Type: Package
Title: Non-Linear Mixed Effects Model Based on the Gamma Function Form
Version: 5.0
Date: 2025-06-20
Description: Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.
License: GPL-2 | GPL-3 [expanded from: GPL]
RoxygenNote: 7.2.3
Imports: Rdpack, nlme, dplyr, cubature, rootSolve, patchwork, scales, ggplot2, future.apply, gridExtra, rlang, grDevices, stats, grid
NeedsCompilation: no
Packaged: 2025-06-20 14:08:35 UTC; htchen
Author: Hongting Chen [aut, cre], Liming Liang [aut]
Maintainer: Hongting Chen <hongtingchen@berkeley.edu>
Repository: CRAN
Date/Publication: 2025-06-20 14:30:02 UTC

Function that produces Area Under the Curve(AUC) property for a single individual in a particular group, for a specific metabolite

Description

Function that produces Area Under the Curve(AUC) property for a single individual in a particular group, for a specific metabolite

Usage

calculate_AUC(f, upperbound)

Arguments

f

function that returns the prediction of a metabolite concentration, for a single individual in a particular group

upperbound

Numeric value that serves as the upperbound of integration

Value

AUC for this metabolite, in a particular group for a single individual

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(cubature)
require(dplyr)
require(nlme)
modify.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),
  Concentration = NA
)
for (i in 1:10) {
 for (d in 1:3) {
   C0 <- runif(1, 10, 15)    # initial concentration
   k <- runif(1, 0.1, 0.3)   # decay rate constant
   modify.df$Concentration[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d] <- 
     C0 * exp(-k * modify.df$Time[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d])
 }
}
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  modify.df, 
  covariates, 
  time_grp_inter = FALSE, 
return_ml_model = FALSE, include_grp = TRUE
)[[1]]
test_data <- modify.df %>% 
  filter(Diet == 1 & ID == "04") %>% 
  select(-c("Concentration", "ID", "Diet")) 
f_dat = modify.df %>% 
  filter(Diet == 1 & ID == "04") %>% 
  select(-Concentration)
f <- generate_f_function(
  data = f_dat, 
  model = model,  
  grp_var = 1, 
  grp_name = "Diet", 
  ID = "04", 
  ref = 1
)
AUC <- calculate_AUC(f, 9)
AUCInf <- calculate_AUC(f, Inf)

Function that produces Cmax property for a single individual in a particular group, for a specific metabolite

Description

Function that produces Cmax property for a single individual in a particular group, for a specific metabolite

Usage

calculate_Cmax(data, model, grp_var, ID, grp_name = "Diet", Tmax)

Arguments

data

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other individiual characteristics covariates (exlcluding other forms of 'Time') Note: Data must be complete (No missing values).

model

Fitted model for the metabolite in question

grp_var

Value of the grouping variable

ID

Subject ID

grp_name

Name of the grouping variable. Default is 'Diet'

Tmax

for this metabolite, in a particular group for a single individual

Value

Cmax for this metabolite, in a particular group for a single individual

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(rootSolve)
require(dplyr)
require(nlme)
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),     
  Concentration = round(runif(270, 5, 15), 2)
)
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  df, 
  covariates, 
  time_grp_inter = FALSE, 
  return_ml_model = FALSE, 
  include_grp = TRUE
  )[[1]]
test_data = df %>% filter(Diet == 1 & ID == "02") %>% select(-c("Concentration", "ID", "Diet")) 
Tmax <- calculate_Tmax(data = test_data, model, grp_var = 1, ID = "02", grp_name = 'Diet', ref = 1)
Cmax <- calculate_Cmax(data = test_data, model, grp_var = 1, ID = "02", grp_name = "Diet", Tmax)

Function that produces Tmax property for a single individual in a particular group, for a specific metabolite

Description

Function that produces Tmax property for a single individual in a particular group, for a specific metabolite

Usage

calculate_Tmax(data, model, grp_var, ID, grp_name = "Diet", ref = 1)

Arguments

data

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other individiual characteristics covariates(excluding other forms of 'Time') Note: Data must be complete (No missing values);

model

Fitted model for the metabolite in question

grp_var

Value of the grouping variable

ID

Subject ID

grp_name

Name of the grouping variable. Default is 'Diet'

ref

numeric or character. The reference level for the grouping variable, as a factor

Value

Tmax for this metabolite, in a particular group for a single individual

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(rootSolve)
require(dplyr)
require(nlme)
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),     
  Concentration = round(runif(270, 5, 15), 2)
)
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  df, 
  covariates, 
  time_grp_inter = FALSE, 
  return_ml_model = FALSE, 
  include_grp = TRUE
)[[1]]
test_data = df %>% filter(Diet == 1 & ID == "01") %>% select(-c("Concentration", "ID", "Diet")) 
Tmax <- calculate_Tmax(data = test_data, model, grp_var = 1, ID = "01", grp_name = 'Diet', ref = 1)

Function that produces Half-life property for a single individual in a particular group, for a specific metabolite

Description

Function that produces Half-life property for a single individual in a particular group, for a specific metabolite

Usage

calculate_half_life(f, Tmax, Cmax)

Arguments

f

function that returns the prediction of a metabolite concentration, for a single individual in a particular group

Tmax

Tmax property of a metabolite, for a single individual in a particular group

Cmax

Cmax property of a metabolite, for a single individual in a particular group

Value

Half-life for this metabolite, in a particular group for a single individual

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(rootSolve)
require(dplyr)
require(nlme)

modify.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),
  Concentration = NA
)
for (i in 1:10) {
 for (d in 1:3) {
   C0 <- runif(1, 10, 15)    # initial concentration
   k <- runif(1, 0.1, 0.3)   # decay rate constant
   modify.df$Concentration[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d] <- 
     C0 * exp(-k * modify.df$Time[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d])
 }
}
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  modify.df, 
  covariates, 
  time_grp_inter = FALSE, 
  return_ml_model = FALSE, 
  include_grp = TRUE
  )[[1]]
test_data = modify.df %>% 
  filter(Diet == 1 & ID == "03") %>% 
  select(-c("Concentration", "ID", "Diet")) 
Tmax <- calculate_Tmax(data = test_data, model, grp_var = 1, ID = "03", grp_name = 'Diet', ref = 1)
Cmax <- calculate_Cmax(data = test_data, model, grp_var = 1, ID = "03", grp_name = "Diet", Tmax)
f_dat = modify.df %>% filter(Diet == 1 & ID == "03") %>% select(-Concentration)
f <- generate_f_function(
  data = f_dat, 
  model = model,  
  grp_var = 1, 
  grp_name = "Diet", 
  ID = "03", 
  ref = 1)
half_life <- calculate_half_life(f, Tmax, Cmax)

Function that produces a summary table for coefficient estimates, their p-values and LRT p-values for every metabolite in the dataframe

Description

Function that produces a summary table for coefficient estimates, their p-values and LRT p-values for every metabolite in the dataframe

Usage

diffGrpResponse(
  df,
  met_vec,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp = "Diet",
  random_formula = ~1 + Time | ID/Diet,
  correlation_formula = corSymm(form = ~Time | ID/Diet),
  weights = varIdent(form = ~1 | Time)
)

Arguments

df

Data frame containing columns Group(numeric or character); ID(subject ID: character); Time(positive: numeric); other Time terms (numeric); other individual 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

Vector of metabolite names

covariates

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

time_terms

Vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp

Grouping variable;

random_formula

Random effects formula for the model, nested effects of Diet within ID (could also add random slope for 'Time');

correlation_formula

Correlation formula. Default is autorgressive but can accommodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity

Value

Data frame that contains the coefficient estimates, their corresponding p-values; LRT p-values for Time-Group interactions (for every 'Time' term);LRT p-values for Group and Time-Group interactions (for every 'Time' term); as well as the fitted models for each metabolite

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)
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")
result <- diffGrpResponse(df, metvar, covariates)[[1]]
summary(result)

Parallelized version of diffGrpResponse()

Description

Parallelized version of diffGrpResponse()

Usage

diffGrpResponse_parallel(
  df,
  met_vec,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp = "Diet",
  random_formula = ~1 + Time | ID/Diet,
  correlation_formula = corSymm(form = ~Time | ID/Diet),
  weights = varIdent(form = ~1 | Time)
)

Arguments

df

Data frame containing columns Group(numeric or character); ID(subject ID: character); Time(positive: numeric); other Time terms (numeric); other individual 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

Vector of metabolite names

covariates

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

time_terms

Vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp

Grouping variable;

random_formula

Random effects formula for the model, nested effects of Diet within ID (could also add random slope for 'Time');

correlation_formula

Correlation formula. Default is autorgressive but can accommodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity

Value

Data frame that contains the coefficient estimates, their corresponding p-values; LRT p-values for Time-Group interactions (for every 'Time' term);LRT p-values for Group and Time-Group interactions (for every 'Time' term); as well as the fitted models for each metabolite

Note

This function uses parallel processing via the 'future.apply' package. To enable parallel execution, runs the following before calling this function:

library(future.apply) plan(multisession, workers = parallel::detectCores() - 1)

You only need to set the plan once per session.

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)
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")
library(future.apply)
plan(multisession, workers = parallel::detectCores() - 1)
result <- diffGrpResponse(df, metvar, covariates)[[1]]
summary(result)

Implementation of the novel non-linear mixed-effects model based on gamma function form with nested covariance structure where random effects are specified for each Diet level within each subject (ID), capturing within-subject correlation across dietary conditions. to identify metabolites that responds to time differentially across dietary groups

Description

Implementation of the novel non-linear mixed-effects model based on gamma function form with nested covariance structure where random effects are specified for each Diet level within each subject (ID), capturing within-subject correlation across dietary conditions. to identify metabolites that responds to time differentially across dietary groups

Usage

gammaFunction(
  data,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp = "Diet",
  random_formula = ~1 + Time | ID/Diet,
  correlation_formula = corSymm(form = ~Time | ID/Diet),
  weights = varIdent(form = ~1 | Time),
  time_grp_inter = TRUE,
  return_ml_model = FALSE,
  include_grp
)

Arguments

data

Data frame that contains the 'ID' column along with all covariates as well as concentration column, named 'Concentration', for a single metabolite Note: All non-concentration columns must be complete (No missing values); the concentration column can have missing values in the forms of either numeric 0 or 'NA'.

covariates

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

time_terms

Vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp

Grouping variable;

random_formula

Random effects formula for the model, nested effects of Diet within ID (could also add random slope for 'Time');

correlation_formula

Correlation formula. Default is autorrgressive but can accomodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity

time_grp_inter

Boolean value that indicates if the model should include interactions terms of 'time_terms' with 'Group';

return_ml_model

Boolean value that indicates if the model should fit "ML" model as well as "REML" model(default)

include_grp

boolean value to indicate whether or not 'grp' should be included in the model construction

Value

mixed effects models for a single metabolites: one with REML, the other with ML

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)
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),    
  Concentration = round(runif(270, 5, 15), 2)
)
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  df, 
  covariates, 
  random_formula = ~ 1  | ID/Diet, 
  correlation_formula = corAR1(form = ~ Time | ID/Diet), 
  weights = NULL,
  include_grp = TRUE)[[1]]
summary(model)

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
 )

Function produce predictions from the model

Description

Function produce predictions from the model

Usage

generate_f_function(data, model, grp_var, grp_name = "Diet", ID, ref = 1)

Arguments

data

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other individiual characteristics covariates (exlcluding other forms of 'Time') Note: Data must be complete (No missing values).

model

Fitted model for the metabolite in question

grp_var

Value of the grouping variable

grp_name

Name of the grouping variable. Default is 'Diet'

ID

Subject ID

ref

reference group

Value

f function that produces the prediction from this model for a specific individual in a specific group

Examples


require(gammaFuncModel)
require(dplyr)
require(nlme)
modify.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),
  Concentration = NA
)
for (i in 1:10) {
 for (d in 1:3) {
   C0 <- runif(1, 10, 15)    # initial concentration
   k <- runif(1, 0.1, 0.3)   # decay rate constant
   modify.df$Concentration[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d] <- 
     C0 * exp(-k * modify.df$Time[modify.df$ID == sprintf("%02d", i) & modify.df$Diet == d])
 }
}
covariates <- c("ID", "Diet", "Age", "BMI")
model <- gammaFunction(
  modify.df, 
  covariates, 
  time_grp_inter = FALSE, 
  return_ml_model = FALSE, 
  include_grp = TRUE
 )[[1]]
test_data = modify.df %>% 
  filter(Diet == 1 & ID == "04") %>% 
  select(-c("Concentration", "ID", "Diet")) 
f_dat = modify.df %>% filter(Diet == 1 & ID == "04") %>% select(-Concentration)
f <- generate_f_function(
  data = f_dat, 
  model = model,  
  grp_var = 1, 
  grp_name = "Diet", 
  ID = "04", 
  ref = 1
 )

Function that produces a fitted gamma model for each metabolite

Description

Function that produces a fitted gamma model for each metabolite

Usage

generate_models(
  df,
  met_vec,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp_name = "Diet",
  random_formula = ~1 + Time | ID/Diet,
  correlation_formula = corSymm(form = ~Time | ID/Diet),
  weights = varIdent(form = ~1 | Time),
  graph = "None",
  save_path = NULL
)

Arguments

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

time_terms

is the vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp_name

is the grouping variable;

random_formula

is the random effects formula for the model, nested effects of Diet within ID (could also add random slope for 'Time');

correlation_formula

is the correlation formula. Default is autorgressive but can accommodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity;

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'.

save_path

location where the pdf file will be saved; default is NULL, i.e. pdf is saved to a temporary location

Value

List that contains fitted models for each metabolite and a pdf file for fitted concenration curves.

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)
 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', 
  save_path = NULL)

Function that produces a summary table for coefficient estimates, their p-values and LRT p-values for every metabolite in the dataframe, for a single Group

Description

Function that produces a summary table for coefficient estimates, their p-values and LRT p-values for every metabolite in the dataframe, for a single Group

Usage

grpResp2Time(
  df,
  met_vec,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp = "Diet",
  random_formula = ~1 | ID,
  correlation_formula = corAR1(form = ~Time | ID),
  weights = NULL
)

Arguments

df

Data frame containing information for a single group, containing columns grp; ID(subject ID: character); Time(positive: numeric); other Time terms (numeric); other individual 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

Vector of metabolite names

covariates

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

time_terms

Vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp

Grouping variable (should be a single valued column);

random_formula

Random effects formula for the model, within ID (could also add random slope for 'Time');

correlation_formula

Correlation formula. Default is autorgressive but can accommodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity

Value

Data frame that contains the coefficient estimates, their corresponding p-values as well as LRT p-values for 'Time' terms

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)
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))
df_single_diet <- subset(df, Diet == 1)
covariates <- c("ID","Diet", "Age", "BMI")
result_SD <- grpResp2Time(df_single_diet, metvar, covariates)[[1]]
summary(result_SD)

Vectorized version of grpRes2Time()

Description

Vectorized version of grpRes2Time()

Usage

grpResp2Time_parallel(
  df,
  met_vec,
  covariates,
  time_terms = c("Time", "log(Time)"),
  grp = "Diet",
  random_formula = ~1 | ID,
  correlation_formula = corAR1(form = ~Time | ID),
  weights = NULL
)

Arguments

df

Data frame containing information for a single group, containing columns grp; ID(subject ID: character); Time(positive: numeric); other Time terms (numeric); other individual 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

Vector of metabolite names

covariates

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

time_terms

Vector that contains all additional form of the covariate 'Time" (including the 'Time' covariate), and must contain 'log(Time)', other forms also include I(Time^2) and I(Time^3);

grp

Grouping variable (should be a single valued column);

random_formula

Random effects formula for the model, within ID (could also add random slope for 'Time');

correlation_formula

Correlation formula. Default is autorgressive but can accommodate other forms such as unstructured covariance or exponential covariance;

weights

specify a variance function that models heteroscedasticity

Value

Data frame that contains the coefficient estimates, their corresponding p-values as well as LRT p-values for 'Time' terms

Note

This function uses parallel processing via the 'future.apply' package. To enable parallel execution, runs the following before calling this function:

library(future.apply) plan(multisession, workers = parallel::detectCores() - 1)

You only need to set the plan once per session.

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)
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))
df_single_diet <- subset(df, Diet == 1)
covariates <- c("ID","Diet", "Age", "BMI")
library(future.apply)
plan(multisession, workers = parallel::detectCores() - 1)
result_SD <- grpResp2Time_parallel(df_single_diet, metvar, covariates)[[1]]
summary(result_SD)

Function that returns a data frame for Tmax, Cmax, half-life, AUC and AUCInf for metabolites

Description

Function that returns a data frame for Tmax, Cmax, half-life, AUC and AUCInf for metabolites

Usage

pk_calculation(df, met_vec, models, grp_name = "Diet", covariates, ref = 1)

Arguments

df

Data frame containing columns Group(factor); ID(subject ID: character); Time(positive: numeric); other individiual characteristics covariates (exlcluding other forms of 'Time') Note: Data must be complete (No missing values).

met_vec

Vector of metabolite names

models

Fitted models for all metabolites of interest

grp_name

Name of the grouping variable

covariates

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

ref

reference level for the grouping variable. could be numeric or character

Value

Data frame with the pharmacokinetic properties of each metabolite

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)

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)
n_rows <- nrow(df)
concentration_data <- sapply(1:10, function(m) {
 shape <- runif(1, 2, 5)     
 scale <- runif(1, 1, 3)     
 rgamma(n_rows, shape = shape, scale = scale)
})
colnames(concentration_data) <- metvar
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')
result <- pk_calculation(
  df = df, 
  met_vec = metvar, 
  models = mods, 
  grp_name = "Diet", 
  covariates = covariates
 )