Merge pull request #43 from sem-in-r/documents

added docs for path_weighting and outer_weighting schemes.

Author: NicholasDanks

R/library.R

@@ -31,14 +31,50 @@ mmMatrix_per_latent <- function(latent, mmMatrix) {
   return(latentmatrix)
 }
 
-# Factorial weighting scheme Function to create inner paths matrix
+#' Inner weighting scheme functions to estimate inner paths matrix
+#'
+#' \code{path_factorial} and \code{path_weighting} specify the inner weighting scheme to be used in the estimation of the
+#' inner paths matrix
+#'
+#' @param smMatrix is the \code{structural_model} - a source-to-target matrix representing the inner/structural model,
+#'  generated by \code{relationships}.
+#'
+#' @param fscores is the matrix of construct scores generated by \code{simplePLS}.
+#'
+#' @param dependant is the vector of dependant constructs in the model.
+#'
+#' @param paths_matrix is the matrix of estimated path coefficients estimated by \code{simplePLS}.
+#'
+#' @usage
+#'  path_factorial(smMatrix,fscores, dependant, paths_matrix)
+#'
+#' @references Lohmoller, J.-B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica- Verlag.
+#'
 #' @export
 path_factorial <- function(smMatrix,fscores, dependant, paths_matrix) {
   inner_paths <- stats::cor(fscores,fscores) * (paths_matrix + t(paths_matrix))
   return(inner_paths)
 }
 
-# Factorial weighting scheme Function to create inner paths matrix
+#' Inner weighting scheme functions to estimate inner paths matrix
+#'
+#' \code{path_factorial} and \code{path_weighting} specify the inner weighting scheme to be used in the estimation of the
+#' inner paths matrix
+#'
+#' @param smMatrix is the \code{structural_model} - a source-to-target matrix representing the inner/structural model,
+#'  generated by \code{relationships}.
+#'
+#' @param fscores is the matrix of construct scores generated by \code{simplePLS}.
+#'
+#' @param dependant is the vector of dependant constructs in the model.
+#'
+#' @param paths_matrix is the matrix of estimated path coefficients estimated by \code{simplePLS}.
+#'
+#' @usage
+#'  path_weighting(smMatrix,fscores, dependant, paths_matrix)
+#'
+#' @references Lohmoller, J.B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica-Verlag.
+#'
 #' @export
 path_weighting <- function(smMatrix, fscores, dependant, paths_matrix) {
   # correlations for outgoing paths
@@ -104,20 +140,56 @@ standardize_outer_weights <- function(normData, mmVariables, outer_weights) {
   return(t(t(outer_weights) / std_devs))
 }
 
+#' Outer weighting scheme functions to estimate construct weighting.
+#'
+#' \code{mode_A}, \code{correlation_weights} and \code{mode_B}, \code{regression_weights} specify the outer weighting
+#' scheme to be used in the estimation of the construct weights and score.
+#'
+#' @param mmMatrix is the \code{measurement_model} - a source-to-target matrix representing the measurement model,
+#'  generated by \code{constructs}.
+#'
+#' @param i is the name of the construct to be estimated.
+#'
+#' @param normData is the dataframe of the normalized item data.
+#'
+#' @param fscores is the matrix of construct scores generated by \code{simplePLS}.
+#'
+#' @usage
+#'  mode_A(mmMatrix, i, normData, fscores)
+#'
+#' @aliases mode_A, correlation_weights
+#'
 #' @export
 mode_A  <- function(mmMatrix, i, normData, fscores) {
     return(stats::cov(normData[,mmMatrix[mmMatrix[,"latent"]==i,"measurement"]],fscores[,i]))
 }
-
 #' @export
 correlation_weights <- mode_A
 
+#' Outer weighting scheme functions to estimate construct weighting.
+#'
+#' \code{mode_A}, \code{correlation_weights} and \code{mode_B}, \code{regression_weights} specify the outer weighting
+#' scheme to be used in the estimation of the construct weights and score.
+#'
+#' @param mmMatrix is the \code{measurement_model} - a source-to-target matrix representing the measurement model,
+#'  generated by \code{constructs}.
+#'
+#' @param i is the name of the construct to be estimated.
+#'
+#' @param normData is the dataframe of the normalized item data.
+#'
+#' @param fscores is the matrix of construct scores generated by \code{simplePLS}.
+#'
+#' @usage
+#'  mode_B(mmMatrix, i, normData, fscores)
+#'
+#' @aliases mode_B, regression_weights
+#'
 #' @export
 mode_B <- function(mmMatrix, i,normData, fscores) {
     return(solve(stats::cor(normData[,mmMatrix[mmMatrix[,"latent"]==i,"measurement"]])) %*%
     stats::cor(normData[,mmMatrix[mmMatrix[,"latent"]==i,"measurement"]],
                fscores[,i]))
 }
-
 #' @export
 regression_weights <- mode_B