generatePlot {gammaFuncModel}R Documentation

Function that generate plots for metabolite models

Description

Function that generate plots for metabolite models

Usage

generatePlot(
  graph,
  df,
  met_vec,
  covariates,
  grp = "Diet",
  models,
  save_path = NULL
)

Arguments

graph

character string, 'None' by default. If not 'None, in addition to returning models, produces pdf file of graphs based on the specific value of 'graph'.

df

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other Time terms (numeric); other individidual characteristics covariates; as well columns of metabolite concentrations; Note: All non-concentration columns must be complete (No missing values); concentration columns can have missing values in the forms of either numeric 0 or 'NA'.

met_vec

the vector of metabolite names

covariates

Vector containing the names of the "ID" covariate, grouping covariate and other covariates excluding any "Time" covariates;

grp

is the grouping variable;

models

a list of fitted non-linear mixed effects metabolite models

save_path

location (file path, not directory) where the pdf file will be saved (must end in '.pdf'); default is NULL, i.e. pdf is saved to a temporary location

Value

A pdf file for fitted concenration curves that is saved to a user provided file location; otherwise saved to a temporary location

References

Wickham, H. (2022). dplyr: A Grammar of Data Manipulation. R package version 1.0.10. Available at: https://CRAN.R-project.org/package=dplyr

Pinheiro, J. C., & Bates, D. M. (2022). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-153. Available at: https://CRAN.R-project.org/package=nlme

Examples


require(gammaFuncModel)
require(dplyr)
require(nlme)
require(patchwork)
require(scales)
 df <- data.frame(
  ID = rep(sprintf("%02d", 1:10), each = 9 * 3),  
  Time = rep(rep(1:9, each = 3), 10),             
  Diet = as.factor(rep(1:3, times = 9 * 10)),     
  Age = rep(sample(20:70, 10, replace = TRUE), each = 9 * 3), 
  BMI = round(rep(runif(10, 18.5, 35), each = 9 * 3), 1)     
)
metvar <- paste0("met", 1:10)
concentration_data <- replicate(10, round(runif(270, 5, 15), 2))
colnames(concentration_data) <- metvar[1:10]
df <- cbind(df, as.data.frame(concentration_data))
covariates <- c("ID", "Diet", "Age", "BMI")
mods <- generate_models(df = df, met_vec = metvar, covariates = covariates, graph = 'None')
generatePlot(
  graph = "individual_separated", 
  df = df, 
  met_vec = metvar, 
  covariates = covariates, 
  grp = "Diet", 
  models = mods,
  save_path = NULL
 )


[Package gammaFuncModel version 5.0 Index]