diff --git a/R/utils_functions.R b/R/utils_functions.R
index 8d7d3177deef026074aeb691efcd562d36712d14..0adf66ef4d48aaa19d57b0417ae72969d9b468ed 100644
--- a/R/utils_functions.R
+++ b/R/utils_functions.R
@@ -1,6 +1,5 @@
#-------------------------------------------------------------------
-# utils_functions.R
-# some useful functions for kfino method
+# utils_functions.R: some useful functions for kfino method
# loi_outlier()
# KBO_known()
# KBO_L()
@@ -227,8 +226,9 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
}
#------------------------------------------------------------------------
-#' KBO_L a function to calculate a likelihood on optimized parameters
-#'
+#' KBO_L a function to calculate a likelihood on initial parameters
+#' optimized by a grid search
+#'
#' @param param a list of 10 input parameters mm, pp and m0
#' @param kappaOpt numeric, truncation setting, default 7
#' @param Y character, name of the numeric variable to predict in the
@@ -298,7 +298,6 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
#iteration (1.1.2)
#-----------------------
- #// Pour l'instant, je fais comme si kappa<N-1 mettre un if sinon
# before truncation
for (k in 1:(kappa-1)){
mnew=rep(0,2^(k+1))
@@ -308,7 +307,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
# CR: renormalization constant that intervenes in the denominator of the pu
qnew=rep(0 ,2^(k+1))
diffTps<-Tps[k+1] - Tps[k]
- #--- numérateur de pu0
+ #--- numerator of pu0
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
@@ -328,14 +327,14 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
sigma2=sigma2new
p=pnew/sum(pnew)
- L=dix*Lnew # fois 2 pr le grandir
+ L=dix*Lnew
q=dix*qnew
}
# after truncation
#----------------------
for (k in kappa:(N-1)){
- # je cree le vecteur des nouveaux m,
+ # creation of the 'new m' vector
mnew=rep(0,2^(kappa+1))
sigma2new=rep(0 ,2^(kappa+1))
pnew=rep(0 ,2^(kappa+1))
@@ -343,7 +342,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
# CR: renormalization constant that intervenes in the denominator of the pu
qnew=rep(0 ,2^(kappa+1))
diffTps<-Tps[k+1] - Tps[k]
- #--- numérateur de pu0
+ #--- numerator of pu0
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
@@ -365,7 +364,7 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
m=mnew[selection]
sigma2=sigma2new[selection]
p=pnew[selection]/sum(pnew[selection])
- L=dix*Lnew[selection] #fois 2 pr le grandir
+ L=dix*Lnew[selection]
q=dix*qnew[selection]
}
@@ -375,8 +374,8 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,dix){
}
#------------------------------------------------------------------------
-#' KBO_EM a function to calculate a likelihood on optimized parameters by
-#' an Expectation-Maximization (EM) algorithm
+#' KBO_EM a function to calculate a likelihood on initial parameters
+#' optimized by an Expectation-Maximization (EM) algorithm
#'
#' @param param a list of 10 input parameters mm, pp and m0
#' @param kappaOpt numeric, truncation setting, default 7
@@ -451,9 +450,6 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
L=dix*c(l0,loinorm1) #Attention *2 pr le grandir
q=c(1,1) #attention
- #Pmean=c(p1)#A VIRER
-
- #nouveaux paramètres
a=c(1,sigma2_pp/(sigma2_m0+sigma2_pp))
b=c(0,0)
c=c(0, Y[1]*sigma2_m0/(sigma2_m0+sigma2_pp))
@@ -468,7 +464,6 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
#-----------------------
# kappa < N-1 for now. add an if condition if necessary
# before truncation
-
for (k in 1:(kappa-1)){
mnew=rep(0,2^(k+1))
sigma2new=rep(0 ,2^(k+1))
@@ -507,9 +502,6 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
bnew[(1+2^k):2^(k+1)]= b[1:(2^k)]*sigma2_pp/sommevar
cnew[(1+2^k):2^(k+1)]= c[1:(2^k)]*sigma2_pp/sommevar + Y[k+1]*sigma2new[1:(2^k)]/sommevar
- #Proba1<-sum(pnew[(1+2^k):2^(k+1)])#A VIRER
- #Pmean=c(Pmean,Proba1/sum(pnew))#A VIRER
-
m=mnew
sigma2=sigma2new
p=pnew/sum(pnew)
@@ -578,9 +570,6 @@ KBO_EM<-function(param,kappaOpt,Y,Tps,N,dix){
selection=order(pnew, decreasing=T)[1:2^kappa]
- #Proba1<-sum(pnew[(1+2^kappa):2^(kappa+1)]) #A VIRER
- #Pmean=c(Pmean,Proba1/sum(pnew))#A VIRER
-
m=mnew[selection]
sigma2=sigma2new[selection]
p=pnew[selection]/sum(pnew[selection])
diff --git a/man/KBO_EM.Rd b/man/KBO_EM.Rd
index 578b5da552eb0455fddcf0d575d9ddfffde12d2f..1b5835a667468928b63322c1c1189c66ec6f3fdc 100644
--- a/man/KBO_EM.Rd
+++ b/man/KBO_EM.Rd
@@ -2,8 +2,8 @@
% Please edit documentation in R/utils_functions.R
\name{KBO_EM}
\alias{KBO_EM}
-\title{KBO_EM a function to calculate a likelihood on optimized parameters by
-an Expectation-Maximization (EM) algorithm}
+\title{KBO_EM a function to calculate a likelihood on initial parameters
+optimized by an Expectation-Maximization (EM) algorithm}
\usage{
KBO_EM(param, kappaOpt, Y, Tps, N, dix)
}
@@ -33,8 +33,8 @@ a list:
}
}
\description{
-KBO_EM a function to calculate a likelihood on optimized parameters by
-an Expectation-Maximization (EM) algorithm
+KBO_EM a function to calculate a likelihood on initial parameters
+optimized by an Expectation-Maximization (EM) algorithm
}
\details{
uses the same input parameter list than the main function
diff --git a/man/KBO_L.Rd b/man/KBO_L.Rd
index ba5adeffc353c0ecd44a958ed84701ba31805a49..a951cb6f39db6d8628725d2b64b67b391794bb22 100644
--- a/man/KBO_L.Rd
+++ b/man/KBO_L.Rd
@@ -2,7 +2,8 @@
% Please edit documentation in R/utils_functions.R
\name{KBO_L}
\alias{KBO_L}
-\title{KBO_L a function to calculate a likelihood on optimized parameters}
+\title{KBO_L a function to calculate a likelihood on initial parameters
+optimized by a grid search}
\usage{
KBO_L(param, kappaOpt = 7, Y, Tps, N, dix)
}
@@ -26,7 +27,8 @@ Tvar can be expressed as a proportion of day in seconds}
a likelihood
}
\description{
-KBO_L a function to calculate a likelihood on optimized parameters
+KBO_L a function to calculate a likelihood on initial parameters
+optimized by a grid search
}
\details{
uses the same input parameter list than the main function