dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
-mod <- epikinetics::biokinetics$new(data = dat, covariate_formula = ~0 + infection_history)
+mod <- epikinetics::biokinetics$new(data = dat,
+ lower_censoring_limit = 40,
+ covariate_formula = ~0 + infection_history)
delta <- mod$fit(parallel_chains = 4,
iter_warmup = 50,
iter_sampling = 200,
@@ -120,26 +122,18 @@ Figure 2
res <- mod$simulate_population_trajectories()
-#> INFO [2024-11-15 12:08:43] Summarising fits
-#> INFO [2024-11-15 12:08:47] Adjusting by regression coefficients
-#> INFO [2024-11-15 12:08:49] Summarising into quantiles
+#> INFO [2024-11-19 13:53:20] Summarising fits
+#> INFO [2024-11-19 13:53:24] Adjusting by regression coefficients
+#> INFO [2024-11-19 13:53:26] Summarising into quantiles
head(res)
-#> time_since_last_exp me lo hi titre_type
-#> <int> <num> <num> <num> <char>
-#> 1: 0 85.03283 61.79873 113.4266 Ancestral
-#> 2: 1 117.52245 87.99594 151.6198 Ancestral
-#> 3: 2 161.60170 124.69805 203.8376 Ancestral
-#> 4: 3 221.98993 173.94588 277.0610 Ancestral
-#> 5: 4 302.97036 238.14148 381.2043 Ancestral
-#> 6: 5 417.55537 325.74862 544.8846 Ancestral
-#> infection_history
-#> <char>
-#> 1: Infection naive
-#> 2: Infection naive
-#> 3: Infection naive
-#> 4: Infection naive
-#> 5: Infection naive
-#> 6: Infection naive
+#> time_since_last_exp me lo hi titre_type infection_history
+#> <int> <num> <num> <num> <char> <char>
+#> 1: 0 112.6822 84.4386 143.8971 Ancestral Infection naive
+#> 2: 1 142.5849 110.8551 176.8130 Ancestral Infection naive
+#> 3: 2 180.8341 145.1370 219.0680 Ancestral Infection naive
+#> 4: 3 228.8394 188.6540 274.4430 Ancestral Infection naive
+#> 5: 4 290.0701 242.2203 346.9681 Ancestral Infection naive
+#> 6: 5 367.5040 308.0682 451.2246 Ancestral Infection naive
See data for the definition of the returned columns.
Using ggplot2:
Figure 2 scale_colour_manual(values = custom_palette) +
scale_fill_manual(values = custom_palette) +
guides(colour = "none", fill = "none") +
- custom_theme
-#> Warning: Removed 6 rows containing missing values or values outside the scale range
-#> (`geom_line()`).
+ custom_theme
@@ -201,20 +193,20 @@ Figure 4
res <- mod$population_stationary_points(n_draws = 2000)
-#> INFO [2024-11-15 12:08:50] Extracting parameters
-#> INFO [2024-11-15 12:08:51] Adjusting by covariates
-#> INFO [2024-11-15 12:08:51] Calculating peak and switch titre values
-#> INFO [2024-11-15 12:08:51] Recovering covariate names
-#> INFO [2024-11-15 12:08:51] Calculating medians
+#> INFO [2024-11-19 13:53:27] Extracting parameters
+#> INFO [2024-11-19 13:53:27] Adjusting by covariates
+#> INFO [2024-11-19 13:53:27] Calculating peak and switch titre values
+#> INFO [2024-11-19 13:53:28] Recovering covariate names
+#> INFO [2024-11-19 13:53:28] Calculating medians
head(res)
-#> infection_history titre_type mu_p mu_s rel_drop_me mu_p_me mu_s_me
-#> <char> <char> <num> <num> <num> <num> <num>
-#> 1: Infection naive Ancestral 1343.598 246.5053 0.1734832 1315.809 228.7876
-#> 2: Infection naive Ancestral 1218.834 253.4858 0.1734832 1315.809 228.7876
-#> 3: Infection naive Ancestral 1236.706 242.7546 0.1734832 1315.809 228.7876
-#> 4: Infection naive Ancestral 1241.937 218.3664 0.1734832 1315.809 228.7876
-#> 5: Infection naive Ancestral 1339.590 205.4437 0.1734832 1315.809 228.7876
-#> 6: Infection naive Ancestral 1602.548 232.3682 0.1734832 1315.809 228.7876
+#> infection_history titre_type mu_p mu_s rel_drop_me mu_p_me mu_s_me
+#> <char> <char> <num> <num> <num> <num> <num>
+#> 1: Infection naive Ancestral 1232.680 256.3040 0.1926824 1231.001 237.135
+#> 2: Infection naive Ancestral 1203.460 222.4226 0.1926824 1231.001 237.135
+#> 3: Infection naive Ancestral 1237.530 223.8834 0.1926824 1231.001 237.135
+#> 4: Infection naive Ancestral 1287.212 231.6793 0.1926824 1231.001 237.135
+#> 5: Infection naive Ancestral 1326.172 257.9224 0.1926824 1231.001 237.135
+#> 6: Infection naive Ancestral 1353.322 270.6602 0.1926824 1231.001 237.135
The values we’re going to plot are the mean peak titre values
(mu_p) and mean set point titre values (mu_s),
for different titre types and infection histories. See data for a full definition of all the returned
@@ -271,24 +263,24 @@
Figure 5
res <- mod$simulate_individual_trajectories(n_draws = 250)
-#> INFO [2024-11-15 12:08:52] Extracting parameters
-#> INFO [2024-11-15 12:08:56] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:00] Recovering covariate names
-#> INFO [2024-11-15 12:09:00] Calculating exposure days. Adjusting exposures by 0 days
-#> INFO [2024-11-15 12:09:03] Resampling
+#> INFO [2024-11-19 13:53:29] Extracting parameters
+#> INFO [2024-11-19 13:53:32] Simulating individual trajectories
+#> INFO [2024-11-19 13:53:36] Recovering covariate names
+#> INFO [2024-11-19 13:53:37] Calculating exposure days. Adjusting exposures by 0 days
+#> INFO [2024-11-19 13:53:40] Resampling
#> Registered S3 method overwritten by 'mosaic':
#> method from
#> fortify.SpatialPolygonsDataFrame ggplot2
-#> INFO [2024-11-15 12:09:14] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:50] Summarising into population quantiles
head(res)
#> calendar_day titre_type me lo hi time_shift
#> <IDat> <char> <num> <num> <num> <num>
-#> 1: 2021-03-08 Ancestral 1122.561 899.7913 1474.929 0
-#> 2: 2021-03-09 Ancestral 1114.609 882.1468 1450.662 0
-#> 3: 2021-03-10 Ancestral 1150.423 911.1426 1530.973 0
-#> 4: 2021-03-11 Ancestral 1121.402 878.6975 1447.972 0
-#> 5: 2021-03-12 Ancestral 1115.039 851.9412 1443.723 0
-#> 6: 2021-03-13 Ancestral 1150.059 900.9269 1520.740 0
+#> 1: 2021-03-08 Ancestral 1145.101 886.7598 1504.746 0
+#> 2: 2021-03-09 Ancestral 1127.169 881.6767 1452.714 0
+#> 3: 2021-03-10 Ancestral 1159.459 899.3294 1478.496 0
+#> 4: 2021-03-11 Ancestral 1134.535 918.3473 1474.952 0
+#> 5: 2021-03-12 Ancestral 1128.106 884.1373 1525.385 0
+#> 6: 2021-03-13 Ancestral 1164.565 909.3966 1484.574 0
See data for a definition of all the returned
columns. Figure 5 A plots the derived population trajectories. Here we
replicate a portion of the graph from the paper, from the minimum date
@@ -358,72 +350,72 @@
Figure 5 shift <- time_shift_values[index]
mod$simulate_individual_trajectories(n_draws = 50, time_shift = shift)
})
-#> INFO [2024-11-15 12:09:15] Extracting parameters
-#> INFO [2024-11-15 12:09:19] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:19] Recovering covariate names
-#> INFO [2024-11-15 12:09:19] Calculating exposure days. Adjusting exposures by -75 days
-#> INFO [2024-11-15 12:09:20] Resampling
-#> INFO [2024-11-15 12:09:22] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:22] Extracting parameters
-#> INFO [2024-11-15 12:09:26] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:26] Recovering covariate names
-#> INFO [2024-11-15 12:09:26] Calculating exposure days. Adjusting exposures by -60 days
-#> INFO [2024-11-15 12:09:27] Resampling
-#> INFO [2024-11-15 12:09:29] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:29] Extracting parameters
-#> INFO [2024-11-15 12:09:33] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:33] Recovering covariate names
-#> INFO [2024-11-15 12:09:33] Calculating exposure days. Adjusting exposures by -45 days
-#> INFO [2024-11-15 12:09:34] Resampling
-#> INFO [2024-11-15 12:09:36] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:37] Extracting parameters
-#> INFO [2024-11-15 12:09:40] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:41] Recovering covariate names
-#> INFO [2024-11-15 12:09:41] Calculating exposure days. Adjusting exposures by -30 days
-#> INFO [2024-11-15 12:09:41] Resampling
-#> INFO [2024-11-15 12:09:43] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:44] Extracting parameters
-#> INFO [2024-11-15 12:09:47] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:48] Recovering covariate names
-#> INFO [2024-11-15 12:09:48] Calculating exposure days. Adjusting exposures by -15 days
-#> INFO [2024-11-15 12:09:48] Resampling
-#> INFO [2024-11-15 12:09:50] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:51] Extracting parameters
-#> INFO [2024-11-15 12:09:54] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:55] Recovering covariate names
-#> INFO [2024-11-15 12:09:55] Calculating exposure days. Adjusting exposures by 0 days
-#> INFO [2024-11-15 12:09:55] Resampling
-#> INFO [2024-11-15 12:09:57] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:58] Extracting parameters
-#> INFO [2024-11-15 12:10:01] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:02] Recovering covariate names
-#> INFO [2024-11-15 12:10:02] Calculating exposure days. Adjusting exposures by 15 days
-#> INFO [2024-11-15 12:10:02] Resampling
-#> INFO [2024-11-15 12:10:04] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:05] Extracting parameters
-#> INFO [2024-11-15 12:10:08] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:09] Recovering covariate names
-#> INFO [2024-11-15 12:10:09] Calculating exposure days. Adjusting exposures by 30 days
-#> INFO [2024-11-15 12:10:10] Resampling
-#> INFO [2024-11-15 12:10:12] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:12] Extracting parameters
-#> INFO [2024-11-15 12:10:16] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:16] Recovering covariate names
-#> INFO [2024-11-15 12:10:16] Calculating exposure days. Adjusting exposures by 45 days
-#> INFO [2024-11-15 12:10:17] Resampling
-#> INFO [2024-11-15 12:10:19] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:20] Extracting parameters
-#> INFO [2024-11-15 12:10:23] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:23] Recovering covariate names
-#> INFO [2024-11-15 12:10:23] Calculating exposure days. Adjusting exposures by 60 days
-#> INFO [2024-11-15 12:10:24] Resampling
-#> INFO [2024-11-15 12:10:26] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:26] Extracting parameters
-#> INFO [2024-11-15 12:10:30] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:31] Recovering covariate names
-#> INFO [2024-11-15 12:10:31] Calculating exposure days. Adjusting exposures by 75 days
-#> INFO [2024-11-15 12:10:31] Resampling
-#> INFO [2024-11-15 12:10:33] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:51] Extracting parameters
+#> INFO [2024-11-19 13:53:55] Simulating individual trajectories
+#> INFO [2024-11-19 13:53:55] Recovering covariate names
+#> INFO [2024-11-19 13:53:55] Calculating exposure days. Adjusting exposures by -75 days
+#> INFO [2024-11-19 13:53:56] Resampling
+#> INFO [2024-11-19 13:53:58] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:58] Extracting parameters
+#> INFO [2024-11-19 13:54:01] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:02] Recovering covariate names
+#> INFO [2024-11-19 13:54:02] Calculating exposure days. Adjusting exposures by -60 days
+#> INFO [2024-11-19 13:54:03] Resampling
+#> INFO [2024-11-19 13:54:04] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:05] Extracting parameters
+#> INFO [2024-11-19 13:54:08] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:09] Recovering covariate names
+#> INFO [2024-11-19 13:54:09] Calculating exposure days. Adjusting exposures by -45 days
+#> INFO [2024-11-19 13:54:09] Resampling
+#> INFO [2024-11-19 13:54:11] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:12] Extracting parameters
+#> INFO [2024-11-19 13:54:15] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:16] Recovering covariate names
+#> INFO [2024-11-19 13:54:16] Calculating exposure days. Adjusting exposures by -30 days
+#> INFO [2024-11-19 13:54:17] Resampling
+#> INFO [2024-11-19 13:54:19] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:19] Extracting parameters
+#> INFO [2024-11-19 13:54:22] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:23] Recovering covariate names
+#> INFO [2024-11-19 13:54:23] Calculating exposure days. Adjusting exposures by -15 days
+#> INFO [2024-11-19 13:54:23] Resampling
+#> INFO [2024-11-19 13:54:25] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:26] Extracting parameters
+#> INFO [2024-11-19 13:54:29] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:30] Recovering covariate names
+#> INFO [2024-11-19 13:54:30] Calculating exposure days. Adjusting exposures by 0 days
+#> INFO [2024-11-19 13:54:30] Resampling
+#> INFO [2024-11-19 13:54:32] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:33] Extracting parameters
+#> INFO [2024-11-19 13:54:36] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:36] Recovering covariate names
+#> INFO [2024-11-19 13:54:36] Calculating exposure days. Adjusting exposures by 15 days
+#> INFO [2024-11-19 13:54:37] Resampling
+#> INFO [2024-11-19 13:54:39] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:39] Extracting parameters
+#> INFO [2024-11-19 13:54:43] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:43] Recovering covariate names
+#> INFO [2024-11-19 13:54:43] Calculating exposure days. Adjusting exposures by 30 days
+#> INFO [2024-11-19 13:54:44] Resampling
+#> INFO [2024-11-19 13:54:46] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:46] Extracting parameters
+#> INFO [2024-11-19 13:54:49] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:50] Recovering covariate names
+#> INFO [2024-11-19 13:54:50] Calculating exposure days. Adjusting exposures by 45 days
+#> INFO [2024-11-19 13:54:50] Resampling
+#> INFO [2024-11-19 13:54:52] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:53] Extracting parameters
+#> INFO [2024-11-19 13:54:56] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:57] Recovering covariate names
+#> INFO [2024-11-19 13:54:57] Calculating exposure days. Adjusting exposures by 60 days
+#> INFO [2024-11-19 13:54:57] Resampling
+#> INFO [2024-11-19 13:54:59] Summarising into population quantiles
+#> INFO [2024-11-19 13:55:00] Extracting parameters
+#> INFO [2024-11-19 13:55:03] Simulating individual trajectories
+#> INFO [2024-11-19 13:55:04] Recovering covariate names
+#> INFO [2024-11-19 13:55:04] Calculating exposure days. Adjusting exposures by 75 days
+#> INFO [2024-11-19 13:55:04] Resampling
+#> INFO [2024-11-19 13:55:06] Summarising into population quantiles
combined_data <- data.table::data.table(data.table::rbindlist(results_list))
Plotting the median values:
diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-10-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-10-1.png
index ffe6d53..dddfd67 100644
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diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png
index 4a19ef5..4842f38 100644
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diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png
index 6291ace..dee4fd7 100644
Binary files a/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png and b/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png differ
diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png
index 8edee61..d9b2f22 100644
Binary files a/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png and b/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png differ
diff --git a/articles/data.html b/articles/data.html
index dc54022..cf9e58e 100644
--- a/articles/data.html
+++ b/articles/data.html
@@ -95,55 +95,41 @@ Input data
name
type
description
-required
pid
numeric or character
Unique identifier to identify a person across observations
-T
day
integer or date
The day of the observation. Can be a date or an integer representing
a relative day of study
-T
last_exp_day
integer or date
The most recent day on which the person was exposed. Must be of the
same type as the ‘day’ column
-T
titre_type
character
Name of the titre or biomarker
-T
value
numeric
Titre value
-T
-
-
-censored
--1, 0 or 1
-Optional column. Whether this observation should be treated as
-censored: -1 for lower, 1 for upper, 0 for none.
-F
@@ -166,14 +152,14 @@ Example
dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
head(dat)
-#> pid day last_exp_day titre_type value censored infection_history
-#> <int> <IDat> <IDat> <char> <num> <int> <char>
-#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 0 Infection naive
-#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 0 Infection naive
-#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 0 Infection naive
-#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 -1 Infection naive
-#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 0 Infection naive
-#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 0 Infection naive
+#> pid day last_exp_day titre_type value infection_history
+#> <int> <IDat> <IDat> <char> <num> <char>
+#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 Infection naive
+#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 Infection naive
+#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 Infection naive
+#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 Infection naive
+#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 Infection naive
+#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 Infection naive
#> last_vax_type exp_num
#> <char> <int>
#> 1: BNT162b2 2
@@ -185,7 +171,7 @@ Example
-Ouput data
+Output data
After fitting a model, a CmdStanMCMC
object is returned. This means that users who are already familiar with
diff --git a/pkgdown.yml b/pkgdown.yml
index e2ec282..42078ed 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,4 +5,4 @@ articles:
biokinetics: biokinetics.html
data: data.html
model: model.html
-last_built: 2024-11-15T12:04Z
+last_built: 2024-11-19T13:48Z
diff --git a/reference/biokinetics.html b/reference/biokinetics.html
index 456b7c4..ef689b7 100644
--- a/reference/biokinetics.html
+++ b/reference/biokinetics.html
@@ -89,7 +89,11 @@
Usage
file_path = NULL,
covariate_formula = ~0,
preds_sd = 0.25,
- scale = "natural"
+ scale = "natural",
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL,
+ strict_upper_limit = TRUE,
+ strict_lower_limit = TRUE
)
@@ -122,6 +126,26 @@ Arguments
@@ -158,12 +182,20 @@ Returns
Method plot_model_inputs()
Plot model input data with a smoothing function. Note that
-this plot is on a log scale, regardless of whether data was provided on a
-log or a natural scale.
+this plot is of the data as provided to the Stan model so is on a log scale,
+regardless of whether data was provided on a log or a natural scale.
+
-Arguments
+Arguments
...Named arguments to the sample() method of CmdStan model.
objects: https://mc-stan.org/cmdstanr/reference/model-method-sample.html
Usage
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
n_drawsInteger. Maximum number of samples to include. Default 2000.
Usage
-Arguments
+Arguments
summariseBoolean. If TRUE, average the individual trajectories to get lo, me and
hi values for the population, disaggregated by titre type. If FALSE return the indidivudal trajectories.
diff --git a/reference/convert_log2_scale.html b/reference/convert_log2_scale.html
new file mode 100644
index 0000000..c1277df
--- /dev/null
+++ b/reference/convert_log2_scale.html
@@ -0,0 +1,113 @@
+
+
Base 2 log scale conversion — convert_log2_scale • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Base 2 log scale conversion
+
+ convert_log2_scale.Rd
+
+
+
+ Natural scale data is converted to a base 2 log scale before model fitting.
+This function does not modify the provided data.table in-place, but returns a transformed copy.
+
+
+
+ convert_log2_scale(dt_in, smallest_value, vars_to_transform = "value")
+
+
+
+ Arguments
+
+
+
+
+ Value
+ A data.table, identical to the input data but with specified columns transformed.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/reference/convert_log2_scale_inverse.html b/reference/convert_log2_scale_inverse.html
index d1530d5..a7c870c 100644
--- a/reference/convert_log2_scale_inverse.html
+++ b/reference/convert_log2_scale_inverse.html
@@ -64,7 +64,7 @@ Invert base 2 log scale conversion
- convert_log2_scale_inverse(dt_in, vars_to_transform)
+ convert_log2_scale_inverse(dt_in, vars_to_transform, smallest_value)
@@ -78,6 +78,10 @@ Arguments
- vars_to_transform
Names of columns to apply the transformation to.
- smallest_value
+The lowest value in the original dataset.
Value
diff --git a/reference/index.html b/reference/index.html
index f1fa73b..22d57aa 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -69,6 +69,10 @@ All functions biokinetics_priors()
Construct priors for the biomarker model.
+
+
+ Base 2 log scale conversion
@@ -81,6 +85,10 @@ All functions plot(<biokinetics_priors>)
Simulate biomarker kinetics predicted by the given biokinetics priors and optionally compare to a dataset.
+
+
+ Plot serological data
@@ -89,6 +97,14 @@ Arguments
- data
Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
Value
diff --git a/reference/plot_sero_data.html b/reference/plot_sero_data.html
new file mode 100644
index 0000000..fa47ef8
--- /dev/null
+++ b/reference/plot_sero_data.html
@@ -0,0 +1,126 @@
+
+Plot serological data — plot_sero_data • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Plot serological data
+
+ plot_sero_data.Rd
+
+
+
+ Plot serological data in the format provided to the biokinetics
+model, with a smoothing function fitted.
+
+
+
+ plot_sero_data(
+ data,
+ tmax = 150,
+ covariates = character(0),
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL
+)
+
+
+
+ Arguments
+
+
+- data
+A data.table with required columns time_since_last_exp, value and titre_type.
- tmax
+Integer. The number of time points in each simulated trajectory. Default 150.
- covariates
+Optional vector of covariate names to facet by (these must correspond to columns in the data.table)
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
+
+ Value
+ A ggplot2 object.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sitemap.xml b/sitemap.xml
index f2c48cf..d269dca 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -10,10 +10,12 @@
/reference/add_exposure_data.html /reference/biokinetics.html /reference/biokinetics_priors.html /reference/convert_log2_scale.html /reference/convert_log2_scale_inverse.html /reference/epikinetics-package.html /reference/index.html /reference/plot.biokinetics_population_trajectories.html /reference/plot.biokinetics_priors.html /reference/plot_sero_data.html
Figure 4
res <- mod$population_stationary_points(n_draws = 2000)
-#> INFO [2024-11-15 12:08:50] Extracting parameters
-#> INFO [2024-11-15 12:08:51] Adjusting by covariates
-#> INFO [2024-11-15 12:08:51] Calculating peak and switch titre values
-#> INFO [2024-11-15 12:08:51] Recovering covariate names
-#> INFO [2024-11-15 12:08:51] Calculating medians
+#> INFO [2024-11-19 13:53:27] Extracting parameters
+#> INFO [2024-11-19 13:53:27] Adjusting by covariates
+#> INFO [2024-11-19 13:53:27] Calculating peak and switch titre values
+#> INFO [2024-11-19 13:53:28] Recovering covariate names
+#> INFO [2024-11-19 13:53:28] Calculating medians
head(res)
-#> infection_history titre_type mu_p mu_s rel_drop_me mu_p_me mu_s_me
-#> <char> <char> <num> <num> <num> <num> <num>
-#> 1: Infection naive Ancestral 1343.598 246.5053 0.1734832 1315.809 228.7876
-#> 2: Infection naive Ancestral 1218.834 253.4858 0.1734832 1315.809 228.7876
-#> 3: Infection naive Ancestral 1236.706 242.7546 0.1734832 1315.809 228.7876
-#> 4: Infection naive Ancestral 1241.937 218.3664 0.1734832 1315.809 228.7876
-#> 5: Infection naive Ancestral 1339.590 205.4437 0.1734832 1315.809 228.7876
-#> 6: Infection naive Ancestral 1602.548 232.3682 0.1734832 1315.809 228.7876
+#> infection_history titre_type mu_p mu_s rel_drop_me mu_p_me mu_s_me
+#> <char> <char> <num> <num> <num> <num> <num>
+#> 1: Infection naive Ancestral 1232.680 256.3040 0.1926824 1231.001 237.135
+#> 2: Infection naive Ancestral 1203.460 222.4226 0.1926824 1231.001 237.135
+#> 3: Infection naive Ancestral 1237.530 223.8834 0.1926824 1231.001 237.135
+#> 4: Infection naive Ancestral 1287.212 231.6793 0.1926824 1231.001 237.135
+#> 5: Infection naive Ancestral 1326.172 257.9224 0.1926824 1231.001 237.135
+#> 6: Infection naive Ancestral 1353.322 270.6602 0.1926824 1231.001 237.135
res <- mod$population_stationary_points(n_draws = 2000)
-#> INFO [2024-11-15 12:08:50] Extracting parameters
-#> INFO [2024-11-15 12:08:51] Adjusting by covariates
-#> INFO [2024-11-15 12:08:51] Calculating peak and switch titre values
-#> INFO [2024-11-15 12:08:51] Recovering covariate names
-#> INFO [2024-11-15 12:08:51] Calculating medians
+#> INFO [2024-11-19 13:53:27] Extracting parameters
+#> INFO [2024-11-19 13:53:27] Adjusting by covariates
+#> INFO [2024-11-19 13:53:27] Calculating peak and switch titre values
+#> INFO [2024-11-19 13:53:28] Recovering covariate names
+#> INFO [2024-11-19 13:53:28] Calculating medians
head(res)
-#> infection_history titre_type mu_p mu_s rel_drop_me mu_p_me mu_s_me
-#> <char> <char> <num> <num> <num> <num> <num>
-#> 1: Infection naive Ancestral 1343.598 246.5053 0.1734832 1315.809 228.7876
-#> 2: Infection naive Ancestral 1218.834 253.4858 0.1734832 1315.809 228.7876
-#> 3: Infection naive Ancestral 1236.706 242.7546 0.1734832 1315.809 228.7876
-#> 4: Infection naive Ancestral 1241.937 218.3664 0.1734832 1315.809 228.7876
-#> 5: Infection naive Ancestral 1339.590 205.4437 0.1734832 1315.809 228.7876
-#> 6: Infection naive Ancestral 1602.548 232.3682 0.1734832 1315.809 228.7876mu_p) and mean set point titre values (mu_s),
for different titre types and infection histories. See data for a full definition of all the returned
@@ -271,24 +263,24 @@
res <- mod$simulate_individual_trajectories(n_draws = 250)
-#> INFO [2024-11-15 12:08:52] Extracting parameters
-#> INFO [2024-11-15 12:08:56] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:00] Recovering covariate names
-#> INFO [2024-11-15 12:09:00] Calculating exposure days. Adjusting exposures by 0 days
-#> INFO [2024-11-15 12:09:03] Resampling
+#> INFO [2024-11-19 13:53:29] Extracting parameters
+#> INFO [2024-11-19 13:53:32] Simulating individual trajectories
+#> INFO [2024-11-19 13:53:36] Recovering covariate names
+#> INFO [2024-11-19 13:53:37] Calculating exposure days. Adjusting exposures by 0 days
+#> INFO [2024-11-19 13:53:40] Resampling
#> Registered S3 method overwritten by 'mosaic':
#> method from
#> fortify.SpatialPolygonsDataFrame ggplot2
-#> INFO [2024-11-15 12:09:14] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:50] Summarising into population quantiles
head(res)
#> calendar_day titre_type me lo hi time_shift
#> <IDat> <char> <num> <num> <num> <num>
-#> 1: 2021-03-08 Ancestral 1122.561 899.7913 1474.929 0
-#> 2: 2021-03-09 Ancestral 1114.609 882.1468 1450.662 0
-#> 3: 2021-03-10 Ancestral 1150.423 911.1426 1530.973 0
-#> 4: 2021-03-11 Ancestral 1121.402 878.6975 1447.972 0
-#> 5: 2021-03-12 Ancestral 1115.039 851.9412 1443.723 0
-#> 6: 2021-03-13 Ancestral 1150.059 900.9269 1520.740 0See data for a definition of all the returned columns. Figure 5 A plots the derived population trajectories. Here we replicate a portion of the graph from the paper, from the minimum date @@ -358,72 +350,72 @@
Figure 5 shift <- time_shift_values[index]
mod$simulate_individual_trajectories(n_draws = 50, time_shift = shift)
})
-#> INFO [2024-11-15 12:09:15] Extracting parameters
-#> INFO [2024-11-15 12:09:19] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:19] Recovering covariate names
-#> INFO [2024-11-15 12:09:19] Calculating exposure days. Adjusting exposures by -75 days
-#> INFO [2024-11-15 12:09:20] Resampling
-#> INFO [2024-11-15 12:09:22] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:22] Extracting parameters
-#> INFO [2024-11-15 12:09:26] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:26] Recovering covariate names
-#> INFO [2024-11-15 12:09:26] Calculating exposure days. Adjusting exposures by -60 days
-#> INFO [2024-11-15 12:09:27] Resampling
-#> INFO [2024-11-15 12:09:29] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:29] Extracting parameters
-#> INFO [2024-11-15 12:09:33] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:33] Recovering covariate names
-#> INFO [2024-11-15 12:09:33] Calculating exposure days. Adjusting exposures by -45 days
-#> INFO [2024-11-15 12:09:34] Resampling
-#> INFO [2024-11-15 12:09:36] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:37] Extracting parameters
-#> INFO [2024-11-15 12:09:40] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:41] Recovering covariate names
-#> INFO [2024-11-15 12:09:41] Calculating exposure days. Adjusting exposures by -30 days
-#> INFO [2024-11-15 12:09:41] Resampling
-#> INFO [2024-11-15 12:09:43] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:44] Extracting parameters
-#> INFO [2024-11-15 12:09:47] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:48] Recovering covariate names
-#> INFO [2024-11-15 12:09:48] Calculating exposure days. Adjusting exposures by -15 days
-#> INFO [2024-11-15 12:09:48] Resampling
-#> INFO [2024-11-15 12:09:50] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:51] Extracting parameters
-#> INFO [2024-11-15 12:09:54] Simulating individual trajectories
-#> INFO [2024-11-15 12:09:55] Recovering covariate names
-#> INFO [2024-11-15 12:09:55] Calculating exposure days. Adjusting exposures by 0 days
-#> INFO [2024-11-15 12:09:55] Resampling
-#> INFO [2024-11-15 12:09:57] Summarising into population quantiles
-#> INFO [2024-11-15 12:09:58] Extracting parameters
-#> INFO [2024-11-15 12:10:01] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:02] Recovering covariate names
-#> INFO [2024-11-15 12:10:02] Calculating exposure days. Adjusting exposures by 15 days
-#> INFO [2024-11-15 12:10:02] Resampling
-#> INFO [2024-11-15 12:10:04] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:05] Extracting parameters
-#> INFO [2024-11-15 12:10:08] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:09] Recovering covariate names
-#> INFO [2024-11-15 12:10:09] Calculating exposure days. Adjusting exposures by 30 days
-#> INFO [2024-11-15 12:10:10] Resampling
-#> INFO [2024-11-15 12:10:12] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:12] Extracting parameters
-#> INFO [2024-11-15 12:10:16] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:16] Recovering covariate names
-#> INFO [2024-11-15 12:10:16] Calculating exposure days. Adjusting exposures by 45 days
-#> INFO [2024-11-15 12:10:17] Resampling
-#> INFO [2024-11-15 12:10:19] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:20] Extracting parameters
-#> INFO [2024-11-15 12:10:23] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:23] Recovering covariate names
-#> INFO [2024-11-15 12:10:23] Calculating exposure days. Adjusting exposures by 60 days
-#> INFO [2024-11-15 12:10:24] Resampling
-#> INFO [2024-11-15 12:10:26] Summarising into population quantiles
-#> INFO [2024-11-15 12:10:26] Extracting parameters
-#> INFO [2024-11-15 12:10:30] Simulating individual trajectories
-#> INFO [2024-11-15 12:10:31] Recovering covariate names
-#> INFO [2024-11-15 12:10:31] Calculating exposure days. Adjusting exposures by 75 days
-#> INFO [2024-11-15 12:10:31] Resampling
-#> INFO [2024-11-15 12:10:33] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:51] Extracting parameters
+#> INFO [2024-11-19 13:53:55] Simulating individual trajectories
+#> INFO [2024-11-19 13:53:55] Recovering covariate names
+#> INFO [2024-11-19 13:53:55] Calculating exposure days. Adjusting exposures by -75 days
+#> INFO [2024-11-19 13:53:56] Resampling
+#> INFO [2024-11-19 13:53:58] Summarising into population quantiles
+#> INFO [2024-11-19 13:53:58] Extracting parameters
+#> INFO [2024-11-19 13:54:01] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:02] Recovering covariate names
+#> INFO [2024-11-19 13:54:02] Calculating exposure days. Adjusting exposures by -60 days
+#> INFO [2024-11-19 13:54:03] Resampling
+#> INFO [2024-11-19 13:54:04] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:05] Extracting parameters
+#> INFO [2024-11-19 13:54:08] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:09] Recovering covariate names
+#> INFO [2024-11-19 13:54:09] Calculating exposure days. Adjusting exposures by -45 days
+#> INFO [2024-11-19 13:54:09] Resampling
+#> INFO [2024-11-19 13:54:11] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:12] Extracting parameters
+#> INFO [2024-11-19 13:54:15] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:16] Recovering covariate names
+#> INFO [2024-11-19 13:54:16] Calculating exposure days. Adjusting exposures by -30 days
+#> INFO [2024-11-19 13:54:17] Resampling
+#> INFO [2024-11-19 13:54:19] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:19] Extracting parameters
+#> INFO [2024-11-19 13:54:22] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:23] Recovering covariate names
+#> INFO [2024-11-19 13:54:23] Calculating exposure days. Adjusting exposures by -15 days
+#> INFO [2024-11-19 13:54:23] Resampling
+#> INFO [2024-11-19 13:54:25] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:26] Extracting parameters
+#> INFO [2024-11-19 13:54:29] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:30] Recovering covariate names
+#> INFO [2024-11-19 13:54:30] Calculating exposure days. Adjusting exposures by 0 days
+#> INFO [2024-11-19 13:54:30] Resampling
+#> INFO [2024-11-19 13:54:32] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:33] Extracting parameters
+#> INFO [2024-11-19 13:54:36] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:36] Recovering covariate names
+#> INFO [2024-11-19 13:54:36] Calculating exposure days. Adjusting exposures by 15 days
+#> INFO [2024-11-19 13:54:37] Resampling
+#> INFO [2024-11-19 13:54:39] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:39] Extracting parameters
+#> INFO [2024-11-19 13:54:43] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:43] Recovering covariate names
+#> INFO [2024-11-19 13:54:43] Calculating exposure days. Adjusting exposures by 30 days
+#> INFO [2024-11-19 13:54:44] Resampling
+#> INFO [2024-11-19 13:54:46] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:46] Extracting parameters
+#> INFO [2024-11-19 13:54:49] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:50] Recovering covariate names
+#> INFO [2024-11-19 13:54:50] Calculating exposure days. Adjusting exposures by 45 days
+#> INFO [2024-11-19 13:54:50] Resampling
+#> INFO [2024-11-19 13:54:52] Summarising into population quantiles
+#> INFO [2024-11-19 13:54:53] Extracting parameters
+#> INFO [2024-11-19 13:54:56] Simulating individual trajectories
+#> INFO [2024-11-19 13:54:57] Recovering covariate names
+#> INFO [2024-11-19 13:54:57] Calculating exposure days. Adjusting exposures by 60 days
+#> INFO [2024-11-19 13:54:57] Resampling
+#> INFO [2024-11-19 13:54:59] Summarising into population quantiles
+#> INFO [2024-11-19 13:55:00] Extracting parameters
+#> INFO [2024-11-19 13:55:03] Simulating individual trajectories
+#> INFO [2024-11-19 13:55:04] Recovering covariate names
+#> INFO [2024-11-19 13:55:04] Calculating exposure days. Adjusting exposures by 75 days
+#> INFO [2024-11-19 13:55:04] Resampling
+#> INFO [2024-11-19 13:55:06] Summarising into population quantiles
combined_data <- data.table::data.table(data.table::rbindlist(results_list))
Plotting the median values:
diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-10-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-10-1.png
index ffe6d53..dddfd67 100644
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diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png
index 4a19ef5..4842f38 100644
Binary files a/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png and b/articles/biokinetics_files/figure-html/unnamed-chunk-4-1.png differ
diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png
index 6291ace..dee4fd7 100644
Binary files a/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png and b/articles/biokinetics_files/figure-html/unnamed-chunk-6-1.png differ
diff --git a/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png b/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png
index 8edee61..d9b2f22 100644
Binary files a/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png and b/articles/biokinetics_files/figure-html/unnamed-chunk-8-1.png differ
diff --git a/articles/data.html b/articles/data.html
index dc54022..cf9e58e 100644
--- a/articles/data.html
+++ b/articles/data.html
@@ -95,55 +95,41 @@ Input data
name
type
description
-required
pid
numeric or character
Unique identifier to identify a person across observations
-T
day
integer or date
The day of the observation. Can be a date or an integer representing
a relative day of study
-T
last_exp_day
integer or date
The most recent day on which the person was exposed. Must be of the
same type as the ‘day’ column
-T
titre_type
character
Name of the titre or biomarker
-T
value
numeric
Titre value
-T
-
-
-censored
--1, 0 or 1
-Optional column. Whether this observation should be treated as
-censored: -1 for lower, 1 for upper, 0 for none.
-F
@@ -166,14 +152,14 @@ Example
dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
head(dat)
-#> pid day last_exp_day titre_type value censored infection_history
-#> <int> <IDat> <IDat> <char> <num> <int> <char>
-#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 0 Infection naive
-#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 0 Infection naive
-#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 0 Infection naive
-#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 -1 Infection naive
-#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 0 Infection naive
-#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 0 Infection naive
+#> pid day last_exp_day titre_type value infection_history
+#> <int> <IDat> <IDat> <char> <num> <char>
+#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 Infection naive
+#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 Infection naive
+#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 Infection naive
+#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 Infection naive
+#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 Infection naive
+#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 Infection naive
#> last_vax_type exp_num
#> <char> <int>
#> 1: BNT162b2 2
@@ -185,7 +171,7 @@ Example
-Ouput data
+Output data
After fitting a model, a CmdStanMCMC
object is returned. This means that users who are already familiar with
diff --git a/pkgdown.yml b/pkgdown.yml
index e2ec282..42078ed 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,4 +5,4 @@ articles:
biokinetics: biokinetics.html
data: data.html
model: model.html
-last_built: 2024-11-15T12:04Z
+last_built: 2024-11-19T13:48Z
diff --git a/reference/biokinetics.html b/reference/biokinetics.html
index 456b7c4..ef689b7 100644
--- a/reference/biokinetics.html
+++ b/reference/biokinetics.html
@@ -89,7 +89,11 @@
Usage
file_path = NULL,
covariate_formula = ~0,
preds_sd = 0.25,
- scale = "natural"
+ scale = "natural",
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL,
+ strict_upper_limit = TRUE,
+ strict_lower_limit = TRUE
)
@@ -122,6 +126,26 @@ Arguments
@@ -158,12 +182,20 @@ Returns
Method plot_model_inputs()
Plot model input data with a smoothing function. Note that
-this plot is on a log scale, regardless of whether data was provided on a
-log or a natural scale.
+this plot is of the data as provided to the Stan model so is on a log scale,
+regardless of whether data was provided on a log or a natural scale.
+
-Arguments
+Arguments
...Named arguments to the sample() method of CmdStan model.
objects: https://mc-stan.org/cmdstanr/reference/model-method-sample.html
Usage
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
n_drawsInteger. Maximum number of samples to include. Default 2000.
Usage
-Arguments
+Arguments
summariseBoolean. If TRUE, average the individual trajectories to get lo, me and
hi values for the population, disaggregated by titre type. If FALSE return the indidivudal trajectories.
diff --git a/reference/convert_log2_scale.html b/reference/convert_log2_scale.html
new file mode 100644
index 0000000..c1277df
--- /dev/null
+++ b/reference/convert_log2_scale.html
@@ -0,0 +1,113 @@
+
+
Base 2 log scale conversion — convert_log2_scale • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Base 2 log scale conversion
+
+ convert_log2_scale.Rd
+
+
+
+ Natural scale data is converted to a base 2 log scale before model fitting.
+This function does not modify the provided data.table in-place, but returns a transformed copy.
+
+
+
+ convert_log2_scale(dt_in, smallest_value, vars_to_transform = "value")
+
+
+
+ Arguments
+
+
+
+
+ Value
+ A data.table, identical to the input data but with specified columns transformed.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/reference/convert_log2_scale_inverse.html b/reference/convert_log2_scale_inverse.html
index d1530d5..a7c870c 100644
--- a/reference/convert_log2_scale_inverse.html
+++ b/reference/convert_log2_scale_inverse.html
@@ -64,7 +64,7 @@ Invert base 2 log scale conversion
- convert_log2_scale_inverse(dt_in, vars_to_transform)
+ convert_log2_scale_inverse(dt_in, vars_to_transform, smallest_value)
@@ -78,6 +78,10 @@ Arguments
- vars_to_transform
Names of columns to apply the transformation to.
- smallest_value
+The lowest value in the original dataset.
Value
diff --git a/reference/index.html b/reference/index.html
index f1fa73b..22d57aa 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -69,6 +69,10 @@ All functions biokinetics_priors()
Construct priors for the biomarker model.
+
+
+ Base 2 log scale conversion
@@ -81,6 +85,10 @@ All functions plot(<biokinetics_priors>)
Simulate biomarker kinetics predicted by the given biokinetics priors and optionally compare to a dataset.
+
+
+ Plot serological data
@@ -89,6 +97,14 @@ Arguments
- data
Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
Value
diff --git a/reference/plot_sero_data.html b/reference/plot_sero_data.html
new file mode 100644
index 0000000..fa47ef8
--- /dev/null
+++ b/reference/plot_sero_data.html
@@ -0,0 +1,126 @@
+
+Plot serological data — plot_sero_data • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Plot serological data
+
+ plot_sero_data.Rd
+
+
+
+ Plot serological data in the format provided to the biokinetics
+model, with a smoothing function fitted.
+
+
+
+ plot_sero_data(
+ data,
+ tmax = 150,
+ covariates = character(0),
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL
+)
+
+
+
+ Arguments
+
+
+- data
+A data.table with required columns time_since_last_exp, value and titre_type.
- tmax
+Integer. The number of time points in each simulated trajectory. Default 150.
- covariates
+Optional vector of covariate names to facet by (these must correspond to columns in the data.table)
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
+
+ Value
+ A ggplot2 object.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sitemap.xml b/sitemap.xml
index f2c48cf..d269dca 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -10,10 +10,12 @@
/reference/add_exposure_data.html /reference/biokinetics.html /reference/biokinetics_priors.html /reference/convert_log2_scale.html /reference/convert_log2_scale_inverse.html /reference/epikinetics-package.html /reference/index.html /reference/plot.biokinetics_population_trajectories.html /reference/plot.biokinetics_priors.html /reference/plot_sero_data.html
Example
dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
head(dat)
-#> pid day last_exp_day titre_type value censored infection_history
-#> <int> <IDat> <IDat> <char> <num> <int> <char>
-#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 0 Infection naive
-#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 0 Infection naive
-#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 0 Infection naive
-#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 -1 Infection naive
-#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 0 Infection naive
-#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 0 Infection naive
+#> pid day last_exp_day titre_type value infection_history
+#> <int> <IDat> <IDat> <char> <num> <char>
+#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 Infection naive
+#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 Infection naive
+#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 Infection naive
+#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 Infection naive
+#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 Infection naive
+#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 Infection naive
#> last_vax_type exp_num
#> <char> <int>
#> 1: BNT162b2 2
@@ -185,7 +171,7 @@ Example
-Ouput data
+Output data
After fitting a model, a CmdStanMCMC
object is returned. This means that users who are already familiar with
diff --git a/pkgdown.yml b/pkgdown.yml
index e2ec282..42078ed 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,4 +5,4 @@ articles:
biokinetics: biokinetics.html
data: data.html
model: model.html
-last_built: 2024-11-15T12:04Z
+last_built: 2024-11-19T13:48Z
diff --git a/reference/biokinetics.html b/reference/biokinetics.html
index 456b7c4..ef689b7 100644
--- a/reference/biokinetics.html
+++ b/reference/biokinetics.html
@@ -89,7 +89,11 @@
Usage
file_path = NULL,
covariate_formula = ~0,
preds_sd = 0.25,
- scale = "natural"
+ scale = "natural",
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL,
+ strict_upper_limit = TRUE,
+ strict_lower_limit = TRUE
)
@@ -122,6 +126,26 @@ Arguments
@@ -158,12 +182,20 @@ Returns
Method plot_model_inputs()
Plot model input data with a smoothing function. Note that
-this plot is on a log scale, regardless of whether data was provided on a
-log or a natural scale.
+this plot is of the data as provided to the Stan model so is on a log scale,
+regardless of whether data was provided on a log or a natural scale.
+
-Arguments
+Arguments
...Named arguments to the sample() method of CmdStan model.
objects: https://mc-stan.org/cmdstanr/reference/model-method-sample.html
Usage
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
n_drawsInteger. Maximum number of samples to include. Default 2000.
Usage
-Arguments
+Arguments
summariseBoolean. If TRUE, average the individual trajectories to get lo, me and
hi values for the population, disaggregated by titre type. If FALSE return the indidivudal trajectories.
diff --git a/reference/convert_log2_scale.html b/reference/convert_log2_scale.html
new file mode 100644
index 0000000..c1277df
--- /dev/null
+++ b/reference/convert_log2_scale.html
@@ -0,0 +1,113 @@
+
+
Base 2 log scale conversion — convert_log2_scale • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Base 2 log scale conversion
+
+ convert_log2_scale.Rd
+
+
+
+ Natural scale data is converted to a base 2 log scale before model fitting.
+This function does not modify the provided data.table in-place, but returns a transformed copy.
+
+
+
+ convert_log2_scale(dt_in, smallest_value, vars_to_transform = "value")
+
+
+
+ Arguments
+
+
+
+
+ Value
+ A data.table, identical to the input data but with specified columns transformed.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/reference/convert_log2_scale_inverse.html b/reference/convert_log2_scale_inverse.html
index d1530d5..a7c870c 100644
--- a/reference/convert_log2_scale_inverse.html
+++ b/reference/convert_log2_scale_inverse.html
@@ -64,7 +64,7 @@ Invert base 2 log scale conversion
- convert_log2_scale_inverse(dt_in, vars_to_transform)
+ convert_log2_scale_inverse(dt_in, vars_to_transform, smallest_value)
@@ -78,6 +78,10 @@ Arguments
- vars_to_transform
Names of columns to apply the transformation to.
- smallest_value
+The lowest value in the original dataset.
Value
diff --git a/reference/index.html b/reference/index.html
index f1fa73b..22d57aa 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -69,6 +69,10 @@ All functions biokinetics_priors()
Construct priors for the biomarker model.
+
+
+ Base 2 log scale conversion
@@ -81,6 +85,10 @@ All functions plot(<biokinetics_priors>)
Simulate biomarker kinetics predicted by the given biokinetics priors and optionally compare to a dataset.
+
+
+ Plot serological data
@@ -89,6 +97,14 @@ Arguments
- data
Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
Value
diff --git a/reference/plot_sero_data.html b/reference/plot_sero_data.html
new file mode 100644
index 0000000..fa47ef8
--- /dev/null
+++ b/reference/plot_sero_data.html
@@ -0,0 +1,126 @@
+
+Plot serological data — plot_sero_data • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Plot serological data
+
+ plot_sero_data.Rd
+
+
+
+ Plot serological data in the format provided to the biokinetics
+model, with a smoothing function fitted.
+
+
+
+ plot_sero_data(
+ data,
+ tmax = 150,
+ covariates = character(0),
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL
+)
+
+
+
+ Arguments
+
+
+- data
+A data.table with required columns time_since_last_exp, value and titre_type.
- tmax
+Integer. The number of time points in each simulated trajectory. Default 150.
- covariates
+Optional vector of covariate names to facet by (these must correspond to columns in the data.table)
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
+
+ Value
+ A ggplot2 object.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sitemap.xml b/sitemap.xml
index f2c48cf..d269dca 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -10,10 +10,12 @@
/reference/add_exposure_data.html /reference/biokinetics.html /reference/biokinetics_priors.html /reference/convert_log2_scale.html /reference/convert_log2_scale_inverse.html /reference/epikinetics-package.html /reference/index.html /reference/plot.biokinetics_population_trajectories.html /reference/plot.biokinetics_priors.html /reference/plot_sero_data.html
dat <- data.table::fread(system.file("delta_full.rds", package = "epikinetics"))
head(dat)
-#> pid day last_exp_day titre_type value censored infection_history
-#> <int> <IDat> <IDat> <char> <num> <int> <char>
-#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 0 Infection naive
-#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 0 Infection naive
-#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 0 Infection naive
-#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 -1 Infection naive
-#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 0 Infection naive
-#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 0 Infection naive
+#> pid day last_exp_day titre_type value infection_history
+#> <int> <IDat> <IDat> <char> <num> <char>
+#> 1: 1 2021-03-10 2021-03-08 Ancestral 175.9350 Infection naive
+#> 2: 1 2021-04-15 2021-03-08 Ancestral 607.5750 Infection naive
+#> 3: 1 2021-07-08 2021-03-08 Ancestral 179.0463 Infection naive
+#> 4: 1 2021-03-10 2021-03-08 Alpha 5.0000 Infection naive
+#> 5: 1 2021-04-15 2021-03-08 Alpha 416.7905 Infection naive
+#> 6: 1 2021-07-08 2021-03-08 Alpha 103.5274 Infection naive
#> last_vax_type exp_num
#> <char> <int>
#> 1: BNT162b2 2
@@ -185,7 +171,7 @@ Example
-Ouput data
+Output data
After fitting a model, a CmdStanMCMC
object is returned. This means that users who are already familiar with
diff --git a/pkgdown.yml b/pkgdown.yml
index e2ec282..42078ed 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,4 +5,4 @@ articles:
biokinetics: biokinetics.html
data: data.html
model: model.html
-last_built: 2024-11-15T12:04Z
+last_built: 2024-11-19T13:48Z
diff --git a/reference/biokinetics.html b/reference/biokinetics.html
index 456b7c4..ef689b7 100644
--- a/reference/biokinetics.html
+++ b/reference/biokinetics.html
@@ -89,7 +89,11 @@
Usage
file_path = NULL,
covariate_formula = ~0,
preds_sd = 0.25,
- scale = "natural"
+ scale = "natural",
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL,
+ strict_upper_limit = TRUE,
+ strict_lower_limit = TRUE
)
@@ -122,6 +126,26 @@ Arguments
@@ -158,12 +182,20 @@ Returns
Method plot_model_inputs()
Plot model input data with a smoothing function. Note that
-this plot is on a log scale, regardless of whether data was provided on a
-log or a natural scale.
+this plot is of the data as provided to the Stan model so is on a log scale,
+regardless of whether data was provided on a log or a natural scale.
+
-Arguments
+Arguments
...Named arguments to the sample() method of CmdStan model.
objects: https://mc-stan.org/cmdstanr/reference/model-method-sample.html
Usage
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
-Arguments
+Arguments
n_drawsInteger. Maximum number of samples to include. Default 2000.
Usage
-Arguments
+Arguments
summariseBoolean. If TRUE, average the individual trajectories to get lo, me and
hi values for the population, disaggregated by titre type. If FALSE return the indidivudal trajectories.
diff --git a/reference/convert_log2_scale.html b/reference/convert_log2_scale.html
new file mode 100644
index 0000000..c1277df
--- /dev/null
+++ b/reference/convert_log2_scale.html
@@ -0,0 +1,113 @@
+
+
Base 2 log scale conversion — convert_log2_scale • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Base 2 log scale conversion
+
+ convert_log2_scale.Rd
+
+
+
+ Natural scale data is converted to a base 2 log scale before model fitting.
+This function does not modify the provided data.table in-place, but returns a transformed copy.
+
+
+
+ convert_log2_scale(dt_in, smallest_value, vars_to_transform = "value")
+
+
+
+ Arguments
+
+
+
+
+ Value
+ A data.table, identical to the input data but with specified columns transformed.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/reference/convert_log2_scale_inverse.html b/reference/convert_log2_scale_inverse.html
index d1530d5..a7c870c 100644
--- a/reference/convert_log2_scale_inverse.html
+++ b/reference/convert_log2_scale_inverse.html
@@ -64,7 +64,7 @@ Invert base 2 log scale conversion
- convert_log2_scale_inverse(dt_in, vars_to_transform)
+ convert_log2_scale_inverse(dt_in, vars_to_transform, smallest_value)
@@ -78,6 +78,10 @@ Arguments
- vars_to_transform
Names of columns to apply the transformation to.
- smallest_value
+The lowest value in the original dataset.
Value
diff --git a/reference/index.html b/reference/index.html
index f1fa73b..22d57aa 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -69,6 +69,10 @@ All functions biokinetics_priors()
Construct priors for the biomarker model.
+
+
+ Base 2 log scale conversion
@@ -81,6 +85,10 @@ All functions plot(<biokinetics_priors>)
Simulate biomarker kinetics predicted by the given biokinetics priors and optionally compare to a dataset.
+
+
+ Plot serological data
@@ -89,6 +97,14 @@ Arguments
- data
Optional data.frame with columns time_since_last_exp and value. The raw data to compare to.
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
Value
diff --git a/reference/plot_sero_data.html b/reference/plot_sero_data.html
new file mode 100644
index 0000000..fa47ef8
--- /dev/null
+++ b/reference/plot_sero_data.html
@@ -0,0 +1,126 @@
+
+Plot serological data — plot_sero_data • epikinetics
+
+
+
+
+
+
+
+
+
+
+ Plot serological data
+
+ plot_sero_data.Rd
+
+
+
+ Plot serological data in the format provided to the biokinetics
+model, with a smoothing function fitted.
+
+
+
+ plot_sero_data(
+ data,
+ tmax = 150,
+ covariates = character(0),
+ upper_censoring_limit = NULL,
+ lower_censoring_limit = NULL
+)
+
+
+
+ Arguments
+
+
+- data
+A data.table with required columns time_since_last_exp, value and titre_type.
- tmax
+Integer. The number of time points in each simulated trajectory. Default 150.
- covariates
+Optional vector of covariate names to facet by (these must correspond to columns in the data.table)
- upper_censoring_limit
+Optional upper detection limit.
- lower_censoring_limit
+Optional lower detection limit.
+
+ Value
+ A ggplot2 object.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/sitemap.xml b/sitemap.xml
index f2c48cf..d269dca 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -10,10 +10,12 @@
/reference/add_exposure_data.html /reference/biokinetics.html /reference/biokinetics_priors.html /reference/convert_log2_scale.html /reference/convert_log2_scale_inverse.html /reference/epikinetics-package.html /reference/index.html /reference/plot.biokinetics_population_trajectories.html /reference/plot.biokinetics_priors.html /reference/plot_sero_data.html