Merge pull request #21 from ocbe-uio/issue-5

Fixed documentation for C++ function (#5)

NAMESPACE

@@ -5,6 +5,7 @@ S3method(plot,cv_MADMMplasso)
 S3method(predict,MADMMplasso)
 export(MADMMplasso)
 export(admm_MADMMplasso)
+export(admm_MADMMplasso_cpp)
 export(compute_pliable)
 export(cv_MADMMplasso)
 export(generate_my_w)

R/RcppExports.R

@@ -1,6 +1,38 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
+#' Fit the ADMM part of  model for a given lambda vale
+#' @param X  n by p matrix of predictors
+#' @param Z n by nz matrix of modifying variables. The elements of z
+#' may represent quantitative or categorical variables, or a mixture of the two.
+#' Categorical varables should be coded by 0-1 dummy variables: for a k-level
+#' variable, one can use either k or k-1  dummy variables.
+#' @param beta0 a vector of length ncol(y) of estimated beta_0 coefficients
+#' @param theta0 matrix of the initial theta_0 coefficients  ncol(Z) by ncol(y)
+#' @param beta  a matrix of the initial beta coefficients    ncol(X) by ncol(y)
+#' @param beta_hat  a matrix of the initial beta and theta coefficients    (ncol(X)+ncol(X) by ncol(Z)) by ncol(y)
+#' @param theta an array of initial theta coefficients    ncol(X) by ncol(Z) by ncol(y)
+#' @param rho1 the Lagrange variable for the ADMM which is usually included as rho in the MADMMplasso call.
+#' @param max_it maximum number of iterations in loop for one lambda during the ADMM optimization. This is usually included  in the MADMMplasso call
+#' @param W_hat N by (p+(p by nz)) of the main and interaction predictors. This generated internally  when MADMMplasso is called or by using the function generate_my_w.
+#' @param XtY a matrix formed by multiplying the transpose of X by y.
+#' @param y  N by D matrix  of responses. The X and Z variables are centered in the function. We recommend that X and Z also be standardized before the call
+#' @param N nrow(X)
+#' @param e_abs absolute error for the admm. This is included int the call of MADMMplasso.
+#' @param e_rel relative error for the admm. This is included int the call of MADMMplasso.
+#' @param alpha mixing parameter, usually obtained from the MADMMplasso call. When the goal is to include more interactions, alpha should be very small and vice versa.
+#' @param lambda a vector  lambda_3 values for the admm call with length ncol(y). This is usually calculated in the MADMMplasso call.   In our current setting, we use the same the lambda_3 value for all responses.
+#' @param alph an overrelaxation parameter in \[1, 1.8\], usually obtained from the MADMMplasso call.
+#' @param svd_w singular value decomposition of W
+#' @param tree The results from the hierarchical clustering of the response matrix.
+#' The easy way to obtain this is by using the function (tree_parms) which gives a default clustering.
+#' However, user decide on a specific structure and then input a tree that follows such structure.
+#' @param my_print Should information form each ADMM iteration be printed along the way? Default TRUE. This prints  the dual and primal residuals
+#' @param invmat A list of length ncol(y), each containing the C_d part of equation 32 in the paper
+#' @param gg penalty terms for the tree structure for lambda_1 and  lambda_2 for the admm call.
+#' @return  predicted values for the ADMM part
+#' @description TODO: add description
+#' @export
 admm_MADMMplasso_cpp <- function(beta0, theta0, beta, beta_hat, theta, rho1, X, Z, max_it, W_hat, XtY, y, N, e_abs, e_rel, alpha, lambda, alph, svd_w, tree, invmat, gg, my_print = TRUE) {
     .Call(`_MADMMplasso_admm_MADMMplasso_cpp`, beta0, theta0, beta, beta_hat, theta, rho1, X, Z, max_it, W_hat, XtY, y, N, e_abs, e_rel, alpha, lambda, alph, svd_w, tree, invmat, gg, my_print)
 }

src/admm_MADMMplasso.cpp

@@ -2,46 +2,38 @@
 #include "misc.h"
 
 // [[Rcpp::depends(RcppArmadillo)]]
-// #' Fit the ADMM part of  model for a given lambda vale
-// #' @param X  n by p matrix of predictors
-// #' @param Z n by nz matrix of modifying variables. The elements of z
-// #' may represent quantitative or categorical variables, or a mixture of the two.
-// #' Categorical varables should be coded by 0-1 dummy variables: for a k-level
-// #' variable, one can use either k or k-1  dummy variables.
-// #' @param beta0 TODO: fill paramater description
-// #' @param theta0 TODO: fill paramater description
-// #' @param beta TODO: fill paramater description
-// #' @param beta_hat TODO: fill paramater description
-// #' @param theta TODO: fill paramater description
-// #' @param rho1 TODO: fill paramater description
-// #' @param max_it TODO: fill paramater description
-// #' @param W_hat TODO: fill paramater description
-// #' @param XtY TODO: fill paramater description
-// #' @param y TODO: fill paramater description
-// #' @param N TODO: fill paramater description
-// #' @param p TODO: fill paramater description
-// #' @param K TODO: fill paramater description
-// #' @param e.abs TODO: fill paramater description
-// #' @param e.rel TODO: fill paramater description
-// #' @param alpha TODO: fill paramater description
-// #' @param lambda TODO: fill paramater description
-// #' @param alph TODO: fill paramater description
-// #' @param svd.w TODO: fill paramater description
-// #' @param tree TODO: fill paramater description
-// #' @param my_print TODO: fill paramater description
-// #' @param invmat TODO: fill paramater description
-// #' @param V TODO: fill paramater description
-// #' @param Q TODO: fill paramater description
-// #' @param E TODO: fill paramater description
-// #' @param EE TODO: fill paramater description
-// #' @param O TODO: fill paramater description
-// #' @param P TODO: fill paramater description
-// #' @param H TODO: fill paramater description
-// #' @param HH TODO: fill paramater description
-// #' @param gg TODO: fill paramater description
-// #' @return  predicted values for the ADMM part
-// #' @description TODO: add description
-// #' @export
+//' Fit the ADMM part of  model for a given lambda vale
+//' @param X  n by p matrix of predictors
+//' @param Z n by nz matrix of modifying variables. The elements of z
+//' may represent quantitative or categorical variables, or a mixture of the two.
+//' Categorical varables should be coded by 0-1 dummy variables: for a k-level
+//' variable, one can use either k or k-1  dummy variables.
+//' @param beta0 a vector of length ncol(y) of estimated beta_0 coefficients
+//' @param theta0 matrix of the initial theta_0 coefficients  ncol(Z) by ncol(y)
+//' @param beta  a matrix of the initial beta coefficients    ncol(X) by ncol(y)
+//' @param beta_hat  a matrix of the initial beta and theta coefficients    (ncol(X)+ncol(X) by ncol(Z)) by ncol(y)
+//' @param theta an array of initial theta coefficients    ncol(X) by ncol(Z) by ncol(y)
+//' @param rho1 the Lagrange variable for the ADMM which is usually included as rho in the MADMMplasso call.
+//' @param max_it maximum number of iterations in loop for one lambda during the ADMM optimization. This is usually included  in the MADMMplasso call
+//' @param W_hat N by (p+(p by nz)) of the main and interaction predictors. This generated internally  when MADMMplasso is called or by using the function generate_my_w.
+//' @param XtY a matrix formed by multiplying the transpose of X by y.
+//' @param y  N by D matrix  of responses. The X and Z variables are centered in the function. We recommend that X and Z also be standardized before the call
+//' @param N nrow(X)
+//' @param e_abs absolute error for the admm. This is included int the call of MADMMplasso.
+//' @param e_rel relative error for the admm. This is included int the call of MADMMplasso.
+//' @param alpha mixing parameter, usually obtained from the MADMMplasso call. When the goal is to include more interactions, alpha should be very small and vice versa.
+//' @param lambda a vector  lambda_3 values for the admm call with length ncol(y). This is usually calculated in the MADMMplasso call.   In our current setting, we use the same the lambda_3 value for all responses.
+//' @param alph an overrelaxation parameter in \[1, 1.8\], usually obtained from the MADMMplasso call.
+//' @param svd_w singular value decomposition of W
+//' @param tree The results from the hierarchical clustering of the response matrix.
+//' The easy way to obtain this is by using the function (tree_parms) which gives a default clustering.
+//' However, user decide on a specific structure and then input a tree that follows such structure.
+//' @param my_print Should information form each ADMM iteration be printed along the way? Default TRUE. This prints  the dual and primal residuals
+//' @param invmat A list of length ncol(y), each containing the C_d part of equation 32 in the paper
+//' @param gg penalty terms for the tree structure for lambda_1 and  lambda_2 for the admm call.
+//' @return  predicted values for the ADMM part
+//' @description TODO: add description
+//' @export
 // [[Rcpp::export]]
 Rcpp::List admm_MADMMplasso_cpp(
   const arma::vec beta0,