}}\preformatted{biokinetics$plot_prior_predictive(tmax = 150, n_draws = 2000)}\if{html}{\out{
}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{}}
+\describe{
+\item{\code{tmax}}{Integer. The number of time points in each simulated trajectory. Default 150.}
+
+\item{\code{n_draws}}{Integer. The number of trajectories to simulate. Default 2000.}
+}
+\if{html}{\out{
}}
+}
+\subsection{Returns}{
+A ggplot2 object.
+}
+}
+\if{html}{\out{}}\preformatted{biokinetics$extract_population_parameters(
- n_draws = 2500,
+ n_draws = 2000,
human_readable_covariates = TRUE
)}\if{html}{\out{
}}
}
@@ -123,7 +164,7 @@ Extract fitted population parameters
\subsection{Arguments}{
\if{html}{\out{}}
\describe{
-\item{\code{n_draws}}{Numeric}
+\item{\code{n_draws}}{Integer. Default 2000.}
\item{\code{human_readable_covariates}}{Logical. Default TRUE.}
}
@@ -140,7 +181,7 @@ A data.table
Extract fitted individual parameters
\subsection{Usage}{
\if{html}{\out{
}}\preformatted{biokinetics$extract_individual_parameters(
- n_draws = 2500,
+ n_draws = 2000,
include_variation_params = TRUE,
human_readable_covariates = TRUE
)}\if{html}{\out{
}}
@@ -149,7 +190,7 @@ Extract fitted individual parameters
\subsection{Arguments}{
\if{html}{\out{
}}
\describe{
-\item{\code{n_draws}}{Numeric}
+\item{\code{n_draws}}{Integer. Default 2000.}
\item{\code{include_variation_params}}{Logical}
@@ -170,7 +211,7 @@ Process the model results into a data table of titre values over time.
\if{html}{\out{
}}\preformatted{biokinetics$simulate_population_trajectories(
t_max = 150,
summarise = TRUE,
- n_draws = 2500
+ n_draws = 2000
)}\if{html}{\out{
}}
}
@@ -183,7 +224,7 @@ Process the model results into a data table of titre values over time.
return 0.025, 0.5 and 0.975 quantiles, labelled lo, me and hi, respectively. If FALSE returns values for individual
draws from posterior parameter distributions.}
-\item{\code{n_draws}}{Integer. Maximum number of samples to include. Default 2500.}
+\item{\code{n_draws}}{Integer. Maximum number of samples to include. Default 2000.}
}
\if{html}{\out{
}}
}
@@ -201,13 +242,13 @@ titre_type and a column for each covariate in the hierarchical model. See the da
\subsection{Method \code{population_stationary_points()}}{
Process the stan model results into a data.table.
\subsection{Usage}{
-\if{html}{\out{
}}\preformatted{biokinetics$population_stationary_points(n_draws = 2500)}\if{html}{\out{
}}
+\if{html}{\out{
}}\preformatted{biokinetics$population_stationary_points(n_draws = 2000)}\if{html}{\out{
}}
}
\subsection{Arguments}{
\if{html}{\out{
}}
\describe{
-\item{\code{n_draws}}{Integer. Maximum number of samples to include. Default 2500.}
+\item{\code{n_draws}}{Integer. Maximum number of samples to include. Default 2000.}
}
\if{html}{\out{
}}
}
@@ -226,7 +267,7 @@ computationally expensive and may take a while to run if n_draws is large.
\subsection{Usage}{
\if{html}{\out{
}}\preformatted{biokinetics$simulate_individual_trajectories(
summarise = TRUE,
- n_draws = 2500,
+ n_draws = 2000,
time_shift = 0
)}\if{html}{\out{
}}
}
@@ -238,7 +279,7 @@ computationally expensive and may take a while to run if n_draws is large.
hi values for the population, disaggregated by titre type. If FALSE return the indidivudal trajectories.
Default TRUE.}
-\item{\code{n_draws}}{Integer. Maximum number of samples to draw. Default 2500.}
+\item{\code{n_draws}}{Integer. Maximum number of samples to draw. Default 2000.}
\item{\code{time_shift}}{Integer. Number of days to adjust the exposure day by. Default 0.}
}
diff --git a/man/epikinetics-package.Rd b/man/epikinetics-package.Rd
index 77153e1..29fb876 100644
--- a/man/epikinetics-package.Rd
+++ b/man/epikinetics-package.Rd
@@ -6,7 +6,7 @@
\alias{epikinetics-package}
\title{epikinetics: Biomarker Kinetics Modelling}
\description{
-Fit kinetic curves to biomarker data, using a Bayesian hierarchical model
+Fit kinetic curves to biomarker data, using a Bayesian hierarchical model.
}
\author{
\strong{Maintainer}: Alex Hill \email{alex.hill@gmail.com}
diff --git a/man/plot.biokinetics_population_trajectories.Rd b/man/plot.biokinetics_population_trajectories.Rd
new file mode 100644
index 0000000..333ae60
--- /dev/null
+++ b/man/plot.biokinetics_population_trajectories.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot.biokinetics_population_trajectories}
+\alias{plot.biokinetics_population_trajectories}
+\title{Plot method for "biokinetics_population_trajectories" class}
+\usage{
+\method{plot}{biokinetics_population_trajectories}(x, ..., data = NULL)
+}
+\arguments{
+\item{x}{An object of class "biokinetics_population_trajectories". These are
+generated by running biokinetics$simulate_population_trajectories(). See
+\href{../../epikinetics/html/biokinetics.html#method-biokinetics-simulate_population_trajectories}{\code{biokinetics$simulate_population_trajectories()}}}
+
+\item{\dots}{Further arguments passed to the method.}
+
+\item{data}{Optional data.table containing raw data as provided to the biokinetics model.}
+}
+\description{
+Plot method for "biokinetics_population_trajectories" class
+}
diff --git a/man/plot.biokinetics_priors.Rd b/man/plot.biokinetics_priors.Rd
new file mode 100644
index 0000000..3f116a8
--- /dev/null
+++ b/man/plot.biokinetics_priors.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot.biokinetics_priors}
+\alias{plot.biokinetics_priors}
+\title{Simulate biomarker kinetics predicted by the given biokinetics priors
+and optionally compare to a dataset.}
+\usage{
+\method{plot}{biokinetics_priors}(x, ..., tmax = 150, n_draws = 2000, data = NULL)
+}
+\arguments{
+\item{x}{A named list of type 'biokinetics_priors'.}
+
+\item{\dots}{Further arguments passed to the method.}
+
+\item{tmax}{Integer. The number of time points in each simulated trajectory. Default 150.}
+
+\item{n_draws}{Integer. The number of trajectories to simulate. Default 2000.}
+
+\item{data}{Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.}
+}
+\value{
+A ggplot2 object.
+}
+\description{
+Simulate trajectories by drawing random samples from the given
+priors for each parameter in the biokinetics model.
+}
diff --git a/src/stan/antibody_kinetics_main.stan b/src/stan/antibody_kinetics_main.stan
index 808fc88..61708a2 100644
--- a/src/stan/antibody_kinetics_main.stan
+++ b/src/stan/antibody_kinetics_main.stan
@@ -149,7 +149,7 @@ model {
// Likelihood for observations at lower limit: log2(value/5) = 0
target += normal_lcdf(0 | mu[cens_lo_idx], sigma);
- // Censoring at log2(value/5) = 7, originally value = 2560
+ // Censoring at log2(value/5) = 9, originally value = 2560
target += normal_lccdf(9 | mu[cens_hi_idx], sigma);
// Covariate-level mean priors, parameterised from previous studies
diff --git a/tests/testthat.R b/tests/testthat.R
index 9fc03dd..41aef3c 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -1,4 +1,5 @@
library(testthat)
library(epikinetics)
+library(vdiffr)
test_check("epikinetics")
diff --git a/tests/testthat/_snaps/plots/inputdata-covariates.svg b/tests/testthat/_snaps/plots/inputdata-covariates.svg
new file mode 100644
index 0000000..20b5d78
--- /dev/null
+++ b/tests/testthat/_snaps/plots/inputdata-covariates.svg
@@ -0,0 +1,2506 @@
+
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+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
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+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+Alpha
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Ancestral
+Infection naive
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+
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+9
+time_since_last_exp
+value
+Titre type
+Alpha
+Ancestral
+Delta
+inputdata_covariates
+
+
diff --git a/tests/testthat/_snaps/plots/inputdata.svg b/tests/testthat/_snaps/plots/inputdata.svg
new file mode 100644
index 0000000..e4c509f
--- /dev/null
+++ b/tests/testthat/_snaps/plots/inputdata.svg
@@ -0,0 +1,2408 @@
+
+
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+
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+
+
+
+
+
+
+
+
+Alpha
+
+
+
+
+
+
+
+
+Ancestral
+
+
+
+
+
+
+
+
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+
+
+0
+200
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+6
+9
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+Titre type
+Alpha
+Ancestral
+Delta
+inputdata
+
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diff --git a/tests/testthat/_snaps/plots/multiplecovariates-unsum.svg b/tests/testthat/_snaps/plots/multiplecovariates-unsum.svg
new file mode 100644
index 0000000..b459444
--- /dev/null
+++ b/tests/testthat/_snaps/plots/multiplecovariates-unsum.svg
@@ -0,0 +1,3482 @@
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diff --git a/tests/testthat/_snaps/plots/multiplecovariates.svg b/tests/testthat/_snaps/plots/multiplecovariates.svg
new file mode 100644
index 0000000..61c4096
--- /dev/null
+++ b/tests/testthat/_snaps/plots/multiplecovariates.svg
@@ -0,0 +1,685 @@
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diff --git a/tests/testthat/_snaps/plots/populationtrajectories-data.svg b/tests/testthat/_snaps/plots/populationtrajectories-data.svg
new file mode 100644
index 0000000..25a0e04
--- /dev/null
+++ b/tests/testthat/_snaps/plots/populationtrajectories-data.svg
@@ -0,0 +1,2508 @@
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diff --git a/tests/testthat/_snaps/plots/populationtrajectories-logscale.svg b/tests/testthat/_snaps/plots/populationtrajectories-logscale.svg
new file mode 100644
index 0000000..dceb8df
--- /dev/null
+++ b/tests/testthat/_snaps/plots/populationtrajectories-logscale.svg
@@ -0,0 +1,2512 @@
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+
+
diff --git a/tests/testthat/_snaps/plots/populationtrajectories-unsum.svg b/tests/testthat/_snaps/plots/populationtrajectories-unsum.svg
new file mode 100644
index 0000000..f06ec39
--- /dev/null
+++ b/tests/testthat/_snaps/plots/populationtrajectories-unsum.svg
@@ -0,0 +1,1170 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Ancestral
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Delta
+Infection naive
+
+
+
+
+
+
+
+
+Delta
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Alpha
+Infection naive
+
+
+
+
+
+
+
+
+Alpha
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Ancestral
+Infection naive
+
+
+0
+50
+100
+150
+0
+50
+100
+150
+0
+50
+100
+150
+128
+8192
+524288
+33554432
+128
+8192
+524288
+33554432
+time_since_last_exp
+mu
+populationtrajectories_unsum
+
+
diff --git a/tests/testthat/_snaps/plots/populationtrajectories.svg b/tests/testthat/_snaps/plots/populationtrajectories.svg
new file mode 100644
index 0000000..1aa7f3f
--- /dev/null
+++ b/tests/testthat/_snaps/plots/populationtrajectories.svg
@@ -0,0 +1,245 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Ancestral
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Delta
+Infection naive
+
+
+
+
+
+
+
+
+Delta
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Alpha
+Infection naive
+
+
+
+
+
+
+
+
+Alpha
+Previously infected (Pre-Omicron)
+
+
+
+
+
+
+
+
+Ancestral
+Infection naive
+
+
+0
+50
+100
+150
+0
+50
+100
+150
+0
+50
+100
+150
+128
+8192
+524288
+33554432
+128
+8192
+524288
+33554432
+time_since_last_exp
+me
+Titre type
+Alpha
+Ancestral
+Delta
+populationtrajectories
+
+
diff --git a/tests/testthat/_snaps/plots/priorpredictive.svg b/tests/testthat/_snaps/plots/priorpredictive.svg
new file mode 100644
index 0000000..bd09980
--- /dev/null
+++ b/tests/testthat/_snaps/plots/priorpredictive.svg
@@ -0,0 +1,2315 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+0
+5
+10
+0
+100
+200
+300
+400
+t
+me
+Censored
+FALSE
+TRUE
+priorpredictive
+
+
diff --git a/tests/testthat/test-data.R b/tests/testthat/test-data.R
index 57ebbaf..f1ccbd1 100644
--- a/tests/testthat/test-data.R
+++ b/tests/testthat/test-data.R
@@ -52,7 +52,9 @@ test_that("Natural scale data is converted to log scale for stan", {
dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
mod <- biokinetics$new(data = dat)
stan_data <- mod$get_stan_data()
- expect_equal(stan_data$value, convert_log2_scale(dat, "value")$value, ignore_attr = TRUE)
+ expect_equal(stan_data$value,
+ convert_log2_scale(dat, "value")$value,
+ ignore_attr = TRUE)
})
test_that("Log scale data is passed directly to stan", {
diff --git a/tests/testthat/test-plots.R b/tests/testthat/test-plots.R
new file mode 100644
index 0000000..710ec71
--- /dev/null
+++ b/tests/testthat/test-plots.R
@@ -0,0 +1,124 @@
+test_that("Can plot prior prediction up to tmax", {
+ priors <- biokinetics_priors()
+ plot <- plot(priors, tmax = 100, n_draws = 500)
+ expect_equal(nrow(plot$data), 100)
+ expect_equal(length(plot$layers), 2)
+})
+
+test_that("Can plot prior prediction with data points", {
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ priors <- biokinetics_priors()
+ expect_error(plot(priors, data = data), "Missing required columns: time_since_last_exp")
+ data[, `:=`(time_since_last_exp = as.integer(day - last_exp_day, units = "days"))]
+ plot <- plot(priors, data = data, n_draws = 500)
+ expect_equal(length(plot$layers), 3)
+})
+
+test_that("Can plot prior predictions from model", {
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ priors <- biokinetics_priors(mu_values = c(4.1, 11, 65, 0.2, -0.01, 0.01),
+ sigma_values = c(2.0, 2.0, 3.0, 0.01, 0.01, 0.001))
+
+ mod <- biokinetics$new(priors = priors,
+ data = data)
+ set.seed(1)
+ plot <- mod$plot_prior_predictive(tmax = 400, n_draws = 500)
+ expect_equal(nrow(plot$data), 400)
+ expect_equal(length(plot$layers), 3)
+})
+
+test_that("Prior predictions from model are the same", {
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ priors <- biokinetics_priors(mu_values = c(4.1, 11, 65, 0.2, -0.01, 0.01),
+ sigma_values = c(2.0, 2.0, 3.0, 0.01, 0.01, 0.001))
+
+ mod <- biokinetics$new(priors = priors,
+ data = data)
+ set.seed(1)
+ plot <- mod$plot_prior_predictive(tmax = 400, n_draws = 500)
+ vdiffr::expect_doppelganger("priorpredictive", plot)
+})
+
+test_that("Can plot input data", {
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ mod <- biokinetics$new(data = data)
+ plot <- mod$plot_model_inputs()
+ vdiffr::expect_doppelganger("inputdata", plot)
+
+ mod <- biokinetics$new(data = data, covariate_formula = ~0 + infection_history)
+ plot <- mod$plot_model_inputs()
+ vdiffr::expect_doppelganger("inputdata_covariates", plot)
+})
+
+mock_model <- function(name, package) {
+ list(sample = function(x, ...) readRDS(test_path("testdata", "testdraws.rds")))
+}
+
+mock_model_multiple_covariates <- function(name, package) {
+ list(sample = function(x, ...) readRDS(test_path("testdata", "testdraws_multiplecovariates.rds")))
+}
+
+test_that("Can plot summarised and un-summarised population trajectories", {
+ # note that this is using a pre-fitted model with very few iterations, so the
+ # fits won't look very good
+ local_mocked_bindings(
+ stan_package_model = mock_model, .package = "instantiate"
+ )
+ mod <- biokinetics$new(file_path = system.file("delta_full.rds", package = "epikinetics"),
+ covariate_formula = ~0 + infection_history)
+ mod$fit()
+ trajectories <- mod$simulate_population_trajectories(summarise = TRUE)
+ vdiffr::expect_doppelganger("populationtrajectories", plot(trajectories))
+
+ unsummarised_trajectories <- mod$simulate_population_trajectories(summarise = FALSE)
+ vdiffr::expect_doppelganger("populationtrajectories_unsum", plot(unsummarised_trajectories))
+})
+
+test_that("Can plot summarised and un-summarised population trajectories for multiple covariates", {
+ # note that this is using a pre-fitted model with very few iterations, so the
+ # fits won't look very good
+ local_mocked_bindings(
+ stan_package_model = mock_model_multiple_covariates, .package = "instantiate"
+ )
+ mod <- biokinetics$new(file_path = system.file("delta_full.rds", package = "epikinetics"),
+ covariate_formula = ~0 + infection_history + last_vax_type)
+ mod$fit()
+ trajectories <- mod$simulate_population_trajectories(summarise = TRUE)
+ vdiffr::expect_doppelganger("multiplecovariates", plot(trajectories))
+
+ unsummarised_trajectories <- mod$simulate_population_trajectories(summarise = FALSE)
+ vdiffr::expect_doppelganger("multiplecovariates_unsum", plot(unsummarised_trajectories))
+})
+
+test_that("Can plot population trajectories with data", {
+ local_mocked_bindings(
+ stan_package_model = mock_model, .package = "instantiate"
+ )
+ # note that this is using a pre-fitted model with very few iterations, so the
+ # fits won't look very good
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ mod <- biokinetics$new(data = data, covariate_formula = ~0 + infection_history)
+ fit <- mod$fit()
+ trajectories <- mod$simulate_population_trajectories()
+ plot <- plot(trajectories, data = data)
+ expect_equal(length(plot$scales$scales), 1)
+ vdiffr::expect_doppelganger("populationtrajectories_data", plot)
+})
+
+test_that("Can plot population trajectories with log scale input data", {
+ local_mocked_bindings(
+ stan_package_model = mock_model, .package = "instantiate"
+ )
+ # note that this is using a pre-fitted model with very few iterations, so the
+ # fits won't look very good
+ data <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
+ data <- convert_log2_scale(data)
+ mod <- biokinetics$new(data = data,
+ covariate_formula = ~0 + infection_history,
+ scale = "log")
+ fit <- mod$fit()
+ trajectories <- mod$simulate_population_trajectories()
+ plot <- plot(trajectories, data = data)
+ expect_equal(length(plot$scales$scales), 0)
+ vdiffr::expect_doppelganger("populationtrajectories_logscale", plot)
+})