fix: replace run_seromodel by fit_seromodel in examples and vigne…

…ttes

R/model_comparison.R

@@ -9,7 +9,7 @@
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(serodata = chagas2012)
-#' model_constant <- run_seromodel(
+#' model_constant <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1500

R/modelling.R

@@ -137,12 +137,14 @@ validate_prepared_serodata <- function(serodata) {
 #'   the implementation of the model. For further details refer to
 #'   [fit_seromodel].
 #' @examples
+#' \dontrun{
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
 #' run_seromodel(
 #'   serodata,
 #'   foi_model = "constant"
 #' )
+#' }
 #' @export
 run_seromodel <- function(
     serodata,
@@ -512,7 +514,7 @@ get_foi_central_estimates <- function(seromodel_object,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant"
 #' )
@@ -587,7 +589,7 @@ extract_seromodel_summary <- function(seromodel_object,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant"
 #' )

R/visualisation.R

@@ -87,7 +87,7 @@ plot_seroprev <- function(serodata,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1000
@@ -173,7 +173,7 @@ plot_seroprev_fitted <- function(seromodel_object,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1000
@@ -267,7 +267,7 @@ plot_foi <- function(seromodel_object,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1000
@@ -343,7 +343,7 @@ plot_rhats <- function(seromodel_object,
 #' @examples
 #' data(chagas2012)
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1000
@@ -421,7 +421,7 @@ plot_seromodel <- function(seromodel_object,
 #' @return ggplot object summarizing the information in `info_table`
 #' @examples
 #' serodata <- prepare_serodata(chagas2012)
-#' seromodel_object <- run_seromodel(
+#' seromodel_object <- fit_seromodel(
 #'   serodata = serodata,
 #'   foi_model = "constant",
 #'   iter = 1000

vignettes/foi_models.Rmd

@@ -50,14 +50,14 @@ The number of positive cases follows a binomial distribution, where $n$ is the n
 $$
 p(a,t) \sim binom(n(a,t), P(a,t))
 $$
-In ***serofoi***, for the constant model, the *FoI* ($\lambda$) is modelled within a Bayesian framework using a uniform prior distribution $\sim U(0,2)$. Future versions of the package may allow to choose different default distributions. This model can be implemented for the previously prepared dataset `data_test` by means of the `run_seromodel` function specifying  `run_seromodel="constant"`.
+In ***serofoi***, for the constant model, the *FoI* ($\lambda$) is modelled within a Bayesian framework using a uniform prior distribution $\sim U(0,2)$. Future versions of the package may allow to choose different default distributions. This model can be implemented for the previously prepared dataset `data_test` by means of the `fit_seromodel` function specifying  `fit_seromodel="constant"`.
 
 The object `simdata_constant` contains a minimal simulated dataset that emulates an hypothetical endemic situation where the *FoI* is constant with value 0.2 and includes data for 250 samples of individuals between 2 and 47 years old with a number of trials $n=5$. The following code shows how to implement the constant model to this simulated serosurvey:
 
 ```{r model_1, include = TRUE, echo = TRUE, results="hide", errors = FALSE, warning = FALSE, message = FALSE, fig.width=4, fig.asp=1.5, fig.align="center", out.width ="50%", fig.keep="none"}
 data("simdata_constant")
 serodata_constant <- prepare_serodata(simdata_constant)
-model_1 <- run_seromodel(
+model_1 <- fit_seromodel(
   serodata = serodata_constant,
   foi_model = "constant",
   iter = 800
@@ -102,7 +102,7 @@ The object `simdata_sw_dec` contains a minimal simulated dataset that emulates a
 ```{r model_2, include = TRUE, echo = TRUE, results="hide", errors = FALSE, warning = FALSE, message = FALSE, fig.width=4, fig.asp=1.5, fig.align="center", out.width ="50%", fig.keep="none"}
 data("simdata_sw_dec")
 serodata_sw_dec <- prepare_serodata(simdata_sw_dec)
-model_2 <- run_seromodel(
+model_2 <- fit_seromodel(
   serodata = serodata_sw_dec,
   foi_model = "tv_normal",
   iter = 1500
@@ -140,7 +140,7 @@ In order to test this model we use the minimal simulated dataset contained in th
 ```{r model_3, include = TRUE, echo = TRUE, results="hide", errors = FALSE, warning = FALSE, message = FALSE, fig.width=4, fig.asp=1.5, fig.align="center", out.width ="50%", fig.keep="none"}
 data("simdata_large_epi")
 serodata_large_epi <- prepare_serodata(simdata_large_epi)
-model_3 <- run_seromodel(
+model_3 <- fit_seromodel(
   serodata = serodata_large_epi,
   foi_model = "tv_normal_log",
   iter = 1500
@@ -191,7 +191,7 @@ Above we showed that the fast epidemic model (`tv_normal_log`) is able to identi
 Now, we would like to know whether this model actually fits this dataset better than the other available models in ***serofoi***. For this, we also implement both the endemic model (`constant`)   and the slow time-varying normal model (`tv_normal`):
 
 ```{r model_comparison, include = FALSE, echo = TRUE, results="hide", errors = FALSE, warning = FALSE, message = FALSE }
-model_1 <- run_seromodel(
+model_1 <- fit_seromodel(
   serodata = serodata_large_epi,
   foi_model = "constant",
   iter = 800
@@ -201,7 +201,7 @@ model_1_plot <- plot_seromodel(model_1,
   size_text = 6
 )
 
-model_2 <- run_seromodel(
+model_2 <- fit_seromodel(
   serodata = serodata_large_epi,
   foi_model = "tv_normal",
   iter = 1500

vignettes/serofoi.Rmd

@@ -63,7 +63,7 @@ library(serofoi)
 data(chagas2012)
 serodata_test <- prepare_serodata(chagas2012)
 # Model implementation
-model_constant <- run_seromodel(
+model_constant <- fit_seromodel(
   serodata = serodata_test,
   foi_model = "constant"
 )

vignettes/use_cases.Rmd

@@ -51,21 +51,21 @@ data("chik2015")
 chik2015p <- prepare_serodata(chik2015)
 
 # Implementation of the models
-m1_chik <- run_seromodel(
+m1_chik <- fit_seromodel(
   serodata = chik2015p,
   foi_model = "constant",
   iter = 1000,
   thin = 2
 )
 
-m2_chik <- run_seromodel(
+m2_chik <- fit_seromodel(
   serodata = chik2015p,
   foi_model = "tv_normal",
   iter = 1500,
   thin = 2
 )
 
-m3_chik <- run_seromodel(
+m3_chik <- fit_seromodel(
   serodata = chik2015p,
   foi_model = "tv_normal_log",
   iter = 1500,
@@ -112,21 +112,21 @@ data("veev2012")
 veev2012p <- prepare_serodata(veev2012)
 
 # Implementation of the models
-m1_veev <- run_seromodel(
+m1_veev <- fit_seromodel(
   serodata = veev2012p,
   foi_model = "constant",
   iter = 500,
   thin = 2
 )
 
-m2_veev <- run_seromodel(
+m2_veev <- fit_seromodel(
   serodata = veev2012p,
   foi_model = "tv_normal",
   iter = 500,
   thin = 2
 )
 
-m3_veev <- run_seromodel(
+m3_veev <- fit_seromodel(
   serodata = veev2012p,
   foi_model = "tv_normal_log",
   iter = 500,
@@ -172,12 +172,12 @@ data("chagas2012")
 chagas2012p <- prepare_serodata(chagas2012)
 
 # Implementation of the models
-m1_cha <- run_seromodel(
+m1_cha <- fit_seromodel(
   serodata = chagas2012p,
   foi_model = "constant",
   iter = 800
 )
-m2_cha <- run_seromodel(
+m2_cha <- fit_seromodel(
   serodata = chagas2012p,
   foi_model = "tv_normal",
   iter = 800