viralx_mars {viralx}

Explain Multivariate Adaptive Regression Splines Model

Description

Explains the predictions of a Multivariate Adaptive Regression Splines (MARS) model for viral load or CD4 counts using the DALEX and DALEXtra tools.

Usage

viralx_mars(vip_featured, hiv_data, nt, pd, pru, vip_train, vip_new)

Arguments

vip_featured

A character value

hiv_data

A data frame

nt

A numeric value

pd

A numeric value

pru

A character value

vip_train

A data frame

vip_new

A numeric vector

Value

A data frame

Examples

library(dplyr)
library(rsample)
library(Formula)
library(plotmo)
library(plotrix)
library(TeachingDemos)
cd_2019 <- c(824, 169, 342, 423, 441, 507, 559,
             173, 764, 780, 244, 527, 417, 800,
             602, 494, 345, 780, 780, 527, 556,
             559, 238, 288, 244, 353, 169, 556,
             824, 169, 342, 423, 441, 507, 559)
vl_2019 <- c(40, 11388, 38961, 40, 75, 4095, 103,
             11388, 46, 103, 11388, 40, 0, 11388,
             0,   4095,   40,  93,  49,  49,  49,
             4095,  6837, 38961, 38961, 0, 0, 93,
             40, 11388, 38961, 40, 75, 4095, 103)
cd_2021 <- c(992, 275, 331, 454, 479, 553,  496,
             230, 605, 432, 170, 670, 238,  238,
             634, 422, 429, 513, 327, 465,  479,
             661, 382, 364, 109, 398, 209, 1960,
             992, 275, 331, 454, 479, 553,  496)
vl_2021 <- c(80, 1690,  5113,  71,  289,  3063,  0,
             262,  0,  15089,  13016, 1513, 60, 60,
             49248, 159308, 56, 0, 516675, 49, 237,
             84,  292,  414, 26176,  62,  126,  93,
             80, 1690, 5113,    71, 289, 3063,   0)
cd_2022 <- c(700, 127, 127, 547, 547, 547, 777,
             149, 628, 614, 253, 918, 326, 326,
             574, 361, 253, 726, 659, 596, 427,
             447, 326, 253, 248, 326, 260, 918,
             700, 127, 127, 547, 547, 547, 777)
vl_2022 <- c(0,   0,   53250,   0,   40,   1901, 0,
             955,    0,    0,    0,   0,   40,   0,
             49248, 159308, 56, 0, 516675, 49, 237,
             0,    23601,   0,   40,   0,   0,   0,
             0,    0,     0,     0,    0,    0,  0)
x <- cbind(cd_2019, vl_2019, cd_2021, vl_2021, cd_2022, vl_2022) |>
as.data.frame()
set.seed(123)
hi_data <- rsample::initial_split(x)
set.seed(123)
hiv_data <- hi_data |>
rsample::training()
nt <- 3
pd <- 1
pru <- "none"
vip_featured <- c("cd_2022")
vip_features <- c("cd_2019", "vl_2019", "cd_2021", "vl_2021", "vl_2022")
set.seed(123)
vi_train <- rsample::initial_split(x)
set.seed(123)
vip_train <- vi_train |>
rsample::training() |>
dplyr::select(rsample::all_of(vip_features))
vip_new <- vip_train[1,]
viralx_mars(vip_featured, hiv_data, nt, pd, pru, vip_train, vip_new)