diff --git a/doc/HowTo.R b/doc/HowTo.R
index e42d705d1a421ab0755ce3d47e42303bed942e41..8a17b8edcd311705c32f47eef583da21b3e8fa71 100644
--- a/doc/HowTo.R
+++ b/doc/HowTo.R
@@ -56,7 +56,7 @@ kfino_plot(resuin=resu2,typeG="quali",
kfino_plot(resuin=resu2,typeG="quanti",
Tvar="Day",Yvar="Poids",Ident="IDE")
-## ----error=TRUE---------------------------------------------------------------
+## -----------------------------------------------------------------------------
# --- With Optimisation on parameters
param1<-list(m0=NULL,
mm=NULL,
@@ -70,6 +70,9 @@ param1<-list(m0=NULL,
K=2,
seqp=seq(0.5,0.7,0.1))
+
+## ----error=TRUE---------------------------------------------------------------
+
resu1<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
param=param1,
@@ -93,7 +96,9 @@ kfino_plot(resuin=resu1,typeG="prediction",
## ----error=TRUE---------------------------------------------------------------
resu1b<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
- doOptim=TRUE,method="EM",param=param1,
+ param=param1,
+ doOptim=TRUE,
+ method="EM",
verbose=TRUE)
# flags are qualitative
diff --git a/doc/HowTo.Rmd b/doc/HowTo.Rmd
index 7300d9d97aaf9d67b4b70c50a8bd4d678fac749b..db0e09c080901181ac16e7b20078094e110718b6 100644
--- a/doc/HowTo.Rmd
+++ b/doc/HowTo.Rmd
@@ -104,7 +104,7 @@ resu2<-kfino_fit(datain=spring1,
resu2 is a list of 3 elements:
-* detectOutlier: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
+* **detectOutlier**: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
- prediction: the parameter of interest - Yvar - predicted
- label_pred: the probability of the value being well predicted
@@ -113,9 +113,8 @@ resu2 is a list of 3 elements:
- flag: flag of the value (OK value, KO value (outlier), OOR value
(out of range values defined by the user in `kfino_fit`)
-* PredictionOK: A subset of `detectOutlier` data set with the predictions
- of the analyzed variable on possible values (OK and KO values)
-* kfino.results: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
+* **PredictionOK**: A subset of `detectOutlier` data set with the predictions of the analyzed variable on possible values (OK and KO values)
+* **kfino.results**: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
```{r}
# structure of detectOutlier data set
@@ -146,10 +145,9 @@ kfino_plot(resuin=resu2,typeG="quanti",
The user can use either (Maximization Likelihood) `ML` or (Expectation-Maximization algorithm) `EM` method.
-### Maximized Likelihood (ML) method
If the user chooses to optimize the initial parameters, m0, mm and pp must be set to NULL.
-```{r,error=TRUE}
+```{r}
# --- With Optimisation on parameters
param1<-list(m0=NULL,
mm=NULL,
@@ -163,6 +161,12 @@ param1<-list(m0=NULL,
K=2,
seqp=seq(0.5,0.7,0.1))
+```
+
+### Maximized Likelihood (ML) method
+
+```{r,error=TRUE}
+
resu1<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
param=param1,
@@ -192,7 +196,9 @@ kfino_plot(resuin=resu1,typeG="prediction",
```{r,error=TRUE}
resu1b<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
- doOptim=TRUE,method="EM",param=param1,
+ param=param1,
+ doOptim=TRUE,
+ method="EM",
verbose=TRUE)
# flags are qualitative
@@ -210,7 +216,7 @@ kfino_plot(resuin=resu1b,typeG="prediction",
# Description of the `merinos1` dataset
-The user can test the **kfino** method using another data set given in the package. Here, we test with the `merinos1`data set on a ewe lamb. For this animal,the range weight is between 10 and 45 kg and must be given in the initial parameters of the `kfino_fit()`function.
+The user can test the **kfino** method using another data set given in the package. Here, we test with the `merinos1` data set on a ewe lamb. For this animal,the range weight is between 10 and 45 kg and must be given in the initial parameters of the `kfino_fit()`function.
```{r}
diff --git a/doc/HowTo.html b/doc/HowTo.html
index d1b5598ebd6bb080e1ea78c5ea95fedd3c021efe..abadc56a93c54951f78474aaacdc9ef242bb2dd2 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 14, 2022</h4>
+<h4 class="date">octobre 05, 2022</h4>
</div>
@@ -1648,10 +1648,10 @@ resu2<-kfino_fit(datain=spring1,
#> [1] 41.0 0.5 45.0</code></pre>
<p>resu2 is a list of 3 elements:</p>
<ul>
-<li><p>detectOutlier: The whole input data set with the detected
-outliers flagged and the prediction of the analyzed variable. the
-following columns are joined to the columns present in the input data
-set:</p>
+<li><p><strong>detectOutlier</strong>: The whole input data set with the
+detected outliers flagged and the prediction of the analyzed variable.
+the following columns are joined to the columns present in the input
+data set:</p>
<ul>
<li>prediction: the parameter of interest - Yvar - predicted</li>
<li>label_pred: the probability of the value being well predicted</li>
@@ -1662,13 +1662,13 @@ value</li>
<li>flag: flag of the value (OK value, KO value (outlier), OOR value
(out of range values defined by the user in <code>kfino_fit</code>)</li>
</ul></li>
-<li><p>PredictionOK: A subset of <code>detectOutlier</code> data set
-with the predictions of the analyzed variable on possible values (OK and
-KO values)</p></li>
-<li><p>kfino.results: kfino results (a list of vectors, prediction,
-probability to be an outlier , likelihood, confidence interval of the
-prediction and the flag of the data) on input parameters that were
-optimized if the user chooses this option</p></li>
+<li><p><strong>PredictionOK</strong>: A subset of
+<code>detectOutlier</code> data set with the predictions of the analyzed
+variable on possible values (OK and KO values)</p></li>
+<li><p><strong>kfino.results</strong>: kfino results (a list of vectors,
+prediction, probability to be an outlier , likelihood, confidence
+interval of the prediction and the flag of the data) on input parameters
+that were optimized if the user chooses this option</p></li>
</ul>
<pre class="r"><code># structure of detectOutlier data set
str(resu2$detectOutlier)
@@ -1721,9 +1721,6 @@ kfino_plot(resuin=resu2,typeG="quanti",
and pp) optimized</h2>
<p>The user can use either (Maximization Likelihood) <code>ML</code> or
(Expectation-Maximization algorithm) <code>EM</code> method.</p>
-<div id="maximized-likelihood-ml-method" class="section level3" number="3.2.1">
-<h3><span class="header-section-number">3.2.1</span> Maximized
-Likelihood (ML) method</h3>
<p>If the user chooses to optimize the initial parameters, m0, mm and pp
must be set to NULL.</p>
<pre class="r"><code># --- With Optimisation on parameters
@@ -1737,8 +1734,11 @@ param1<-list(m0=NULL,
sigma2_mm=0.05,
sigma2_pp=5,
K=2,
- seqp=seq(0.5,0.7,0.1))
-
+ seqp=seq(0.5,0.7,0.1))</code></pre>
+<div id="maximized-likelihood-ml-method" class="section level3" number="3.2.1">
+<h3><span class="header-section-number">3.2.1</span> Maximized
+Likelihood (ML) method</h3>
+<pre class="r"><code>
resu1<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
param=param1,
@@ -1776,7 +1776,9 @@ kfino_plot(resuin=resu1,typeG="quanti",
Expectation-Maximization (EM) method</h3>
<pre class="r"><code>resu1b<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
- doOptim=TRUE,method="EM",param=param1,
+ param=param1,
+ doOptim=TRUE,
+ method="EM",
verbose=TRUE)
#> [1] "-------:"
#> [1] "Optimization of initial parameters with EM method - result:"
@@ -1809,7 +1811,7 @@ kfino_plot(resuin=resu1b,typeG="prediction",
<code>merinos1</code> dataset</h1>
<p>The user can test the <strong>kfino</strong> method using another
data set given in the package. Here, we test with the
-<code>merinos1</code>data set on a ewe lamb. For this animal,the range
+<code>merinos1</code> data set on a ewe lamb. For this animal,the range
weight is between 10 and 45 kg and must be given in the initial
parameters of the <code>kfino_fit()</code>function.</p>
<pre class="r"><code>data(merinos1)
@@ -1919,12 +1921,12 @@ 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
+#> [5] jquerylib_0.1.4 tools_4.2.1 digest_0.6.29 jsonlite_1.8.2
#> [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
+#> [13] pkgconfig_2.0.3 rlang_1.0.6 cli_3.4.1 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
+#> [25] vctrs_0.4.2 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
#> [33] farver_2.1.1 purrr_0.3.4 magrittr_2.0.3 ellipsis_0.3.2
#> [37] scales_1.2.1 htmltools_0.5.3 assertthat_0.2.1 colorspace_2.0-3
diff --git a/doc/multipleFit.html b/doc/multipleFit.html
index 62d44b09109d1377b4c4de35d3a240a008a51ac7..d145ce47c18df6cbae33ecaa3810d478b75e5815 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 14, 2022</h4>
+<h4 class="date">octobre 05, 2022</h4>
</div>
@@ -1571,7 +1571,7 @@ for (i in seq_along(myIDE)){
#> [1] "250017033503096"
#> [1] 566 5
Sys.time() - t0
-#> Time difference of 18.46779 secs
+#> Time difference of 18.57455 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 9.275384 secs
+#> Time difference of 9.251507 secs
print(length(resu2))
#> [1] 4</code></pre>
@@ -1666,12 +1666,12 @@ 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
+#> [5] jquerylib_0.1.4 tools_4.2.1 digest_0.6.29 jsonlite_1.8.2
#> [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
+#> [13] pkgconfig_2.0.3 rlang_1.0.6 cli_3.4.1 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
+#> [25] vctrs_0.4.2 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
#> [33] farver_2.1.1 purrr_0.3.4 magrittr_2.0.3 codetools_0.2-18
#> [37] ellipsis_0.3.2 scales_1.2.1 htmltools_0.5.3 assertthat_0.2.1
diff --git a/vignettes/HowTo.Rmd b/vignettes/HowTo.Rmd
index 7300d9d97aaf9d67b4b70c50a8bd4d678fac749b..db0e09c080901181ac16e7b20078094e110718b6 100644
--- a/vignettes/HowTo.Rmd
+++ b/vignettes/HowTo.Rmd
@@ -104,7 +104,7 @@ resu2<-kfino_fit(datain=spring1,
resu2 is a list of 3 elements:
-* detectOutlier: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
+* **detectOutlier**: The whole input data set with the detected outliers flagged and the prediction of the analyzed variable. the following columns are joined to the columns present in the input data set:
- prediction: the parameter of interest - Yvar - predicted
- label_pred: the probability of the value being well predicted
@@ -113,9 +113,8 @@ resu2 is a list of 3 elements:
- flag: flag of the value (OK value, KO value (outlier), OOR value
(out of range values defined by the user in `kfino_fit`)
-* PredictionOK: A subset of `detectOutlier` data set with the predictions
- of the analyzed variable on possible values (OK and KO values)
-* kfino.results: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
+* **PredictionOK**: A subset of `detectOutlier` data set with the predictions of the analyzed variable on possible values (OK and KO values)
+* **kfino.results**: kfino results (a list of vectors, prediction, probability to be an outlier , likelihood, confidence interval of the prediction and the flag of the data) on input parameters that were optimized if the user chooses this option
```{r}
# structure of detectOutlier data set
@@ -146,10 +145,9 @@ kfino_plot(resuin=resu2,typeG="quanti",
The user can use either (Maximization Likelihood) `ML` or (Expectation-Maximization algorithm) `EM` method.
-### Maximized Likelihood (ML) method
If the user chooses to optimize the initial parameters, m0, mm and pp must be set to NULL.
-```{r,error=TRUE}
+```{r}
# --- With Optimisation on parameters
param1<-list(m0=NULL,
mm=NULL,
@@ -163,6 +161,12 @@ param1<-list(m0=NULL,
K=2,
seqp=seq(0.5,0.7,0.1))
+```
+
+### Maximized Likelihood (ML) method
+
+```{r,error=TRUE}
+
resu1<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
param=param1,
@@ -192,7 +196,9 @@ kfino_plot(resuin=resu1,typeG="prediction",
```{r,error=TRUE}
resu1b<-kfino_fit(datain=spring1,
Tvar="dateNum",Yvar="Poids",
- doOptim=TRUE,method="EM",param=param1,
+ param=param1,
+ doOptim=TRUE,
+ method="EM",
verbose=TRUE)
# flags are qualitative
@@ -210,7 +216,7 @@ kfino_plot(resuin=resu1b,typeG="prediction",
# Description of the `merinos1` dataset
-The user can test the **kfino** method using another data set given in the package. Here, we test with the `merinos1`data set on a ewe lamb. For this animal,the range weight is between 10 and 45 kg and must be given in the initial parameters of the `kfino_fit()`function.
+The user can test the **kfino** method using another data set given in the package. Here, we test with the `merinos1` data set on a ewe lamb. For this animal,the range weight is between 10 and 45 kg and must be given in the initial parameters of the `kfino_fit()`function.
```{r}