adding some cleaning

DESCRIPTION

@@ -31,6 +31,6 @@ Suggests:
     testthat (>= 3.0.0)
 License: BSD_3_clause + file LICENSE
 NeedsCompilation: yes
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 ByteCompile: yes
 VignetteBuilder: knitr

NAMESPACE

@@ -1,7 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(Eloglik,susiF)
-S3method(Eloglik,susiF_ss)
 S3method(HMM_regression,susiF)
 S3method(L_mixsq,mixture_normal)
 S3method(L_mixsq,mixture_normal_per_scale)

R/ELBO.R

@@ -146,9 +146,9 @@ loglik_SFR_post.susiF <- function (obj, l, Y, X, ...)
 #
 #' @param Y Matrix of outcomes
 #
-#' @param X Matrix of covariates
+#' @param X Matrix of covariates 
 #' 
-#' @param data list of data for susif_ss
+#' @param \dots Other arguments.
 #
 #' @return Expected log likelihood
 #' @export
@@ -170,14 +170,14 @@ Eloglik.susiF = function (obj,Y ,X,  ...) {
 
   return(-(n*t/2) * log(2*pi*obj$sigma2) - (1/(2*obj$sigma2)) * get_ER2( obj, Y, X))
 }
-#' @export
-#' @keywords internal
-Eloglik.susiF_ss = function (obj,data,...) {
-  n <- data$N
-  t <- ncol(data$Bhat)
-
-  return(-(n*t/2) * log(2*pi*obj$sigma2) - (1/(2*obj$sigma2)) * get_ER2( obj,data))
-}
+#  @export
+#  @keywords internal
+#Eloglik.susiF_ss = function (obj,data,...) {
+#  n <- data$N
+#  t <- ncol(data$Bhat)
+#
+#  return(-(n*t/2) * log(2*pi*obj$sigma2) - (1/(2*obj$sigma2)) * get_ER2( obj,data))
+#}
 
 #' @title Get objective function from data and susiF object
 #

R/operation_on_susiF_obj.R

@@ -1883,7 +1883,6 @@ update_lBF.susiF <- function    (obj,l, lBF,...)
 #
 #' @param Shat matrix pxJ standard error, Shat[j,t] corresponds to standard error of the regression coefficient of Y[,t] on X[,j]
 #
-#'@param alpha  vector of length p, containing the updated log Bayes factors
 #'@param indx_lst list generated by gen_wavelet_indx for the given level of resolution
 #'@return susiF object
 #'@export
@@ -2070,7 +2069,7 @@ update_residual_variance.susiF <- function( obj,sigma2,...)
 #' @param obj a susif object defined by init_susiF_obj function
 #' @param min_purity minimal purity within a CS
 #' @param X matrix of covariates
-#' @param max_crit remove cs base on max absolute correlation instead of min absolute correlation, usefull in the
+#' @param median_crit remove cs base on max absolute correlation instead of min absolute correlation, usefull in the
 #
 #' @return a list of index corresponding the the dummy effect
 #

R/plot_functions.R

@@ -24,6 +24,12 @@ plot_colors <- c("black", "dodgerblue2", "green4", "#6A3D9A", "#FF7F00",
 #' 
 #' @param pip_only logical, if TRUE only ouput the PIP plot
 #' 
+#' @param show_outing_grid logical, if TRUE show grid
+#' 
+#' @param pip_only logical, if TRUE show pip only
+#' 
+#' @param show_affected_region logical, if TRUE show affected regions
+#' 
 #' @param font_size Passed as the \dQuote{ont_size} argument to
 #'   \code{\link[cowplot]{theme_cowplot}}.
 #' 

R/susiF.R

@@ -219,7 +219,7 @@
 #'
 #'out <- susiF(Y,X,L=2 , prior = 'mixture_normal_per_scale')
 #'
-#'plot_susiF(out, cred.band = TRUE)
+#'plot_susiF(out)
 #'
 #'
 #'
@@ -326,6 +326,15 @@ susiF <- function(Y, X, L = 2,
   if(!cal_obj){
     tol <-10^-3
   }
+  if(L>ncol(X)){
+    L <-ncol(X)
+  }
+
+  if(L_start>ncol(X)){
+    L_start <-ncol(X)
+  }
+
+
   if(L_start >L)
   {
     L_start <- L

R/susiF_workhorse.R

@@ -67,7 +67,7 @@ susiF.workhorse <- function(obj,
                             cor_small=FALSE,
                             is.pois=FALSE,
                             e = 0.001){
-
+#browser()
   G_prior  <- get_G_prior(obj )
   Y_f      <-  cbind( W$D,W$C)
   update_Y <- Y_f