The goal of viraldomain is to provide methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. These methods help determine the extent of extrapolation when making predictions.
You can install the development version of viraldomain from GitHub with:
# install.packages("devtools")
devtools::install_github("juanv66x/viraldomain")
#> data.table (1.16.0 -> 1.16.2) [CRAN]
#> modelenv (0.1.1 -> 0.2.0 ) [CRAN]
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> checking for file ‘/tmp/Rtmp7ZZw7N/remotesa05859be188e/juanv66x-viraldomain-eae2c27/DESCRIPTION’ ... ✔ checking for file ‘/tmp/Rtmp7ZZw7N/remotesa05859be188e/juanv66x-viraldomain-eae2c27/DESCRIPTION’ (511ms)
#> ─ preparing ‘viraldomain’:
#> checking DESCRIPTION meta-information ... ✔ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts (598ms)
#> ─ checking for empty or unneeded directories
#> ─ building ‘viraldomain_0.0.6.tar.gz’
#>
#> This data set serves as input for predictive modeling tasks related to HIV research. It contains numeric measurements of CD4 lymphocyte counts (cd) and viral load (vl) at three different time points: 2019, 2021, and 2022. These measurements are crucial indicators of HIV disease progression.
library(viraldomain)
data(viral)
print(head(viral))
#> cd_2019 vl_2019 cd_2021 vl_2021 cd_2022 vl_2022
#> 1 819.7516 39.55592 996.2036 82.1747 694.6232 6.795355
#> 2 174.7661 11393.16919 265.0125 1688.2138 121.7820 1.353418
#> 3 340.1795 38955.32816 330.5063 5105.9419 118.5338 53254.165806
#> 4 430.6603 36.36362 448.4024 79.7060 541.9145 1.182425
#> 5 449.8093 69.63252 476.5963 285.0246 551.6427 41.886864
#> 6 497.9111 4087.85600 555.2554 3054.2144 553.9491 1900.625796This data set is designed for testing the applicability domain of methods related to HIV research. It provides a tibble with 53 rows and 2 columns containing numeric measurements of CD4 lymphocyte counts (cd_2022) and viral load (vl_2022) for seropositive individuals in 2022.
data(sero)
print(head(sero))
#> cd_2022 vl_2022
#> 1 544.7516 18.437985
#> 2 165.7661 89.218960
#> 3 692.1795 1.458075
#> 4 523.6603 1.308280
#> 5 161.8093 5.066157
#> 6 373.9111 3114.900907This function fits a K-Nearest Neighbor (KNN) model to the provided data and computes a domain applicability score based on PCA distances.
# Example usage of knn_domain_score
domain_scores <- knn_domain_score(
featured = "cd_2022",
train_data = viral |> dplyr::select(cd_2022, vl_2022),
knn_hyperparameters = list(neighbors = 5, weight_func = "optimal", dist_power = 0.33),
test_data = sero,
threshold_value = 0.99
)
print(domain_scores)
#> # A tibble: 53 × 3
#> .pred distance distance_pctl
#> <dbl> <dbl> <dbl>
#> 1 528. 0.424 9.08
#> 2 405. 1.32 69.9
#> 3 545. 1.01 60.7
#> 4 599. 0.356 4.37
#> 5 585. 1.33 70.2
#> 6 371. 0.452 18.7
#> 7 483. 1.11 66.1
#> 8 405. 0.321 3.49
#> 9 291. 0.573 25.3
#> 10 405. 0.660 38.6
#> # ℹ 43 more rowsThis function generates a domain plot for a simple model based on PCA distances of the provided data.
# Example usage of simple_domain_plot
simple_domain_plot(
featured_col = "cd_2022",
train_data = viral |> dplyr::select(cd_2022, vl_2022),
test_data = sero,
treshold_value = 0.99
)