You can
 ## parse a row vector with different number of knots for each predictor
 ## variable to be fitted with, although not recommended.
 ##
-## @item @qcode{obj.Order} @tab @tab A scalar or a row vector with the same
+## @item @qcode{Order} @tab @tab A scalar or a row vector with the same
 ## columns as @var{X}.  It defines the order of the polynomial when training the
 ## GAM.  As a scalar, it is expanded to a row vector.  The default values is 3,
 ## hence expanded to @qcode{ones (1, columns (X)) * 3}.  You can parse a row
 ## vector with different number of polynomial order for each predictor variable
 ## to be fitted with, although not recommended.
 ##
-## @item @qcode{obj.DoF} @tab @tab A scalar or a row vector with the same
+## @item @qcode{DoF} @tab @tab A scalar or a row vector with the same
 ## columns as @var{X}.  It defines the degrees of freedom for fitting a
 ## polynomial when training the GAM.  As a scalar, it is expanded to a row
 ## vector.  The default value is 8, hence expanded to

inst/Classification/ClassificationKNN.m

@@ -48,55 +48,55 @@
 ## data and various parameters for the k-Nearest Neighbor classification model,
 ## which can be accessed in the following fields:
 ##
-## @multitable @columnfractions 0.28 0.02 0.7
+## @multitable @columnfractions 0.23 0.02 0.75
 ## @headitem @var{Field} @tab @tab @var{Description}
 ##
-## @item @qcode{obj.X} @tab @tab Unstandardized predictor data, specified as a
+## @item @qcode{X} @tab @tab Unstandardized predictor data, specified as a
 ## numeric matrix.  Each column of @var{X} represents one predictor (variable),
 ## and each row represents one observation.
 ##
-## @item @qcode{obj.Y} @tab @tab Class labels, specified as a logical or
+## @item @qcode{Y} @tab @tab Class labels, specified as a logical or
 ## numeric vector, or cell array of character vectors.  Each value in @var{Y} is
 ## the observed class label for the corresponding row in @var{X}.
 ##
-## @item @qcode{obj.NumObservations} @tab @tab Number of observations used in
+## @item @qcode{NumObservations} @tab @tab Number of observations used in
 ## training the ClassificationKNN model, specified as a positive integer scalar.
 ## This number can be less than the number of rows in the training data because
 ## rows containing @qcode{NaN} values are not part of the fit.
 ##
-## @item @qcode{obj.RowsUsed} @tab @tab Rows of the original training data
+## @item @qcode{RowsUsed} @tab @tab Rows of the original training data
 ## used in fitting the ClassificationKNN model, specified as a numerical vector.
 ## If you want to use this vector for indexing the training data in @var{X}, you
 ## have to convert it to a logical vector, i.e
 ## @qcode{X = obj.X(logical (obj.RowsUsed), :);}
 ##
-## @item @qcode{obj.Standardize} @tab @tab A boolean flag indicating whether
+## @item @qcode{Standardize} @tab @tab A boolean flag indicating whether
 ## the data in @var{X} have been standardized prior to training.
 ##
-## @item @qcode{obj.Sigma} @tab @tab Predictor standard deviations, specified
+## @item @qcode{Sigma} @tab @tab Predictor standard deviations, specified
 ## as a numeric vector of the same length as the columns in @var{X}.  If the
 ## predictor variables have not been standardized, then @qcode{"obj.Sigma"} is
 ## empty.
 ##
-## @item @qcode{obj.Mu} @tab @tab Predictor means, specified as a numeric
+## @item @qcode{Mu} @tab @tab Predictor means, specified as a numeric
 ## vector of the same length as the columns in @var{X}.  If the predictor
 ## variables have not been standardized, then @qcode{"obj.Mu"} is empty.
 ##
-## @item @qcode{obj.NumPredictors} @tab @tab The number of predictors
+## @item @qcode{NumPredictors} @tab @tab The number of predictors
 ## (variables) in @var{X}.
 ##
-## @item @qcode{obj.PredictorNames} @tab @tab Predictor variable names,
+## @item @qcode{PredictorNames} @tab @tab Predictor variable names,
 ## specified as a cell array of character vectors.  The variable names are in
 ## the same order in which they appear in the training data @var{X}.
 ##
-## @item @qcode{obj.ResponseName} @tab @tab Response variable name, specified
+## @item @qcode{ResponseName} @tab @tab Response variable name, specified
 ## as a character vector.
 ##
-## @item @qcode{obj.ClassNames} @tab @tab Names of the classes in the training
+## @item @qcode{ClassNames} @tab @tab Names of the classes in the training
 ## data @var{Y} with duplicates removed, specified as a cell array of character
 ## vectors.
 ##
-## @item @qcode{obj.BreakTies} @tab @tab Tie-breaking algorithm used by predict
+## @item @qcode{BreakTies} @tab @tab Tie-breaking algorithm used by predict
 ## when multiple classes have the same smallest cost, specified as one of the
 ## following character arrays: @qcode{"smallest"} (default), which favors the
 ## class with the smallest index among the tied groups, i.e. the one that
@@ -105,11 +105,11 @@
 ## with the closest member point according to the distance metric used.
 ## @qcode{"random"}, which randomly picks one class among the tied groups.
 ##
-## @item @qcode{obj.Prior} @tab @tab Prior probabilities for each class,
+## @item @qcode{Prior} @tab @tab Prior probabilities for each class,
 ## specified as a numeric vector.  The order of the elements in @qcode{Prior}
 ## corresponds to the order of the classes in @qcode{ClassNames}.
 ##
-## @item @qcode{obj.Cost} @tab @tab Cost of the misclassification of a point,
+## @item @qcode{Cost} @tab @tab Cost of the misclassification of a point,
 ## specified as a square matrix. @qcode{Cost(i,j)} is the cost of classifying a
 ## point into class @qcode{j} if its true class is @qcode{i} (that is, the rows
 ## correspond to the true class and the columns correspond to the predicted
@@ -120,46 +120,46 @@
 ## @qcode{i = j}.  In other words, the cost is 0 for correct classification and
 ## 1 for incorrect classification.
 ##
-## @item @qcode{obj.ScoreTransform} @tab @tab A function_handle which is used
+## @item @qcode{ScoreTransform} @tab @tab A function_handle which is used
 ## for transforming the kNN prediction score into a posterior probability.  By
 ## default, it is @qcode{'none'}, in which case the @code{predict} and
 ## @code{resubPredict} methods return the prediction scores.
 ##
-## @item @qcode{obj.NumNeighbors} @tab @tab Number of nearest neighbors in
+## @item @qcode{NumNeighbors} @tab @tab Number of nearest neighbors in
 ## @var{X} used to classify each point during prediction, specified as a
 ## positive integer value.
 ##
-## @item @qcode{obj.Distance} @tab @tab Distance metric, specified as a
+## @item @qcode{Distance} @tab @tab Distance metric, specified as a
 ## character vector.  The allowable distance metric names depend on the choice
 ## of the neighbor-searcher method.  See the available distance metrics in
 ## @code{knnseaarch} for more info.
 ##
-## @item @qcode{obj.DistanceWeight} @tab @tab Distance weighting function,
+## @item @qcode{DistanceWeight} @tab @tab Distance weighting function,
 ## specified as a function handle, which accepts a matrix of nonnegative
 ## distances, and returns a matrix the same size containing nonnegative distance
 ## weights.
 ##
-## @item @qcode{obj.DistParameter} @tab @tab Parameter for the distance
+## @item @qcode{DistParameter} @tab @tab Parameter for the distance
 ## metric, specified either as a positive definite covariance matrix (when the
 ## distance metric is @qcode{"mahalanobis"}, or a positive scalar as the
 ## Minkowski distance exponent (when the distance metric is @qcode{"minkowski"},
 ## or a vector of positive scale values with length equal to the number of
 ## columns of @var{X} (when the distance metric is @qcode{"seuclidean"}.  For
 ## any other distance metric, the value of @qcode{DistParameter} is empty.
 ##
-## @item @qcode{obj.NSMethod} @tab @tab Nearest neighbor search method,
+## @item @qcode{NSMethod} @tab @tab Nearest neighbor search method,
 ## specified as either @qcode{"kdtree"}, which creates and uses a Kd-tree to
 ## find nearest neighbors, or @qcode{"exhaustive"}, which uses the exhaustive
 ## search algorithm by computing the distance values from all points in @var{X}
 ## to find nearest neighbors.
 ##
-## @item @qcode{obj.IncludeTies} @tab @tab A boolean flag indicating whether
+## @item @qcode{IncludeTies} @tab @tab A boolean flag indicating whether
 ## prediction includes all the neighbors whose distance values are equal to the
 ## @math{k^th} smallest distance.  If @qcode{IncludeTies} is @qcode{true},
 ## prediction includes all of these neighbors.  Otherwise, prediction uses
 ## exactly @math{k} neighbors.
 ##
-## @item @qcode{obj.BucketSize} @tab @tab Maximum number of data points in the
+## @item @qcode{BucketSize} @tab @tab Maximum number of data points in the
 ## leaf node of the Kd-tree, specified as positive integer value. This argument
 ## is meaningful only when @qcode{NSMethod} is @qcode{"kdtree"}.
 ##