diff --git a/NAMESPACE b/NAMESPACE
index e77736fa579b8a18407b5391ac607036007488da..4ec440dd0859d60a0f8a023969c04046f5634fc6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -100,6 +100,8 @@ export(get_eval_data_from_dds)
export(get_eval_data_from_ltmb)
export(get_inference_dds)
export(get_label_y_position)
+export(get_mad_left_threshold)
+export(get_mad_user_message)
export(get_ml_metrics_obj)
export(get_performances_metrics_obj)
export(get_pr_curve)
@@ -110,6 +112,7 @@ export(get_rsquare_2plot)
export(glance_tmb)
export(group_logQij_per_genes_and_labels)
export(handleAnovaError)
+export(identifyTopFit)
export(inferenceToExpected_withFixedEff)
export(inferenceToExpected_withMixedEff)
export(init_variable)
@@ -203,6 +206,7 @@ importFrom(stats,approxfun)
importFrom(stats,as.formula)
importFrom(stats,cor)
importFrom(stats,drop.terms)
+importFrom(stats,mad)
importFrom(stats,median)
importFrom(stats,model.matrix)
importFrom(stats,p.adjust)
diff --git a/R/filtering_fit.R b/R/filtering_fit.R
new file mode 100644
index 0000000000000000000000000000000000000000..302c9121cbce6ad7d8100a9fce6fe1d0bce8e380
--- /dev/null
+++ b/R/filtering_fit.R
@@ -0,0 +1,117 @@
+# WARNING - Generated by {fusen} from dev/flat_full.Rmd: do not edit by hand
+
+
+#' Identify top or low fitting observations based on specified diagnostic metric and filtering method.
+#'
+#' This function identifies top or low fitting observations based on a specified metric and filtering method.
+#'
+#' @param list_tmb List of glmmTMB objects.
+#' @param metric The metric used for diagnostic (e.g., "AIC", "BIC", "logLik", "deviance", "dispersion").
+#' @param filter_method The filtering method to be used (e.g., "mad").
+#' Feel free to implement your own filetering method
+#' @param keep Whether to keep "top" or "low" fitting observations.
+#' @param sort Logical indicating whether to sort the results.
+#' @param decreasing Logical indicating whether to sort in decreasing order.
+#' @param mad_tolerance Tolerance for MAD-based filtering.
+#' @return A character vector of row names corresponding to the top or low fitting observations.
+#' @export
+#'
+#' @examples
+#' input_var_list <- init_variable()
+#' ## -- simulate RNAseq data
+#' mock_data <- mock_rnaseq(input_var_list,
+#' n_genes = 5,
+#' min_replicates = 3,
+#' max_replicates = 3,
+#' basal_expression = 2,
+#' sequencing_depth = 1e5)
+#' ## -- prepare data & fit a model with mixed effect
+#' data2fit = prepareData2fit(countMatrix = mock_data$counts,
+#' metadata = mock_data$metadata)
+#'l_tmb <- fitModelParallel(formula = kij ~ myVariable, data = data2fit,
+#' group_by = "geneID", family = glmmTMB::nbinom2(link = "log"),
+#' n.cores = 1)
+#' # Identify top fitting observations based on AIC with MAD filtering
+#' identifyTopFit(l_tmb, metric = "AIC", filter_method = "mad", keep = "top", sort = TRUE, decreasing = TRUE, mad_tolerance = 3)
+#'
+#' # Identify low fitting observations based on BIC without sorting
+#' identifyTopFit(l_tmb, metric = "BIC", filter_method = "mad", keep = "low", sort = FALSE)
+#'
+#' # Identify top fitting observations based on log-likelihood with MAD filtering and custom tolerance
+#' identifyTopFit(l_tmb, metric = "logLik", filter_method = "mad", keep = "top", mad_tolerance = 2)
+identifyTopFit <- function(list_tmb, metric = "AIC", filter_method = "mad", keep = "top",
+ sort = F, decreasing = T, mad_tolerance = 3){
+
+ ## -- verif
+ stopifnot(is.list(list_tmb))
+ stopifnot(metric %in% c("AIC", "BIC", "logLik", "deviance", "dispersion"))
+
+ glance_df <- glance_tmb(list_tmb)
+ ## -- MAD method
+ if (filter_method == "mad"){
+ lft_threshold <- get_mad_left_threshold(glance_df[[metric]], mad_tolerance)
+ get_mad_user_message(metric, glance_df[[metric]], lft_threshold, keep)
+ index2keep <- if (keep == "top") glance_df[[metric]] > lft_threshold else glance_df[[metric]] < lft_threshold
+ id2keep <- rownames(glance_df)[index2keep]
+ glance_df <- glance_df[ id2keep , ]
+ } ## -- feel free to implement other filtering methods ..
+
+ ## -- sort
+ if (isTRUE(sort)){
+ id2keep <- rownames(glance_df)[order(glance_df[[metric]], decreasing = decreasing)]
+ }
+ return(id2keep)
+}
+
+#' Calculate the left threshold for MAD-based filtering.
+#'
+#' This function calculates the left threshold for MAD-based filtering.
+#'
+#' @param x Vector of values used for filtering.
+#' @param tolerance Tolerance value for MAD-based filtering.
+#' @return The left threshold value.
+#' @importFrom stats median mad
+#' @export
+#'
+#' @examples
+#' # Calculate the left threshold for MAD-based filtering with tolerance 3
+#' get_mad_left_threshold(c(100, 200, 300), 3)
+#'
+#' # Calculate the left threshold for MAD-based filtering with tolerance 2
+#' get_mad_left_threshold(c(50, 75, 100), 2)
+get_mad_left_threshold <- function(x, tolerance){
+ med <- stats::median(x)
+ mad <- stats::mad(x, center = median(x),
+ constant = 1, na.rm = TRUE,
+ low = FALSE, high = FALSE)
+ med - (tolerance * mad)
+}
+
+#' Generate user message for MAD filtering.
+#'
+#' This function generates a user message explaining the MAD filtering process.
+#'
+#' @param var The name of the metric used for filtering.
+#' @param x Vector of values used for filtering.
+#' @param left_threshold The left threshold for filtering.
+#' @param keep Whether to keep "top" or "low" observations.
+#' @return A message explaining the MAD filtering process.
+#' @export
+#'
+#' @examples
+#' # Generate user message for MAD filtering with top fitting observations
+#' get_mad_user_message("AIC", c(100, 200, 300), 150, "top")
+#'
+#' # Generate user message for MAD filtering with low fitting observations
+#' get_mad_user_message("BIC", c(50, 75, 100), 75, "low")
+get_mad_user_message <- function(var, x , left_threshold, keep) {
+ message("Based on the specified metric (", var, ") and the MAD filtering method, the following selection criteria were applied:")
+ message("1. The MAD-based threshold for considering outliers was calculated.")
+ keep_str <- ifelse(keep == "top", "above", "bellow")
+ message(paste("2. Values", keep_str , "the threshold were identified, threshold:", left_threshold))
+ message("3. Summary of selection:")
+ nb_keep <- ifelse(keep == "top", length(x[x > left_threshold]), length(x[x < left_threshold]) )
+ sub_msg2 <- paste("values", keep_str, "the threshold")
+ message(paste("-", nb_keep, "out of", length(x), "observations had", sub_msg2, "for the", var, "metric."))
+}
+
diff --git a/dev/flat_full.Rmd b/dev/flat_full.Rmd
index 32fbba8ecdc446281c21728a0a810d91076fddc0..be40c69583b9eaee0554cc5468c57c490ea3fcf5 100644
--- a/dev/flat_full.Rmd
+++ b/dev/flat_full.Rmd
@@ -3142,6 +3142,177 @@ test_that("fitModelParallel fits models in parallel for each group and returns a
```
+```{r function-filtering_fit, filename = "filtering_fit"}
+
+#' Identify top or low fitting observations based on specified diagnostic metric and filtering method.
+#'
+#' This function identifies top or low fitting observations based on a specified metric and filtering method.
+#'
+#' @param list_tmb List of glmmTMB objects.
+#' @param metric The metric used for diagnostic (e.g., "AIC", "BIC", "logLik", "deviance", "dispersion").
+#' @param filter_method The filtering method to be used (e.g., "mad").
+#' Feel free to implement your own filetering method
+#' @param keep Whether to keep "top" or "low" fitting observations.
+#' @param sort Logical indicating whether to sort the results.
+#' @param decreasing Logical indicating whether to sort in decreasing order.
+#' @param mad_tolerance Tolerance for MAD-based filtering.
+#' @return A character vector of row names corresponding to the top or low fitting observations.
+#' @export
+#'
+#' @examples
+#' input_var_list <- init_variable()
+#' ## -- simulate RNAseq data
+#' mock_data <- mock_rnaseq(input_var_list,
+#' n_genes = 5,
+#' min_replicates = 3,
+#' max_replicates = 3,
+#' basal_expression = 2,
+#' sequencing_depth = 1e5)
+#' ## -- prepare data & fit a model with mixed effect
+#' data2fit = prepareData2fit(countMatrix = mock_data$counts,
+#' metadata = mock_data$metadata)
+#'l_tmb <- fitModelParallel(formula = kij ~ myVariable, data = data2fit,
+#' group_by = "geneID", family = glmmTMB::nbinom2(link = "log"),
+#' n.cores = 1)
+#' # Identify top fitting observations based on AIC with MAD filtering
+#' identifyTopFit(l_tmb, metric = "AIC", filter_method = "mad", keep = "top",
+#' sort = TRUE, decreasing = TRUE, mad_tolerance = 3)
+#'
+#' # Identify low fitting observations based on BIC without sorting
+#' identifyTopFit(l_tmb, metric = "BIC", filter_method = "mad", keep = "low", sort = FALSE)
+#'
+#' # Identify top fitting observations based on log-likelihood with MAD filtering and custom tolerance
+#' identifyTopFit(l_tmb, metric = "logLik", filter_method = "mad", keep = "top", mad_tolerance = 2)
+identifyTopFit <- function(list_tmb, metric = "AIC", filter_method = "mad", keep = "top",
+ sort = F, decreasing = T, mad_tolerance = 3){
+
+ ## -- verif
+ stopifnot(is.list(list_tmb))
+ stopifnot(metric %in% c("AIC", "BIC", "logLik", "deviance", "dispersion"))
+
+ glance_df <- glance_tmb(list_tmb)
+ ## -- MAD method
+ if (filter_method == "mad"){
+ lft_threshold <- get_mad_left_threshold(glance_df[[metric]], mad_tolerance)
+ get_mad_user_message(metric, glance_df[[metric]], lft_threshold, keep)
+ index2keep <- if (keep == "top") glance_df[[metric]] > lft_threshold else glance_df[[metric]] < lft_threshold
+ id2keep <- rownames(glance_df)[index2keep]
+ glance_df <- glance_df[ id2keep , ]
+ } ## -- feel free to implement other filtering methods ..
+
+ ## -- sort
+ if (isTRUE(sort)){
+ id2keep <- rownames(glance_df)[order(glance_df[[metric]], decreasing = decreasing)]
+ }
+ return(id2keep)
+}
+
+#' Calculate the left threshold for MAD-based filtering.
+#'
+#' This function calculates the left threshold for MAD-based filtering.
+#'
+#' @param x Vector of values used for filtering.
+#' @param tolerance Tolerance value for MAD-based filtering.
+#' @return The left threshold value.
+#' @importFrom stats median mad
+#' @export
+#'
+#' @examples
+#' # Calculate the left threshold for MAD-based filtering with tolerance 3
+#' get_mad_left_threshold(c(100, 200, 300), 3)
+#'
+#' # Calculate the left threshold for MAD-based filtering with tolerance 2
+#' get_mad_left_threshold(c(50, 75, 100), 2)
+get_mad_left_threshold <- function(x, tolerance){
+ med <- stats::median(x)
+ mad <- stats::mad(x, center = median(x),
+ constant = 1, na.rm = TRUE,
+ low = FALSE, high = FALSE)
+ med - (tolerance * mad)
+}
+
+#' Generate user message for MAD filtering.
+#'
+#' This function generates a user message explaining the MAD filtering process.
+#'
+#' @param var The name of the metric used for filtering.
+#' @param x Vector of values used for filtering.
+#' @param left_threshold The left threshold for filtering.
+#' @param keep Whether to keep "top" or "low" observations.
+#' @return A message explaining the MAD filtering process.
+#' @export
+#'
+#' @examples
+#' # Generate user message for MAD filtering with top fitting observations
+#' get_mad_user_message("AIC", c(100, 200, 300), 150, "top")
+#'
+#' # Generate user message for MAD filtering with low fitting observations
+#' get_mad_user_message("BIC", c(50, 75, 100), 75, "low")
+get_mad_user_message <- function(var, x , left_threshold, keep) {
+ message("Based on the specified metric (", var, ") and the MAD filtering method, the following selection criteria were applied:")
+ message("1. The MAD-based threshold for considering outliers was calculated.")
+ keep_str <- ifelse(keep == "top", "above", "bellow")
+ message(paste("2. Values", keep_str , "the threshold were identified, threshold:", left_threshold))
+ message("3. Summary of selection:")
+ nb_keep <- ifelse(keep == "top", length(x[x > left_threshold]), length(x[x < left_threshold]) )
+ sub_msg2 <- paste("values", keep_str, "the threshold")
+ message(paste("-", nb_keep, "out of", length(x), "observations had", sub_msg2, "for the", var, "metric."))
+}
+
+```
+
+
+```{r test-update_fittedmodel}
+
+# Tests for identifyTopFit function
+test_that("identifyTopFit correctly identifies top-fitting objects", {
+ input_var_list <- init_variable()
+ ## -- simulate RNAseq data
+ set.seed(101)
+ mock_data <- mock_rnaseq(input_var_list,
+ n_genes = 5,
+ min_replicates = 3,
+ max_replicates = 3,
+ basal_expression = 2,
+ sequencing_depth = 1e5)
+ ## -- prepare data & fit a model with mixed effect
+ data2fit = prepareData2fit(countMatrix = mock_data$counts,
+ metadata = mock_data$metadata,
+ normalization = T)
+ l_tmb <- fitModelParallel(formula = kij ~ myVariable, data = data2fit,
+ group_by = "geneID", family = glmmTMB::nbinom2(link = "log"),
+ n.cores = 1)
+ glance_tmb(l_tmb)
+ # Identify top fitting observations based on AIC with MAD filtering
+ top_genes <- identifyTopFit(l_tmb, metric = "AIC", filter_method = "mad", keep = "top",
+ sort = TRUE, decreasing = TRUE, mad_tolerance = 3)
+ expect_equal(top_genes, c("gene5", "gene4", "gene1", "gene2", "gene3"))
+ # Identify low fitting observations based on BIC without sorting
+ top_genes <-identifyTopFit(l_tmb, metric = "BIC", filter_method = "mad", keep = "low", sort = FALSE)
+ expect_equal(top_genes, character())
+
+ # Identify top fitting observations based on log-likelihood with MAD filtering and custom tolerance
+ top_genes <- identifyTopFit(l_tmb, metric = "logLik", filter_method = "mad", keep = "top", mad_tolerance = 2)
+ expect_equal(top_genes, c("gene1", "gene2", "gene3", "gene4"))
+})
+
+# Tests for get_mad_left_threshold function
+test_that("get_mad_left_threshold correctly calculates MAD-based left threshold", {
+ left_threshold <- get_mad_left_threshold(c(100, 200, 300), 3)
+ expect_equal(left_threshold, 200 - (3 * 100))
+})
+
+
+# Tests for get_mad_message
+test_that('get_mad_user_message return correct output', {
+ expect_message(get_mad_user_message("BIC", c(50, 75, 100), 75, "low"))
+ expect_message(get_mad_user_message("BIC", c(50, 75, 100), 75, "top"))
+})
+
+
+```
+
+
```{r function-update_fittedmodel, filename = "update_fittedmodel"}
diff --git a/man/get_mad_left_threshold.Rd b/man/get_mad_left_threshold.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..ddcd334b9df4f2fc4d4b6d8f514d1fbfa1a50641
--- /dev/null
+++ b/man/get_mad_left_threshold.Rd
@@ -0,0 +1,26 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/filtering_fit.R
+\name{get_mad_left_threshold}
+\alias{get_mad_left_threshold}
+\title{Calculate the left threshold for MAD-based filtering.}
+\usage{
+get_mad_left_threshold(x, tolerance)
+}
+\arguments{
+\item{x}{Vector of values used for filtering.}
+
+\item{tolerance}{Tolerance value for MAD-based filtering.}
+}
+\value{
+The left threshold value.
+}
+\description{
+This function calculates the left threshold for MAD-based filtering.
+}
+\examples{
+# Calculate the left threshold for MAD-based filtering with tolerance 3
+get_mad_left_threshold(c(100, 200, 300), 3)
+
+# Calculate the left threshold for MAD-based filtering with tolerance 2
+get_mad_left_threshold(c(50, 75, 100), 2)
+}
diff --git a/man/get_mad_user_message.Rd b/man/get_mad_user_message.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..bf14154d4ab63f3468f66a78a0a6d700b35e43c2
--- /dev/null
+++ b/man/get_mad_user_message.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/filtering_fit.R
+\name{get_mad_user_message}
+\alias{get_mad_user_message}
+\title{Generate user message for MAD filtering.}
+\usage{
+get_mad_user_message(var, x, left_threshold, keep)
+}
+\arguments{
+\item{var}{The name of the metric used for filtering.}
+
+\item{x}{Vector of values used for filtering.}
+
+\item{left_threshold}{The left threshold for filtering.}
+
+\item{keep}{Whether to keep "top" or "low" observations.}
+}
+\value{
+A message explaining the MAD filtering process.
+}
+\description{
+This function generates a user message explaining the MAD filtering process.
+}
+\examples{
+# Generate user message for MAD filtering with top fitting observations
+get_mad_user_message("AIC", c(100, 200, 300), 150, "top")
+
+# Generate user message for MAD filtering with low fitting observations
+get_mad_user_message("BIC", c(50, 75, 100), 75, "low")
+}
diff --git a/man/identifyTopFit.Rd b/man/identifyTopFit.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..300e0b587bd5587f2cf0f448b0187d5ce868f67e
--- /dev/null
+++ b/man/identifyTopFit.Rd
@@ -0,0 +1,62 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/filtering_fit.R
+\name{identifyTopFit}
+\alias{identifyTopFit}
+\title{Identify top or low fitting observations based on specified diagnostic metric and filtering method.}
+\usage{
+identifyTopFit(
+ list_tmb,
+ metric = "AIC",
+ filter_method = "mad",
+ keep = "top",
+ sort = F,
+ decreasing = T,
+ mad_tolerance = 3
+)
+}
+\arguments{
+\item{list_tmb}{List of glmmTMB objects.}
+
+\item{metric}{The metric used for diagnostic (e.g., "AIC", "BIC", "logLik", "deviance", "dispersion").}
+
+\item{filter_method}{The filtering method to be used (e.g., "mad").
+Feel free to implement your own filetering method}
+
+\item{keep}{Whether to keep "top" or "low" fitting observations.}
+
+\item{sort}{Logical indicating whether to sort the results.}
+
+\item{decreasing}{Logical indicating whether to sort in decreasing order.}
+
+\item{mad_tolerance}{Tolerance for MAD-based filtering.}
+}
+\value{
+A character vector of row names corresponding to the top or low fitting observations.
+}
+\description{
+This function identifies top or low fitting observations based on a specified metric and filtering method.
+}
+\examples{
+input_var_list <- init_variable()
+## -- simulate RNAseq data
+mock_data <- mock_rnaseq(input_var_list,
+ n_genes = 5,
+ min_replicates = 3,
+ max_replicates = 3,
+ basal_expression = 2,
+ sequencing_depth = 1e5)
+## -- prepare data & fit a model with mixed effect
+data2fit = prepareData2fit(countMatrix = mock_data$counts,
+ metadata = mock_data$metadata)
+l_tmb <- fitModelParallel(formula = kij ~ myVariable, data = data2fit,
+ group_by = "geneID", family = glmmTMB::nbinom2(link = "log"),
+ n.cores = 1)
+# Identify top fitting observations based on AIC with MAD filtering
+identifyTopFit(l_tmb, metric = "AIC", filter_method = "mad", keep = "top", sort = TRUE, decreasing = TRUE, mad_tolerance = 3)
+
+# Identify low fitting observations based on BIC without sorting
+identifyTopFit(l_tmb, metric = "BIC", filter_method = "mad", keep = "low", sort = FALSE)
+
+# Identify top fitting observations based on log-likelihood with MAD filtering and custom tolerance
+identifyTopFit(l_tmb, metric = "logLik", filter_method = "mad", keep = "top", mad_tolerance = 2)
+}
diff --git a/tests/testthat/test-filtering_fit.R b/tests/testthat/test-filtering_fit.R
new file mode 100644
index 0000000000000000000000000000000000000000..021f0d85d500137406fa4b2073bc246523e1e5d5
--- /dev/null
+++ b/tests/testthat/test-filtering_fit.R
@@ -0,0 +1,49 @@
+# WARNING - Generated by {fusen} from dev/flat_full.Rmd: do not edit by hand
+
+
+# Tests for identifyTopFit function
+test_that("identifyTopFit correctly identifies top-fitting objects", {
+ input_var_list <- init_variable()
+ ## -- simulate RNAseq data
+ set.seed(101)
+ mock_data <- mock_rnaseq(input_var_list,
+ n_genes = 5,
+ min_replicates = 3,
+ max_replicates = 3,
+ basal_expression = 2,
+ sequencing_depth = 1e5)
+ ## -- prepare data & fit a model with mixed effect
+ data2fit = prepareData2fit(countMatrix = mock_data$counts,
+ metadata = mock_data$metadata,
+ normalization = T)
+ l_tmb <- fitModelParallel(formula = kij ~ myVariable, data = data2fit,
+ group_by = "geneID", family = glmmTMB::nbinom2(link = "log"),
+ n.cores = 1)
+ glance_tmb(l_tmb)
+ # Identify top fitting observations based on AIC with MAD filtering
+ top_genes <- identifyTopFit(l_tmb, metric = "AIC", filter_method = "mad", keep = "top",
+ sort = TRUE, decreasing = TRUE, mad_tolerance = 3)
+ expect_equal(top_genes, c("gene5", "gene4", "gene1", "gene2", "gene3"))
+ # Identify low fitting observations based on BIC without sorting
+ top_genes <-identifyTopFit(l_tmb, metric = "BIC", filter_method = "mad", keep = "low", sort = FALSE)
+ expect_equal(top_genes, character())
+
+ # Identify top fitting observations based on log-likelihood with MAD filtering and custom tolerance
+ top_genes <- identifyTopFit(l_tmb, metric = "logLik", filter_method = "mad", keep = "top", mad_tolerance = 2)
+ expect_equal(top_genes, c("gene1", "gene2", "gene3", "gene4"))
+})
+
+# Tests for get_mad_left_threshold function
+test_that("get_mad_left_threshold correctly calculates MAD-based left threshold", {
+ left_threshold <- get_mad_left_threshold(c(100, 200, 300), 3)
+ expect_equal(left_threshold, 200 - (3 * 100))
+})
+
+
+# Tests for get_mad_message
+test_that('get_mad_user_message return correct output', {
+ expect_message(get_mad_user_message("BIC", c(50, 75, 100), 75, "low"))
+ expect_message(get_mad_user_message("BIC", c(50, 75, 100), 75, "top"))
+})
+
+
diff --git a/vignettes/03-rnaseq_analysis.Rmd b/vignettes/03-rnaseq_analysis.Rmd
index 6c89661fba3ae3a72efdf85f6a4fb9e83973bf27..f9c542e2eb513dabb2a7ff1bb1cf01477cda98d2 100644
--- a/vignettes/03-rnaseq_analysis.Rmd
+++ b/vignettes/03-rnaseq_analysis.Rmd
@@ -207,6 +207,18 @@ diagnostic_plot(list_tmb = l_tmb)
diagnostic_plot(list_tmb = l_tmb, focus = c("dispersion", "logLik"))
```
+## Select best fit based on diagnostic metrics
+
+The identifyTopFit function allows for selecting genes that best fit models, based on various metrics such as AIC, BIC, LogLik, deviance, dispersion . This function provides the flexibility to employ multiple filtering methods to identify genes most relevant for further analysis. We implemented a filtering method based on the Median Absolute Deviation (MAD). For example, using the MAD method with a tolerance threshold of 3, the function identifies genes whose metric values are greater ("top") or lower ("low") than `median(metric) - 3 * MAD(metric)`. The keep option allows choosing whether to retain genes with metric values above or below the MAD threshold.
+
+```{r example-identifytopfit, warning = FALSE, message = FALSE}
+identifyTopFit(list_tmb = l_tmb, metric = "AIC",
+ filter_method = "mad", keep = "top",
+ sort = TRUE, decreasing = TRUE,
+ mad_tolerance = 3)
+```
+
+
## Anova to select the best model
Utilizing the `anovaParallel()` function enables you to perform model selection by assessing the significance of the fixed effects. You can also include additional parameters like type. For more details, refer to [car::Anova](https://rdrr.io/cran/car/man/Anova.html).