From 521634e424ae2e27e1e9d4f5a63f3db5ccb312f8 Mon Sep 17 00:00:00 2001 From: David Date: Mon, 14 Oct 2024 19:56:34 +0100 Subject: [PATCH] updated vignettes --- README.md | 22 ++++++++++-------- .../figs/diag/invariant_param_priorpost.png | Bin 157845 -> 159170 bytes .../post/ab_kinetics_trajectories_High.png | Bin 1043357 -> 864693 bytes .../post/ab_kinetics_trajectories_none.png | Bin 388988 -> 520864 bytes .../figs/diag/invariant_param_priorpost.png | Bin 155773 -> 153264 bytes .../post/ab_kinetics_trajectories_High.png | Bin 732098 -> 821003 bytes .../post/ab_kinetics_trajectories_none.png | Bin 556962 -> 473854 bytes vignettes/data_format.Rmd | 2 +- vignettes/model_define.Rmd | 6 ++--- vignettes/sim_recovery.Rmd | 4 ++-- 10 files changed, 18 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index d1db1b90..affd1e11 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # serojump -![plot](./images/schematic_A.png) +![Schematic of the methods behind the `serojump` package](./images/schematic_A.png) ## Overview @@ -8,11 +8,11 @@ The `serojump` package provides tools for fitting serological models to antibody This package is ideal for researchers looking to: -- Model antibody kinetics over time. -- Estimate infection rates and antibody waning using serological data. -- Perform MCMC inference on serological models. +- Model antibody kinetics over time +- Estimate infection rates and antibody waning using serological data +- Perform MCMC inference on serological models -The best place to get started with serojump is our preprint. In this repo, ceveral model templates are provided to simplify usage, while also allowing users to customize models for specific research questions. +The best place to get started with serojump is our preprint (coming soon). In this repo, ceveral model templates are provided to simplify usage, while also allowing users to customize models for specific research questions. ## Installation @@ -26,7 +26,8 @@ Make sure you have R installed on your system. You can download R from [https:// You can install the development version of `serojump` from GitHub using the `devtools` package. If you don't already have `devtools` installed, you can install it with: -```{r} + +``` install.packages("devtools") devtools::install_github("seroanalytics/serojump") @@ -36,7 +37,8 @@ devtools::install_github("seroanalytics/serojump") ### Step 3: Load `serojump` from GitHub After installation, you can load the serojump package into your R session with: -```{r} + +``` library(serojump) @@ -46,12 +48,12 @@ library(serojump) For detailed usage instructions, please refer to the package vignettes and examples. These vignettes explain: -- The format of the data require for serojump to work, click [here](vignette("data_format.Rmd")). -- How to define observational models and antibody kinetics models and run the serojump sampler, click [here](vignette("model_define")). +- The format of the data require for serojump to work, click [here](https://seroanalytics.org/serojump/articles/data_format.html). +- How to define observational models and antibody kinetics models and run the serojump sampler, click [here](https://seroanalytics.org/serojump/articles/model_define.html). - Specify priors for your models and integrate empirical data [coming soon] In addition we have several worked examples of `serojump` on: -- Simulated data [here](vignette("sim_recovery")). +- Simulated data click [here](https://seroanalytics.org/serojump/articles/sim_recovery.html). --- diff --git a/outputs/fits/simulated_data/cop/figs/diag/invariant_param_priorpost.png 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Hodgson" date: "`r Sys.Date()`" output: html_document --- diff --git a/vignettes/model_define.Rmd b/vignettes/model_define.Rmd index 27bf7e24..2e139838 100644 --- a/vignettes/model_define.Rmd +++ b/vignettes/model_define.Rmd @@ -16,7 +16,7 @@ This document explains two critical parts of the model defined in the provided R ## 0. The Observational Model -```{r} +```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} library(devtools) library(tidyr) @@ -204,7 +204,7 @@ seroModel <- createSeroJumpModel(data_sero, NULL, modeldefinition) ## 4. Run the model! -```{r} +```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} save_info <- list( file_name = "simple", @@ -224,7 +224,7 @@ model_summary <- runInfRJMCMC(seroModel, rj_settings, save_info = save_info) ## 5. Plot the outputs -```{r} +```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} # Need to have save_info model_summary to run these plotMCMCDiagnosis(model_summary, save_info = save_info) # plots the convergence diagnosis diff --git a/vignettes/sim_recovery.Rmd b/vignettes/sim_recovery.Rmd index 1a682cc5..81510b1a 100644 --- a/vignettes/sim_recovery.Rmd +++ b/vignettes/sim_recovery.Rmd @@ -13,7 +13,7 @@ This document describes the steps and components of the provided R code for crea --- ## Step 1: Load Required Libraries -```{r} +```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} library(devtools) library(tidyr) @@ -224,7 +224,7 @@ model_no_cop <- createSeroJumpModel(data_titre_model, NULL, modeldefinition_no_c ``` -```{r} +```{r, echo=FALSE, message=FALSE, warning=FALSE, results='hide'} rj_settings <- list( numberChainRuns = 4,

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