generate_f_function {gammaFuncModel} | R Documentation |
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
)
[Package gammaFuncModel version 5.0 Index]