fonction loi_outlier externe

NAMESPACE

@@ -5,6 +5,7 @@ export(KBO_known)
 export(kfino_fit)
 export(kfino_fit2)
 export(kfino_plot)
+export(loi_outlier)
 importFrom(dplyr,"%>%")
 importFrom(dplyr,.data)
 importFrom(dplyr,arrange)

R/utils.R

+#' loi_outlier This function defines an outlier distribution (Surface of a
+#' trapezium) and uses input parameters given in the main function kfino_fit()
+#'
+#' @param y numeric, point
+#' @param K numeric, constant value
+#' @param expertMin numeric, the minimal weight expected by the user
+#' @param expertMax numeric, the maximal weight expected by the user
+#'
+#' @details this function is used to calculate an outlier distribution
+#'          following a trapezium shape
+#' @return a numeric value
+#' @export
+#'
+#' @examples
+#' loi_outlier(2,5,10,45)
+loi_outlier<-function(y,
+                      K,
+                      expertMin,
+                      expertMax){
+  2/((K+1)*(expertMax - expertMin)) +
+    (2*(K-1)/(K+1))*((y - expertMin)/((expertMax - expertMin)^2))
+}
+
+
+#--------------------------------------------------------------------------
 #' KBO_known
 #'
 #' @param param list, a list of 10 input parameters for mm, pp and m0
@@ -28,19 +53,12 @@ KBO_known<-function(param,threshold,Y,Tps,N){
   # paramètre de troncature
   kappa <-10
 
-  #--- function defining an outlier distribution (Surface of a trapezium)
-  # uses expertMin and expertMax
-  loi_outlier<-function(y){
-    2/((K+1)*(expertMax - expertMin)) +
-      (2*(K-1)/(K+1))*((y - expertMin)/((expertMax - expertMin)^2))
-  }
-
   # initialisation (1.1.1)
   #--------------------
   m1= (sigma2_pp*m0 + Y[1]*sigma2_m0)/(sigma2_m0+sigma2_pp)
   sigma1=(sigma2_m0*sigma2_pp)/(sigma2_m0+sigma2_pp)
 
-  l0<- loi_outlier(Y[1])
+  l0<- loi_outlier(Y[1],K,expertMin,expertMax)
   loinorm1<-dnorm(Y[1],m0, sqrt(sigma2_m0+sigma2_pp))
 
   p0= ((1-pp)*l0) / (pp*loinorm1 + (1-pp)*l0)
@@ -71,7 +89,7 @@ KBO_known<-function(param,threshold,Y,Tps,N){
     qnew=rep(0 ,2^(k+1))
     diffTps<-Tps[k+1] - Tps[k]
     #--- numérateur de pu0
-    tpbeta<-loi_outlier(Y[k+1])
+    tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
 
     pnew[1:(2^k)]=p[1:(2^k)]*(1-pp)*tpbeta
     Lnew[1:(2^k)]=L[1:(2^k)]*tpbeta
@@ -117,7 +135,7 @@ KBO_known<-function(param,threshold,Y,Tps,N){
     diffTps<-Tps[k+1] - Tps[k]
 
     #--- numérateur de pu0
-    tpbeta<-loi_outlier(Y[k+1])
+    tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
 
     pnew[1:(2^kappa)]=p[1:(2^kappa)]*(1-pp)*tpbeta
     Lnew[1:(2^kappa)]=L[1:(2^kappa)]*tpbeta
@@ -200,13 +218,6 @@ KBO_L<-function(param,Y,Tps,N,dix){
   sigma2_pp<-param[["sigma2_pp"]]
   K<-param[["K"]]
 
-  #--- function defining an outlier distribution (Surface of a trapezium)
-  # uses expertMin and expertMax
-  loi_outlier<-function(y){
-    2/((K+1)*(expertMax - expertMin)) +
-      (2*(K-1)/(K+1))*((y - expertMin)/((expertMax - expertMin)^2))
-  }
-
   #---- paramètre de troncature
   # Ici je met kappa =7, ca me fais retirer les proba <0.01 au lieu de 0.001
   # comme qd kappa=10 mais ca divise par 10 le temps de calcul
@@ -217,7 +228,7 @@ KBO_L<-function(param,Y,Tps,N,dix){
   m1= (sigma2_pp*m0 + Y[1]*sigma2_m0)/(sigma2_m0+sigma2_pp)
   sigma1=(sigma2_m0*sigma2_pp)/(sigma2_m0+sigma2_pp)
 
-  l0<- loi_outlier(Y[1])
+  l0<- loi_outlier(Y[1],K,expertMin,expertMax)
   loinorm1<-dnorm(Y[1],m0, sqrt(sigma2_m0+sigma2_pp))
 
   p0= ((1-pp)*l0) / (pp*loinorm1 + (1-pp)*l0)
@@ -242,7 +253,7 @@ KBO_L<-function(param,Y,Tps,N,dix){
     qnew=rep(0 ,2^(k+1))
     diffTps<-Tps[k+1] - Tps[k]
     #--- numérateur de pu0
-    tpbeta<-loi_outlier(Y[k+1])
+    tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
     pnew[1:(2^k)]=p[1:(2^k)]*(1-pp)*tpbeta
     Lnew[1:(2^k)]=L[1:(2^k)]*tpbeta
     mnew[1:(2^k)]= m[1:(2^k)]*exp(-aa*diffTps) + mm*(1-exp(-aa*diffTps)) #m_u0
@@ -277,7 +288,7 @@ KBO_L<-function(param,Y,Tps,N,dix){
     qnew=rep(0 ,2^(kappa+1))
     diffTps<-Tps[k+1] - Tps[k]
     #--- numérateur de pu0
-    tpbeta<-loi_outlier(Y[k+1])
+    tpbeta<-loi_outlier(Y[k+1],K,expertMin,expertMax)
     pnew[1:(2^kappa)]=p[1:(2^kappa)]*(1-pp)*tpbeta
     Lnew[1:(2^kappa)]=L[1:(2^kappa)]*tpbeta
     mnew[1:(2^kappa)]= m[1:(2^kappa)]*exp(-aa*diffTps) + mm*(1-exp(-aa*diffTps)) #m_u0

man/loi_outlier.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{loi_outlier}
+\alias{loi_outlier}
+\title{loi_outlier This function defines an outlier distribution (Surface of a
+trapezium) and uses input parameters given in the main function kfino_fit()}
+\usage{
+loi_outlier(y, K, expertMin, expertMax)
+}
+\arguments{
+\item{y}{numeric, point}
+
+\item{K}{numeric, constant value}
+
+\item{expertMin}{numeric, the minimal weight expected by the user}
+
+\item{expertMax}{numeric, the maximal weight expected by the user}
+}
+\value{
+a numeric value
+}
+\description{
+loi_outlier This function defines an outlier distribution (Surface of a
+trapezium) and uses input parameters given in the main function kfino_fit()
+}
+\details{
+this function is used to calculate an outlier distribution
+         following a trapezium shape
+}
+\examples{
+loi_outlier(2,5,10,45)
+}