add vignette

only use log scale in plot if data is not already logged

NAMESPACE

@@ -5,6 +5,7 @@ S3method(plot,biokinetics_priors)
 export(add_exposure_data)
 export(biokinetics)
 export(biokinetics_priors)
+export(convert_log2_scale)
 export(convert_log2_scale_inverse)
 importFrom(R6,R6Class)
 importFrom(data.table,":=")

R/inspect-model.R

@@ -135,7 +135,7 @@ inspect_model <- function(mod, private) {
       if (is.null(selected) || selected == "None") {
         selected <- character(0)
       }
-      plot_data(data(), ncol = cols(), covariates = selected) +
+      plot_sero_data(data(), ncol = cols(), covariates = selected) +
         theme(plot.margin = unit(c(1, 0, 0, 0), "cm"))
     })
 

R/shiny-utils.R

@@ -9,7 +9,7 @@ raw_numeric_input <- function(inputId, value, min = NA, max = NA, step = NA) {
 
 raw_text_input <- function(inputId, value = "", placeholder = NULL) {
   value <- shiny::restoreInput(id = inputId, default = value)
-  tags$input(id = inputId, type = "text", class = "shiny-input-text form-control", value = value, placeholder = placeholder)
+  shiny::tags$input(id = inputId, type = "text", class = "shiny-input-text form-control", value = value, placeholder = placeholder)
 }
 
 raw_select_input <- function(inputId, choices, selected = NULL, multiple = FALSE) {
@@ -18,7 +18,7 @@ raw_select_input <- function(inputId, choices, selected = NULL, multiple = FALSE
   if (is.null(selected)) {
     if (!multiple) selected <- shiny:::firstChoice(choices)
   } else selected <- as.character(selected)
-  tags$select(id = inputId, class = "shiny-input-select", class = "form-control", shiny:::selectOptions(choices, selected, inputId))
+  shiny::tags$select(id = inputId, class = "shiny-input-select", class = "form-control", shiny:::selectOptions(choices, selected, inputId))
 }
 
 

R/utils.R

@@ -1,3 +1,11 @@
+#' @title Base 2 log scale conversion
+#'
+#' @description Natural scale data is converted to a base 2 log scale before model fitting using
+#' this function. This function does not modify the provided data.table in-place, but returns a transformed copy.
+#' @return A data.table, identical to the input data but with specified columns transformed.
+#' @param dt_in data.table containing data to be transformed from natural to base 2 log scale.
+#' @param vars_to_transform Names of columns to apply the transformation to.
+#' @export
 convert_log2_scale <- function(
   dt_in, vars_to_transform = "value",
   simplify_limits = TRUE) {

README.md

@@ -81,6 +81,12 @@ To run all tests locally:
 devtools::test()
 ```
 
+To run tests in a single file:
+
+```{r}
+devtools::test(filter="filename")
+```
+
 Some tests are skipped on CI to avoid exorbitantly long build times, but this means 
 it is important to run all tests locally at least once before merging a pull request.
 

vignettes/diagnostics.Rmd

@@ -0,0 +1,64 @@
+---
+title: "Model diagnostics and plotting"
+output: rmarkdown::html_vignette
+vignette: >
+    %\VignetteIndexEntry{Model diagnostics and plotting}
+    %\VignetteEngine{knitr::rmarkdown}
+    %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+## Plotting model inputs
+
+### Priors
+Calling `plot` on an object of type [biokinetics_priors](../reference/biokinetics_priors.html) will generate
+a plot of the kinetics predicted by the given priors.
+
+```{r}
+priors <- epikinetics::biokinetics_priors()
+plot(priors)
+```
+
+You can optionally pass a dataset to compare against the predicted kinetics. Required columns are
+`time_since_last_exp` and `value` and values should be on the scale required by the model.
+
+```{r}
+data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+data[, time_since_last_exp := as.integer(day - last_exp_day, units = "days")]
+data <- epikinetics::convert_log2_scale(data)
+priors <- epikinetics::biokinetics_priors()
+plot(priors, data = data)
+```
+
+If you have an instance of the [biokinetics](../reference/biokinetics.html) class, the method `plot_prior_predictive`
+generates this plot for the priors and data given to the model.
+
+```{r}
+data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+priors <- epikinetics::biokinetics_priors()
+mod <- epikinetics::biokinetics$new(priors = priors, data = data)
+mod$plot_prior_predictive()
+```
+
+### Data
+
+If you have an instance of the [biokinetics](../reference/biokinetics.html) class, the method `plot_model_inputs`
+plots the input data used to fit the model, disaggregated by the covariates in the covariate formula.
+
+```{r}
+mod <- epikinetics::biokinetics$new(priors = priors, data = data, covariate_formula = ~0 + infection_history)
+mod$plot_model_inputs()
+```
+
+## Interactive data exploration
+
+To play around with different priors and visualise input data filtered and disaggregated in different ways,
+the function [biokinetics$inspect](reference/biokinetics.html#method-biokinetics-inspect) runs a local RShiny app with interactive plots.
+
+![RShiny demonstration](./shiny.webm)