Applicability Domain Methods of Viral Load and CD4 Lymphocytes.
viraldomain
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.
Installation
You can install the development version of viraldomain from GitHub with:
# install.packages("devtools")
devtools::install_github("juanv66x/viraldomain")
#>
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> checking for file ‘/tmp/Rtmp6medQX/remotesa89540251f8f/juanv66x-viraldomain-1983b42/DESCRIPTION’ ... ✔ checking for file ‘/tmp/Rtmp6medQX/remotesa89540251f8f/juanv66x-viraldomain-1983b42/DESCRIPTION’ (363ms)
#> ─ preparing ‘viraldomain’:
#> ✔ checking DESCRIPTION meta-information
#> ─ checking for LF line-endings in source and make files and shell scripts
#> ─ checking for empty or unneeded directories
#> ─ building ‘viraldomain_0.0.0.9000.tar.gz’
#>
#>
Data
Predictive Modeling Data for Viral Load and CD4 Lymphocyte Counts
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
#> 1 824.5332 38.56798 991.7403 82.54730 699.5054 4.076213
#> 2 168.7046 11389.97420 274.4726 1671.39342 126.1513 14.921826
#> 3 342.5670 38960.42871 330.9015 5120.02580 127.0883 53268.898678
#> 4 423.1296 41.16719 454.1496 70.85965 546.0022 -7.202574
#> 5 441.1572 74.67582 478.8419 281.52784 547.4582 44.738029
#> 6 506.6313 4095.79251 553.0661 3077.96262 547.5480 1895.702386
Seropositive Data for Applicability Domain Testing
This 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
#> 1 548.9531 19.975988
#> 2 160.1478 92.854885
#> 3 694.0009 -15.890951
#> 4 515.9214 -15.630209
#> 5 152.9998 -1.104756
#> 6 382.8012 3105.038849
Functions
knn_domain_score
This 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>
#> 1 591. 0.438 20.3
#> 2 332. 1.35 70.7
#> 3 330. 1.02 60.7
#> 4 354. 0.332 3.60
#> 5 467. 1.38 74.9
#> 6 350. 0.425 7.57
#> 7 528. 1.11 66.5
#> 8 336. 0.346 3.98
#> 9 528. 0.568 24.5
#> 10 332. 0.664 38.0
#> # ℹ 43 more rows
simple_domain_plot
This 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
)
