Merge pull request #10 from seroanalytics/i5

rename scova to biokinetics

.gitignore

@@ -4,3 +4,4 @@ src/stan/**/*.exe
 src/stan/**/*.EXE
 inst/doc
 .idea
+*.png

NAMESPACE

@@ -1,8 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
+export(biokinetics)
+export(biokinetics_priors)
 export(convert_log_scale_inverse)
-export(scova)
-export(scova_priors)
 importFrom(R6,R6Class)
 importFrom(data.table,":=")
 importFrom(data.table,.BY)

R/scova.R → R/biokinetics.R

@@ -1,11 +1,11 @@
-##' @title SARS-CoV-2 Antibody Kinetics Model
+##' @title Biomarker Kinetics Model
 ##'
-##' @description Create an instance of the SARS-CoV-2 antibody model and
-##' fit it to a dataset using Gaussian priors and a given hierarchical model structure.
+##' @description Create an instance of the biomarker kinetics model and
+##' fit it to a dataset.
 ##' @export
 ##' @importFrom R6 R6Class
-scova <- R6::R6Class(
-  "scova",
+biokinetics <- R6::R6Class(
+  "biokinetics",
   cloneable = FALSE,
   private = list(
     priors = NULL,
@@ -152,7 +152,7 @@ scova <- R6::R6Class(
       dt_out <- merge(
         dt_samples_wide, dt_times, by = "t_id", allow.cartesian = TRUE)
 
-      dt_out[, mu := scova_simulate_trajectory(
+      dt_out[, mu := biokinetics_simulate_trajectory(
         t, t0_pop, tp_pop, ts_pop, m1_pop, m2_pop, m3_pop),
                by = c("t", "p", "k", ".draw")]
 
@@ -223,22 +223,22 @@ scova <- R6::R6Class(
   ),
   public = list(
     #' @description Initialise the kinetics model.
-    #' @return An epikinetics::scova object.
+    #' @return An epikinetics::biokinetics object.
     #' @param data Optional data table of model inputs. One of data or file must be provided. See the data vignette
     #' for required columns: \code{vignette("data", package = "epikinetics")}.
     #' @param file_path Optional file path to model inputs in CSV format. One of data or file must be provided.
-    #' @param priors Object of type \link[epikinetics]{scova_priors}. Default scova_priors().
+    #' @param priors Object of type \link[epikinetics]{biokinetics_priors}. Default biokinetics_priors().
     #' @param covariate_formula Formula specifying hierarchical structure of model. Default ~0.
     #' @param preds_sd Standard deviation of predictor coefficients. Default 0.25.
     #' @param time_type One of 'relative' or 'absolute'. Default 'relative'.
-    initialize = function(priors = scova_priors(),
+    initialize = function(priors = biokinetics_priors(),
                           data = NULL,
                           file_path = NULL,
                           covariate_formula = ~0,
                           preds_sd = 0.25,
                           time_type = "relative") {
-      if (!inherits(priors, "scova_priors")) {
-        stop("'priors' must be of type 'scova_priors'")
+      if (!inherits(priors, "biokinetics_priors")) {
+        stop("'priors' must be of type 'biokinetics_priors'")
       }
       private$priors <- priors
       validate_numeric(preds_sd)
@@ -409,11 +409,11 @@ scova <- R6::R6Class(
 
       logger::log_info("Calculating peak and switch titre values")
       dt_peak_switch[, `:=`(
-        mu_0 = scova_simulate_trajectory(
+        mu_0 = biokinetics_simulate_trajectory(
           0, t0_pop, tp_pop, ts_pop, m1_pop, m2_pop, m3_pop),
-        mu_p = scova_simulate_trajectory(
+        mu_p = biokinetics_simulate_trajectory(
           tp_pop, t0_pop, tp_pop, ts_pop, m1_pop, m2_pop, m3_pop),
-        mu_s = scova_simulate_trajectory(
+        mu_s = biokinetics_simulate_trajectory(
           ts_pop, t0_pop, tp_pop, ts_pop, m1_pop, m2_pop, m3_pop)),
                        by = c("p", "k", "draw")]
 
@@ -479,7 +479,7 @@ scova <- R6::R6Class(
       # Running the C++ code to simulate trajectories for each parameter sample
       # for each individual
       logger::log_info("Simulating individual trajectories")
-      dt_params_ind_traj <- scova_simulate_trajectories(dt_params_ind_trim)
+      dt_params_ind_traj <- biokinetics_simulate_trajectories(dt_params_ind_trim)
 
       dt_params_ind_traj <- data.table::setDT(convert_log_scale_inverse_cpp(
           dt_params_ind_traj, vars_to_transform = "mu"))

R/priors.R

@@ -11,22 +11,22 @@ gaussian_priors <- function(names, mu_values, sigma_values) {
   ret
 }
 
-#' @title Construct priors for the SARS-CoV-2 antibody model.
+#' @title Construct priors for the biomarker model.
 #' @export
-#' @description The scova model has 6 parameters: t0, tp, ts, m1, m2, m3 corresponding to critical time points and
+#' @description The biokinetics model has 6 parameters: t0, tp, ts, m1, m2, m3 corresponding to critical time points and
 #' gradients. See the model vignette for details: \code{vignette("model", package = "epikinetics")}. Each of these
 #' parameters has a Gaussian prior, and these can be specified by the user. This function takes means and standard
-#' deviations for each prior and constructs an object of type 'scova_priors' to be passed to the model.
-#' @return A named list of type 'scova_priors'.
+#' deviations for each prior and constructs an object of type 'biokinetics_priors' to be passed to the model.
+#' @return A named list of type 'biokinetics_priors'.
 #' @param mu_values Mean of Gaussian prior for each of t0, tp, ts, m1, m2, m3, in order.
 #' @param sigma_values Standard deviation of Gaussian prior for each of t0, tp, ts, m1, m2, m3, in order.
 #' @examples
-#' priors <- scova_priors(mu_values = c(4.0, 10, 60, 0.25, -0.02, 0),
+#' priors <- biokinetics_priors(mu_values = c(4.0, 10, 60, 0.25, -0.02, 0),
 #' sigma_values = c(2.0, 2.0, 3.0, 0.01, 0.01, 0.01))
-scova_priors <- function(mu_values = c(4.0, 10, 60, 0.25, -0.02, 0),
+biokinetics_priors <- function(mu_values = c(4.0, 10, 60, 0.25, -0.02, 0),
                          sigma_values = c(2.0, 2.0, 3.0, 0.01, 0.01, 0.01)) {
   names <- c("t0", "tp", "ts", "m1", "m2", "m3")
   ret <- gaussian_priors(names, mu_values, sigma_values)
-  class(ret) <- append("scova_priors", class(ret))
+  class(ret) <- append("biokinetics_priors", class(ret))
   ret
 }

R/simulate.R

@@ -1,4 +1,4 @@
-scova_simulate_trajectory <- function(t, t0, tp, ts, m1, m2, m3) {
+biokinetics_simulate_trajectory <- function(t, t0, tp, ts, m1, m2, m3) {
   mu <- t0
   if (t < tp) {
     mu <- mu + m1 * t
@@ -10,6 +10,6 @@ scova_simulate_trajectory <- function(t, t0, tp, ts, m1, m2, m3) {
   max(mu, 0)
 }
 
-scova_simulate_trajectories <- function(dat) {
+biokinetics_simulate_trajectories <- function(dat) {
   data.table::setDT(simulate_trajectories_cpp(dat))
 }

tests/testthat/test-data.R

@@ -8,8 +8,8 @@ local_mocked_bindings(
 
 test_that("Can construct stan data", {
   dt <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
-  mod <- scova$new(data = dt,
-                 priors = scova_priors(),
+  mod <- biokinetics$new(data = dt,
+                 priors = biokinetics_priors(),
                  covariate_formula = ~0 + infection_history,
                  preds_sd = 0.25,
                  time_type = "relative")
@@ -20,12 +20,12 @@ test_that("Can construct stan data", {
 })
 
 test_that("Can initialise file path data", {
-  expect_true(inherits(scova$new(file_path = system.file("delta_full.rds", package = "epikinetics"),
-                               priors = scova_priors()), "scova"))
+  expect_true(inherits(biokinetics$new(file_path = system.file("delta_full.rds", package = "epikinetics"),
+                               priors = biokinetics_priors()), "biokinetics"))
 })
 
 test_that("Can provide data directly", {
   dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
-  expect_true(inherits(scova$new(data = dat,
-                               priors = scova_priors()), "scova"))
+  expect_true(inherits(biokinetics$new(data = dat,
+                               priors = biokinetics_priors()), "biokinetics"))
 })