viraltab {viralmodels}R Documentation

Competing models table

Description

Trains and optimizes a series of regression models for viral load or CD4 counts

Usage

viraltab(
  traindata,
  semilla,
  target,
  viralvars,
  logbase,
  pliegues,
  repeticiones,
  rejilla,
  rank_output = TRUE
)

Arguments

traindata

A data frame

semilla

A numeric value

target

A character value

viralvars

Vector of variable names related to viral data.

logbase

The base for logarithmic transformations.

pliegues

A numeric value

repeticiones

A numeric value

rejilla

A numeric value

rank_output

Logical value. If TRUE, returns ranked output; if FALSE, returns unranked output.

Value

A table of competing models

Examples


library(dplyr)
library(magrittr)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
impute_undetectable <- function(column) {
set.seed(123)
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
library(viraldomain)
data("viral", package = "viraldomain")
viral_imputed <- viral %>%
mutate(across(starts_with("vl"), ~impute_undetectable(.x)))
traindata <- viral_imputed
semilla <- 1501
target <- "cd_2022"
viralvars <- c("vl_2019", "vl_2021", "vl_2022")
logbase <- 10
pliegues <- 2
repeticiones <- 1
rejilla <- 1
set.seed(123)
viraltab(traindata, semilla, target, viralvars, logbase, pliegues, 
repeticiones, rejilla, rank_output = TRUE)


[Package viralmodels version 1.3.4 Index]