mars_domain_score {viraldomain}

Calculate the MARS model domain applicability score

Description

This function fits a MARS (Multivariate Adaptive Regression Splines) model to the provided data and computes a domain applicability score based on PCA distances.

Usage

mars_domain_score(
  featured_col,
  train_data,
  mars_hyperparameters,
  test_data,
  threshold_value
)

Arguments

featured_col

The name of the featured column.

train_data

A data frame containing the training data.

mars_hyperparameters

A list of hyperparameters for the MARS model, including:

• num_terms: The number of terms to include in the MARS model.

• prod_degree: The degree of interaction terms to include.

• prune_method: The method used for pruning the MARS model.

test_data

A data frame containing the test data.

threshold_value

The threshold value for the domain score.

Value

A tibble with the domain applicability scores.

Examples

library(viraldomain)
library(dplyr)

# Set the seed for reproducibility
set.seed(1234)

# Create a tibble with the training data
data(viral)

# Number of imputations needed
num_imputations <- sum(viral$vl_2022 <= 40)  # Count values below 40 cpm

# Impute unique values
imputed_values <- unique(rexp(num_imputations, rate = 1/13))

# Create a new tibble with mutated/imputed viral load
imputed_viral <- viral |>
  mutate(imputed_vl_2022 = ifelse(vl_2022 <= 40, imputed_values, vl_2022),
         log10_imputed_vl_2022 = log10(ifelse(vl_2022 <= 40, imputed_values, vl_2022)),
         jittered_log10_imputed_vl_2022 = jitter(log10_imputed_vl_2022))

# Create a new tibble with mutated/imputed cd4 counts
imputed_viral <- imputed_viral |>
  mutate(
    jittered_cd_2022 = ifelse(
    duplicated(cd_2022),
    cd_2022 + sample(1:100, length(cd_2022), replace = TRUE),
    cd_2022
    )
  )

# New data frame with mutated/imputed columns
imp_viral <- imputed_viral |>
select(jittered_cd_2022, jittered_log10_imputed_vl_2022) |>
scale() |>
as.data.frame()

# Set the seed for reproducibility
set.seed(1234)

# Create a tibble with the testing data
data(sero)

# Number of imputations needed
num_imputations <- sum(sero$vl_2022 <= 40)  # Count values below 40 cpm

# Impute unique values
imputed_values <- unique(rexp(num_imputations, rate = 1/13))

# Create a new tibble with mutated/imputed viral load
imputed_sero <- sero |>
  mutate(imputed_vl_2022 = ifelse(vl_2022 <= 40, imputed_values, vl_2022),
         log10_imputed_vl_2022 = log10(ifelse(vl_2022 <= 40, imputed_values, vl_2022)),
         jittered_log10_imputed_vl_2022 = jitter(log10_imputed_vl_2022))

# Create a new tibble with mutated/imputed cd
imputed_sero <- imputed_sero |>
  mutate(
    jittered_cd_2022 = ifelse(
    duplicated(cd_2022),
    cd_2022 + sample(1:100, length(cd_2022), replace = TRUE),
    cd_2022
    )
  )

# New data frame with mutated/imputed columns
imp_sero <- imputed_sero |>
select(jittered_cd_2022, jittered_log10_imputed_vl_2022) |>
scale() |>
as.data.frame()

# Specify your function parameters
featured_col <- "jittered_cd_2022"
train_data <- imp_viral
mars_hyperparameters <- list(num_terms = 3, prod_degree = 1, prune_method = "none")
test_data <- imp_sero
threshold_value <- 0.99

# Call the function
mars_domain_score(featured_col, train_data, mars_hyperparameters, test_data, threshold_value)