From 769bc62a9f692691e6efdcc5685d36dd4dbf2b03 Mon Sep 17 00:00:00 2001
From: sanchezi <isabelle.sanchez@inrae.fr>
Date: Wed, 14 Sep 2022 16:39:40 +0200
Subject: [PATCH] insertion of comments by BC
---
NAMESPACE | 6 +-
R/kfino.R | 131 ++++++++++++++------------
R/utils_functions.R | 34 ++++---
doc/HowTo.html | 8 +-
doc/multipleFit.html | 12 +--
man/kfino_fit.Rd | 18 ++--
man/{KBO_EM.Rd => utils_EM.Rd} | 19 ++--
man/{KBO_known.Rd => utils_fit.Rd} | 14 +--
man/{KBO_L.Rd => utils_likelihood.Rd} | 16 ++--
tests/testthat/test-outputAlgo.R | 4 +-
10 files changed, 140 insertions(+), 122 deletions(-)
rename man/{KBO_EM.Rd => utils_EM.Rd} (69%)
rename man/{KBO_known.Rd => utils_fit.Rd} (78%)
rename man/{KBO_L.Rd => utils_likelihood.Rd} (68%)
diff --git a/NAMESPACE b/NAMESPACE
index 5732095..8868c61 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,11 +1,11 @@
# Generated by roxygen2: do not edit by hand
-export(KBO_EM)
-export(KBO_L)
-export(KBO_known)
export(doutlier)
export(kfino_fit)
export(kfino_plot)
+export(utils_EM)
+export(utils_fit)
+export(utils_likelihood)
importFrom(dplyr,"%>%")
importFrom(dplyr,.data)
importFrom(dplyr,arrange)
diff --git a/R/kfino.R b/R/kfino.R
index 2ee4611..0a20f77 100644
--- a/R/kfino.R
+++ b/R/kfino.R
@@ -7,15 +7,15 @@
#' @param doOptim logical, if TRUE optimization of the initial parameters,
#' default TRUE
#' @param method character, the method used to optimize the initial parameters:
-#' Expectation-Maximization algorithm `"EM"` or Maximization
-#' Likelihood `"ML"`, default `"ML"`
+#' Expectation-Maximization algorithm `"EM"` (faster) or Maximization
+#' Likelihood `"ML"` (more robust), default `"ML"`
#' @param threshold numeric, threshold to qualify an observation as outlier
#' according to the label_pred, default 0.5
-#' @param kappa numeric, truncation setting for likelihood optimization,
-#' default 10
-#' @param kappaOpt numeric, truncation setting for initial parameters'
-#' optimization, default 7
-#' @param verbose write stuff if TRUE (optional), default FALSE.
+#' @param kappa numeric, truncation setting for likelihood optimization over
+#' initial parameters, default 10
+#' @param kappaOpt numeric, truncation setting for the filtering and outlier
+#' detection step with optimized parameters, default 7
+#' @param verbose write details if TRUE (optional), default FALSE.
#'
#' @details The initialization parameter list `param` contains:
#' \describe{
@@ -65,7 +65,9 @@
#' \item{lwr}{lower bound of the confidence interval of the predicted value}
#' \item{upr}{upper bound of the confidence interval of the predicted value}
#' \item{flag}{flag of the value (OK value, KO value (outlier), OOR value
-#' (out of range values defined by the user in `kfino_fit`)}
+#' (out of range values defined by the user in `kfino_fit` with
+#' `expertMin`, `expertMax` input parameters). If
+#' flag == OOR the 4 previous columns are set to NA.}
#' }
#' @return PredictionOK: A subset of `detectOutlier` data set with the predictions
#' of the analyzed variable on possible values (OK and KO values)
@@ -240,17 +242,18 @@ kfino_fit<-function(datain,Tvar,Yvar,
Y=YY[Subechant]
Tps=TpsTps[Subechant]
N=50
- Vopt=KBO_L(list(m0=m0opt,
- mm=mmopt,
- pp=popt,
- aa=aa,
- expertMin=expertMin,
- expertMax=expertMax,
- sigma2_mm=sigma2_mm,
- sigma2_m0=sigma2_m0,
- sigma2_pp=sigma2_pp,
- K=K),
- Y=Y,Tps=Tps,N=N,scalingC=scalingC,kappaOpt=kappaOpt)
+ Vopt=utils_likelihood(list(m0=m0opt,
+ mm=mmopt,
+ pp=popt,
+ aa=aa,
+ expertMin=expertMin,
+ expertMax=expertMax,
+ sigma2_mm=sigma2_mm,
+ sigma2_m0=sigma2_m0,
+ sigma2_pp=sigma2_pp,
+ K=K),
+ Y=Y,Tps=Tps,N=N,
+ scalingC=scalingC,kappaOpt=kappaOpt)
for (m0 in seq(bornem0[1],bornem0[2],2) ){
for (mm in seq((m0-5),(m0+20),2) ){
@@ -261,17 +264,18 @@ kfino_fit<-function(datain,Tvar,Yvar,
Y=YY[Subechant]
Tps=TpsTps[Subechant]
- V=KBO_L(list(m0=m0,
- mm=mm,
- pp=p,
- aa=aa,
- expertMin=expertMin,
- expertMax=expertMax,
- sigma2_mm=sigma2_mm,
- sigma2_m0=sigma2_m0,
- sigma2_pp=sigma2_pp,
- K=K),
- Y=Y,Tps=Tps,N=N,scalingC=scalingC,kappaOpt=kappaOpt)
+ V=utils_likelihood(list(m0=m0,
+ mm=mm,
+ pp=p,
+ aa=aa,
+ expertMin=expertMin,
+ expertMax=expertMax,
+ sigma2_mm=sigma2_mm,
+ sigma2_m0=sigma2_m0,
+ sigma2_pp=sigma2_pp,
+ K=K),
+ Y=Y,Tps=Tps,N=N,
+ scalingC=scalingC,kappaOpt=kappaOpt)
if (V > Vopt){
Vopt=V
m0opt=m0
@@ -296,7 +300,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
print("-------:")
}
- resultat=KBO_known(param=list(mm=mmopt,
+ resultat=utils_fit(param=list(mm=mmopt,
pp=popt,
m0=m0opt,
aa=aa,
@@ -328,7 +332,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
diff_m0=diff_mm=diff_p=20
k=1
while (diff_m0 > 0.5 && diff_p > 0.0001 && diff_mm > 2){
- Res_EM=KBO_EM(param=list(mm=m_tmp,
+ Res_EM=utils_EM(param=list(mm=m_tmp,
pp=p_tmp,
m0=m0_tmp,
aa=aa,
@@ -362,7 +366,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
diff_m0=diff_mm=diff_p=20
k=1
while (diff_m0 > 0.5 && diff_p > 0.0001 && diff_mm > 2){
- Res_EM=KBO_EM(param=list(mm=m_tmp,
+ Res_EM=utils_EM(param=list(mm=m_tmp,
pp=p_tmp,
m0=m0_tmp,
aa=aa,
@@ -401,7 +405,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
k=1
while (diff_m0 > 0.5 && diff_p > 0.0001 && diff_mm > 2){
print(k)
- Res_EM=KBO_EM(param=list(mm=m_tmp,
+ Res_EM=utils_EM(param=list(mm=m_tmp,
pp=p_tmp,
m0=m0_tmp,
aa=aa,
@@ -411,7 +415,8 @@ kfino_fit<-function(datain,Tvar,Yvar,
sigma2_mm=sigma2_mm,
sigma2_pp=sigma2_pp,
K=K),
- kappaOpt=kappaOpt, Y=Y,Tps=Tps,N=N,scalingC=scalingC)
+ kappaOpt=kappaOpt, Y=Y,Tps=Tps,N=N,
+ scalingC=scalingC)
diff_m0=abs(m0_tmp - Res_EM$m0[[1]])
diff_p=abs(p_tmp - Res_EM$pp)
diff_mm=abs(m_tmp - Res_EM$mm[[1]])
@@ -447,7 +452,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
mmopt=quantile(Y[(3*N/4):N], probs = c(.5))
popt=0.5
- Vopt=KBO_EM(param=list(m0=m0opt,
+ Vopt=utils_EM(param=list(m0=m0opt,
mm=mmopt,
pp=popt,
aa=aa,
@@ -473,7 +478,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
cat("Optimized pp: ",popt,"\n")
print("-------:")
}
- resultat=KBO_known(param=list(mm=mmopt,
+ resultat=utils_fit(param=list(mm=mmopt,
pp=popt,
m0=m0opt,
aa=aa,
@@ -502,31 +507,33 @@ kfino_fit<-function(datain,Tvar,Yvar,
}
popt=0.5
- Vopt=KBO_L(list(m0=m0opt,
- mm=mmopt,
- pp=popt,
- aa=aa,
- expertMin=expertMin,
- expertMax=expertMax,
- sigma2_mm=sigma2_mm,
- sigma2_m0=sigma2_m0,
- sigma2_pp=sigma2_pp,
- K=K),
- Y=Y,Tps=Tps,N=N,scalingC=scalingC,kappaOpt=kappaOpt)
+ Vopt=utils_likelihood(list(m0=m0opt,
+ mm=mmopt,
+ pp=popt,
+ aa=aa,
+ expertMin=expertMin,
+ expertMax=expertMax,
+ sigma2_mm=sigma2_mm,
+ sigma2_m0=sigma2_m0,
+ sigma2_pp=sigma2_pp,
+ K=K),
+ Y=Y,Tps=Tps,N=N,
+ scalingC=scalingC,kappaOpt=kappaOpt)
for (m0 in seq(bornem0[1],bornem0[2],2) ){
for (mm in seq((m0-5),(m0+20),2) ){
for (p in seqp){
- V=KBO_L(list(m0=m0,
- mm=mm,
- pp=p,
- aa=aa,
- expertMin=expertMin,
- expertMax=expertMax,
- sigma2_mm=sigma2_mm,
- sigma2_m0=sigma2_m0,
- sigma2_pp=sigma2_pp,
- K=K),
- Y=Y,Tps=Tps,N=N,scalingC=scalingC,kappaOpt=kappaOpt)
+ V=utils_likelihood(list(m0=m0,
+ mm=mm,
+ pp=p,
+ aa=aa,
+ expertMin=expertMin,
+ expertMax=expertMax,
+ sigma2_mm=sigma2_mm,
+ sigma2_m0=sigma2_m0,
+ sigma2_pp=sigma2_pp,
+ K=K),
+ Y=Y,Tps=Tps,N=N,
+ scalingC=scalingC,kappaOpt=kappaOpt)
if (V > Vopt){
Vopt=V
@@ -545,7 +552,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
cat("Optimized pp: ",popt,"\n")
print("-------:")
}
- resultat=KBO_known(param=list(mm=mmopt,
+ resultat=utils_fit(param=list(mm=mmopt,
pp=popt,
m0=m0opt,
aa=aa,
@@ -576,7 +583,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
print(X)
print("-------:")
}
- resultat=KBO_known(param=list(m0=X[[1]],
+ resultat=utils_fit(param=list(m0=X[[1]],
pp=X[[2]],
mm=X[[3]],
aa=aa,
@@ -608,7 +615,7 @@ kfino_fit<-function(datain,Tvar,Yvar,
print("Used parameters: ")
print(X)
}
- resultat=KBO_known(param=list(m0=X[[1]],
+ resultat=utils_fit(param=list(m0=X[[1]],
pp=X[[2]],
mm=X[[3]],
aa=aa,
diff --git a/R/utils_functions.R b/R/utils_functions.R
index 4869440..465eb6f 100644
--- a/R/utils_functions.R
+++ b/R/utils_functions.R
@@ -1,9 +1,9 @@
#-------------------------------------------------------------------
# utils_functions.R: some useful functions for kfino method
# doutlier()
-# KBO_known()
-# KBO_L()
-# KBO_EM()
+# utils_fit()
+# utils_likelihood()
+# utils_EM()
#-------------------------------------------------------------------
#' doutlier defines an outlier distribution (Surface of a
@@ -38,7 +38,8 @@ doutlier<-function(y,
#--------------------------------------------------------------------------
-#' KBO_known a function to calculate a likelihood on given parameters
+#' utils_fit a fonction running the kfino algorithm to filter data and
+#' detect outliers under the knowledge of all parameters
#'
#' @param param list, see initial parameter list in \code{kfino_fit}
#' @param threshold numeric, threshold for confidence interval, default 0.5
@@ -80,8 +81,8 @@ doutlier<-function(y,
#' K=2,
#' seqp=seq(0.5,0.7,0.1))
#' print(Y)
-#' KBO_known(param=param2,threshold=0.5,kappa=10,Y=Y,Tps=Tps,N=N)
-KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
+#' utils_fit(param=param2,threshold=0.5,kappa=10,Y=Y,Tps=Tps,N=N)
+utils_fit<-function(param,threshold,kappa=10,Y,Tps,N){
# load objects
mm=param[["mm"]]
pp=param[["pp"]]
@@ -234,7 +235,7 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
}
#------------------------------------------------------------------------
-#' KBO_L a function to calculate a likelihood on initial parameters
+#' utils_likelihood a function to calculate a likelihood on initial parameters
#' optimized by a grid search
#'
#' @param param list, see initial parameter list in \code{kfino_fit}
@@ -246,10 +247,12 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
#' numeric vector.
#' Tvar can be expressed as a proportion of day in seconds
#' @param N numeric, length of the numeric vector of Y values
-#' @param scalingC numeric, scaling constant
+#' @param scalingC numeric, scaling constant. To be changed if the function is
+#' not able to calculate the likelihood because the number of data is large
#'
#' @details uses the same input parameter list than the main function
#' @return a likelihood
+#' @keywords internal
#' @export
#'
#' @examples
@@ -269,8 +272,8 @@ KBO_known<-function(param,threshold,kappa=10,Y,Tps,N){
#' K=2,
#' seqp=seq(0.5,0.7,0.1))
#' print(Y)
-#' KBO_L(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
-KBO_L<-function(param,kappaOpt=7,Y,Tps,N,scalingC){
+#' utils_likelihood(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
+utils_likelihood<-function(param,kappaOpt=7,Y,Tps,N,scalingC){
# load objects
mm=param[["mm"]]
pp=param[["pp"]]
@@ -383,8 +386,8 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,scalingC){
}
#------------------------------------------------------------------------
-#' KBO_EM a function to calculate a likelihood on initial parameters
-#' optimized by an Expectation-Maximization (EM) algorithm
+#' utils_EM a function to estimate the parameters `m_0` , `mm`, `pp` through
+#' an Expectation-Maximization (EM) method
#'
#' @param param list, see initial parameter list in \code{kfino_fit}
#' @param kappaOpt numeric, truncation setting for initial parameters'
@@ -395,7 +398,8 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,scalingC){
#' numeric vector.
#' Tvar can be expressed as a proportion of day in seconds
#' @param N numeric, length of the numeric vector of Y values
-#' @param scalingC numeric, scaling constant
+#' @param scalingC numeric, scaling constant. To be changed if the function is
+#' not able to calculate the likelihood because the number of data is large
#'
#' @details uses the same input parameter list than the main function
#' @return a list:
@@ -424,8 +428,8 @@ KBO_L<-function(param,kappaOpt=7,Y,Tps,N,scalingC){
#' K=2,
#' seqp=seq(0.5,0.7,0.1))
#' print(Y)
-#' KBO_EM(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
-KBO_EM<-function(param,kappaOpt,Y,Tps,N,scalingC){
+#' utils_EM(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
+utils_EM<-function(param,kappaOpt,Y,Tps,N,scalingC){
# load objects
mm<-param[["mm"]]
pp<-param[["pp"]]
diff --git a/doc/HowTo.html b/doc/HowTo.html
index 71ede75..d1b5598 100644
--- a/doc/HowTo.html
+++ b/doc/HowTo.html
@@ -1512,7 +1512,7 @@ div.tocify {
<h1 class="title toc-ignore">How to perform a kfino outlier
detection</h1>
<h4 class="author">B. Cloez & I. Sanchez</h4>
-<h4 class="date">septembre 13, 2022</h4>
+<h4 class="date">septembre 14, 2022</h4>
</div>
@@ -1920,9 +1920,9 @@ informations</h1>
#> loaded via a namespace (and not attached):
#> [1] highr_0.9 pillar_1.8.1 bslib_0.4.0 compiler_4.2.1
#> [5] jquerylib_0.1.4 tools_4.2.1 digest_0.6.29 jsonlite_1.8.0
-#> [9] evaluate_0.16 lifecycle_1.0.1 tibble_3.1.8 gtable_0.3.1
-#> [13] pkgconfig_2.0.3 rlang_1.0.5 cli_3.3.0 DBI_1.1.3
-#> [17] rstudioapi_0.14 yaml_2.3.5 xfun_0.32 fastmap_1.1.0
+#> [9] evaluate_0.16 lifecycle_1.0.2 tibble_3.1.8 gtable_0.3.1
+#> [13] pkgconfig_2.0.3 rlang_1.0.5 cli_3.4.0 DBI_1.1.3
+#> [17] rstudioapi_0.14 yaml_2.3.5 xfun_0.33 fastmap_1.1.0
#> [21] withr_2.5.0 stringr_1.4.1 knitr_1.40 generics_0.1.3
#> [25] vctrs_0.4.1 sass_0.4.2 grid_4.2.1 tidyselect_1.1.2
#> [29] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.16
diff --git a/doc/multipleFit.html b/doc/multipleFit.html
index da848d0..62d44b0 100644
--- a/doc/multipleFit.html
+++ b/doc/multipleFit.html
@@ -1512,7 +1512,7 @@ div.tocify {
<h1 class="title toc-ignore">How to perform a kfino outlier detection on
multiple individuals</h1>
<h4 class="author">B. Cloez & I. Sanchez</h4>
-<h4 class="date">septembre 13, 2022</h4>
+<h4 class="date">septembre 14, 2022</h4>
</div>
@@ -1571,7 +1571,7 @@ for (i in seq_along(myIDE)){
#> [1] "250017033503096"
#> [1] 566 5
Sys.time() - t0
-#> Time difference of 18.85249 secs
+#> Time difference of 18.46779 secs
print(length(resu1))
#> [1] 4</code></pre>
@@ -1620,7 +1620,7 @@ resu2<-foreach(i=seq_along(myIDE), .packages="kfino") %dopar%
parallel::stopCluster(myCluster)
Sys.time() - t0
-#> Time difference of 9.442032 secs
+#> Time difference of 9.275384 secs
print(length(resu2))
#> [1] 4</code></pre>
@@ -1667,9 +1667,9 @@ Adaptor for the ‘parallel’ Package</em>. R package version 1.0.17, <a href="
#> loaded via a namespace (and not attached):
#> [1] highr_0.9 pillar_1.8.1 bslib_0.4.0 compiler_4.2.1
#> [5] jquerylib_0.1.4 tools_4.2.1 digest_0.6.29 jsonlite_1.8.0
-#> [9] evaluate_0.16 lifecycle_1.0.1 tibble_3.1.8 gtable_0.3.1
-#> [13] pkgconfig_2.0.3 rlang_1.0.5 cli_3.3.0 DBI_1.1.3
-#> [17] rstudioapi_0.14 yaml_2.3.5 xfun_0.32 fastmap_1.1.0
+#> [9] evaluate_0.16 lifecycle_1.0.2 tibble_3.1.8 gtable_0.3.1
+#> [13] pkgconfig_2.0.3 rlang_1.0.5 cli_3.4.0 DBI_1.1.3
+#> [17] rstudioapi_0.14 yaml_2.3.5 xfun_0.33 fastmap_1.1.0
#> [21] withr_2.5.0 stringr_1.4.1 knitr_1.40 generics_0.1.3
#> [25] vctrs_0.4.1 sass_0.4.2 grid_4.2.1 tidyselect_1.1.2
#> [29] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.16
diff --git a/man/kfino_fit.Rd b/man/kfino_fit.Rd
index 7d326d8..b0bc99d 100644
--- a/man/kfino_fit.Rd
+++ b/man/kfino_fit.Rd
@@ -31,19 +31,19 @@ Tvar should be expressed as a proportion of day in seconds}
default TRUE}
\item{method}{character, the method used to optimize the initial parameters:
-Expectation-Maximization algorithm `"EM"` or Maximization
-Likelihood `"ML"`, default `"ML"`}
+Expectation-Maximization algorithm `"EM"` (faster) or Maximization
+Likelihood `"ML"` (more robust), default `"ML"`}
\item{threshold}{numeric, threshold to qualify an observation as outlier
according to the label_pred, default 0.5}
-\item{kappa}{numeric, truncation setting for likelihood optimization,
-default 10}
+\item{kappa}{numeric, truncation setting for likelihood optimization over
+initial parameters, default 10}
-\item{kappaOpt}{numeric, truncation setting for initial parameters'
-optimization, default 7}
+\item{kappaOpt}{numeric, truncation setting for the filtering and outlier
+detection step with optimized parameters, default 7}
-\item{verbose}{write stuff if TRUE (optional), default FALSE.}
+\item{verbose}{write details if TRUE (optional), default FALSE.}
}
\value{
a S3 list with two data frames and a list of vectors of
@@ -59,7 +59,9 @@ detectOutlier: The whole input data set with the detected outliers
\item{lwr}{lower bound of the confidence interval of the predicted value}
\item{upr}{upper bound of the confidence interval of the predicted value}
\item{flag}{flag of the value (OK value, KO value (outlier), OOR value
- (out of range values defined by the user in `kfino_fit`)}
+ (out of range values defined by the user in `kfino_fit` with
+ `expertMin`, `expertMax` input parameters). If
+ flag == OOR the 4 previous columns are set to NA.}
}
PredictionOK: A subset of `detectOutlier` data set with the predictions
diff --git a/man/KBO_EM.Rd b/man/utils_EM.Rd
similarity index 69%
rename from man/KBO_EM.Rd
rename to man/utils_EM.Rd
index 591ebf3..7c19d22 100644
--- a/man/KBO_EM.Rd
+++ b/man/utils_EM.Rd
@@ -1,11 +1,11 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_functions.R
-\name{KBO_EM}
-\alias{KBO_EM}
-\title{KBO_EM a function to calculate a likelihood on initial parameters
-optimized by an Expectation-Maximization (EM) algorithm}
+\name{utils_EM}
+\alias{utils_EM}
+\title{utils_EM a function to estimate the parameters `m_0` , `mm`, `pp` through
+an Expectation-Maximization (EM) method}
\usage{
-KBO_EM(param, kappaOpt, Y, Tps, N, scalingC)
+utils_EM(param, kappaOpt, Y, Tps, N, scalingC)
}
\arguments{
\item{param}{list, see initial parameter list in \code{kfino_fit}}
@@ -22,7 +22,8 @@ Tvar can be expressed as a proportion of day in seconds}
\item{N}{numeric, length of the numeric vector of Y values}
-\item{scalingC}{numeric, scaling constant}
+\item{scalingC}{numeric, scaling constant. To be changed if the function is
+not able to calculate the likelihood because the number of data is large}
}
\value{
a list:
@@ -34,8 +35,8 @@ a list:
}
}
\description{
-KBO_EM a function to calculate a likelihood on initial parameters
-optimized by an Expectation-Maximization (EM) algorithm
+utils_EM a function to estimate the parameters `m_0` , `mm`, `pp` through
+an Expectation-Maximization (EM) method
}
\details{
uses the same input parameter list than the main function
@@ -57,5 +58,5 @@ param2<-list(m0=41,
K=2,
seqp=seq(0.5,0.7,0.1))
print(Y)
-KBO_EM(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
+utils_EM(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
}
diff --git a/man/KBO_known.Rd b/man/utils_fit.Rd
similarity index 78%
rename from man/KBO_known.Rd
rename to man/utils_fit.Rd
index fe48f4c..4372b67 100644
--- a/man/KBO_known.Rd
+++ b/man/utils_fit.Rd
@@ -1,10 +1,11 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_functions.R
-\name{KBO_known}
-\alias{KBO_known}
-\title{KBO_known a function to calculate a likelihood on given parameters}
+\name{utils_fit}
+\alias{utils_fit}
+\title{utils_fit a fonction running the kfino algorithm to filter data and
+ detect outliers under the knowledge of all parameters}
\usage{
-KBO_known(param, threshold, kappa = 10, Y, Tps, N)
+utils_fit(param, threshold, kappa = 10, Y, Tps, N)
}
\arguments{
\item{param}{list, see initial parameter list in \code{kfino_fit}}
@@ -35,7 +36,8 @@ a list
}
}
\description{
-KBO_known a function to calculate a likelihood on given parameters
+utils_fit a fonction running the kfino algorithm to filter data and
+ detect outliers under the knowledge of all parameters
}
\details{
uses the same input parameter list than the main function
@@ -57,5 +59,5 @@ param2<-list(m0=41,
K=2,
seqp=seq(0.5,0.7,0.1))
print(Y)
-KBO_known(param=param2,threshold=0.5,kappa=10,Y=Y,Tps=Tps,N=N)
+utils_fit(param=param2,threshold=0.5,kappa=10,Y=Y,Tps=Tps,N=N)
}
diff --git a/man/KBO_L.Rd b/man/utils_likelihood.Rd
similarity index 68%
rename from man/KBO_L.Rd
rename to man/utils_likelihood.Rd
index f7c4b53..05bf42e 100644
--- a/man/KBO_L.Rd
+++ b/man/utils_likelihood.Rd
@@ -1,11 +1,11 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils_functions.R
-\name{KBO_L}
-\alias{KBO_L}
-\title{KBO_L a function to calculate a likelihood on initial parameters
+\name{utils_likelihood}
+\alias{utils_likelihood}
+\title{utils_likelihood a function to calculate a likelihood on initial parameters
optimized by a grid search}
\usage{
-KBO_L(param, kappaOpt = 7, Y, Tps, N, scalingC)
+utils_likelihood(param, kappaOpt = 7, Y, Tps, N, scalingC)
}
\arguments{
\item{param}{list, see initial parameter list in \code{kfino_fit}}
@@ -22,13 +22,14 @@ Tvar can be expressed as a proportion of day in seconds}
\item{N}{numeric, length of the numeric vector of Y values}
-\item{scalingC}{numeric, scaling constant}
+\item{scalingC}{numeric, scaling constant. To be changed if the function is
+not able to calculate the likelihood because the number of data is large}
}
\value{
a likelihood
}
\description{
-KBO_L a function to calculate a likelihood on initial parameters
+utils_likelihood a function to calculate a likelihood on initial parameters
optimized by a grid search
}
\details{
@@ -51,5 +52,6 @@ param2<-list(m0=41,
K=2,
seqp=seq(0.5,0.7,0.1))
print(Y)
-KBO_L(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
+utils_likelihood(param=param2,kappaOpt=7,Y=Y,Tps=Tps,N=N,scalingC=6)
}
+\keyword{internal}
diff --git a/tests/testthat/test-outputAlgo.R b/tests/testthat/test-outputAlgo.R
index d3a1992..9aeab27 100644
--- a/tests/testthat/test-outputAlgo.R
+++ b/tests/testthat/test-outputAlgo.R
@@ -57,7 +57,7 @@ test_that("kfino - optimisation - computed correctly", {
expect_equal(as.vector(resu1[[3]]$likelihood),1.035855e-135)
})
-test_that("KBO_L - likelihood - computed correctly", {
+test_that("utils_likelihood - likelihood - computed correctly", {
set.seed(1234)
Y<-rnorm(n=10,mean=50,4)
Tps<-seq(1,10)
@@ -73,7 +73,7 @@ test_that("KBO_L - likelihood - computed correctly", {
sigma2_pp=5,
K=2,
seqp=seq(0.5,0.7,0.1))
- resu<-KBO_L(param=param2,Y=Y,Tps=Tps,N=N,scalingC=6,kappaOpt=7)
+ resu<-utils_likelihood(param=param2,Y=Y,Tps=Tps,N=N,scalingC=6,kappaOpt=7)
expect_equal(as.vector(resu),0.00490257592)
--
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