Package 'viralmodels' reference manual

Title:	Viral Load and CD4 Lymphocytes Regression Models
Description:	Provides a comprehensive framework for building, evaluating, and visualizing regression models for analyzing viral load and CD4 (Cluster of Differentiation 4) lymphocytes data. It leverages the principles of the tidymodels ecosystem of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org> to offer a user-friendly experience in model development. This package includes functions for data preprocessing, feature engineering, model training, tuning, and evaluation, along with visualization tools to enhance the interpretation of model results. It is specifically designed for researchers in biostatistics, computational biology, and HIV research who aim to perform reproducible and rigorous analyses to gain insights into disease dynamics. The main focus is on improving the understanding of the relationships between viral load, CD4 lymphocytes, and other relevant covariates to contribute to HIV research and the visibility of vulnerable seropositive populations.
Authors:	Juan Pablo Acuña González [aut, cre]
Maintainer:	Juan Pablo Acuña González <[email protected]>
License:	MIT + file LICENSE
Version:	1.3.2
Built:	2024-12-27 19:20:18 UTC
Source:	https://github.com/juanv66x/viralmodels

Select best model

Description

Returns performance metrics for a selected model

Usage

viralmodel(
  traindata,
  semilla,
  target,
  viralvars,
  logbase,
  pliegues,
  repeticiones,
  rejilla,
  modelo
)
viralmodel(
  traindata,
  semilla,
  target,
  viralvars,
  logbase,
  pliegues,
  repeticiones,
  rejilla,
  modelo
)

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
`modelo`	A character value

Value

A table with a single model hyperparameters

Examples


library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
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
modelo <- "simple_rf"
set.seed(123)
viralmodel(traindata, semilla, target, viralvars, logbase, pliegues, repeticiones, rejilla, modelo)

library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
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
modelo <- "simple_rf"
set.seed(123)
viralmodel(traindata, semilla, target, viralvars, logbase, pliegues, repeticiones, rejilla, modelo)

Predict Viral Load or CD4 Count using Many Models

Description

This function predicts viral load or CD4 count values based on multiple machine learning models using cross-validation. It allows users to specify two types of predictions: normal predictions on the full dataset or observation-by-observation (obs-by-obs) predictions.

Usage

viralpreds(
  target,
  pliegues,
  repeticiones,
  rejilla,
  semilla,
  data,
  prediction_type = "full"
)
viralpreds(
  target,
  pliegues,
  repeticiones,
  rejilla,
  semilla,
  data,
  prediction_type = "full"
)

Arguments

`target`	A character string specifying the column name of the target variable to predict.
`pliegues`	An integer specifying the number of folds for cross-validation.
`repeticiones`	An integer specifying the number of times the cross-validation should be repeated.
`rejilla`	An integer specifying the number of grid search iterations for tuning hyperparameters.
`semilla`	An integer specifying the seed for random number generation to ensure reproducibility.
`data`	A data frame containing the predictors and the target variable.
`prediction_type`	A character string specifying the type of predictions to perform. Use `"full"` (default) to perform predictions on the full dataset at once, or `"batch"` to perform predictions in a smaller size batches of data.

Value

A list containing two elements: predictions (a vector of predicted values for the target variable) and RMSE (the root mean square error of the best model).

Examples


library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
library(viraldomain)
data("viral", package = "viraldomain")
viral_imputed <- viral |>
mutate(across(starts_with("vl"), ~impute_undetectable(.x)))
traindata <- viral_imputed
target <- "cd_2022"
viralvars <- c("vl_2019", "vl_2021", "vl_2022")
logbase <- 10
pliegues <- 5
repeticiones <- 2
rejilla <- 2
semilla <- 123
viralpreds(target, pliegues, repeticiones, rejilla, semilla, traindata)

library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
library(viraldomain)
data("viral", package = "viraldomain")
viral_imputed <- viral |>
mutate(across(starts_with("vl"), ~impute_undetectable(.x)))
traindata <- viral_imputed
target <- "cd_2022"
viralvars <- c("vl_2019", "vl_2021", "vl_2022")
logbase <- 10
pliegues <- 5
repeticiones <- 2
rejilla <- 2
semilla <- 123
viralpreds(target, pliegues, repeticiones, rejilla, semilla, traindata)

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
)
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(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)

library(dplyr)
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)

Competing models plot

Description

Plots the rankings of a series of regression models for viral load or CD4 counts

Usage

viralvis(
  traindata,
  semilla,
  target,
  viralvars,
  logbase,
  pliegues,
  repeticiones,
  rejilla
)
viralvis(
  traindata,
  semilla,
  target,
  viralvars,
  logbase,
  pliegues,
  repeticiones,
  rejilla
)

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

Value

A plot of ranking models

Examples


library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
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)
viralvis(traindata, semilla, target, viralvars, logbase, pliegues, repeticiones, rejilla)

library(tidyverse)
library(baguette)
library(kernlab)
library(kknn)
library(ranger)
library(rules)
library(glmnet)
# Define the function to impute values in the undetectable range
set.seed(123)
impute_undetectable <- function(column) {
ifelse(column <= 40,
      rexp(sum(column <= 40), rate = 1/13) + 1,
            column)
            }
# Apply the function to all vl columns using purrr's map_dfc
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)
viralvis(traindata, semilla, target, viralvars, logbase, pliegues, repeticiones, rejilla)

Package 'viralmodels'

Help Index

Select best model

Description

Usage

Arguments

Value

Examples

Predict Viral Load or CD4 Count using Many Models

Description

Usage

Arguments

Value

Examples

Competing models table

Description

Usage

Arguments

Value

Examples

Competing models plot

Description

Usage

Arguments

Value

Examples