f_fit_ki {drugDemand}  R Documentation 
Model Fitting for Number of Skipped Visits
Description
Fits a count model to the number of skipped visits between two consecutive drug dispensing visits.
Usage
f_fit_ki(df, model, nreps, showplot = TRUE)
Arguments
df 
The subjectlevel dosing data, including 
model 
The count model used to analyze the number of skipped visits, with options including "constant", "poisson", "zeroinflated poisson", and "negative binomial". 
nreps 
The number of simulations for drawing posterior model parameter values. 
showplot 
A Boolean variable that controls whether or not to
show the fitted count bar chart. It defaults to 
Value
A list with three components:

fit
: A list of results from the model fit that includes
model
: The specific model used in the analysis. 
theta
: The estimated model parameters. 
vtheta
: The estimated covariance matrix oftheta
. 
aic
: The Akaike Information Criterion value. 
bic
: The Bayesian Information Criterion value.


fit_plot
: A fitted count bar chart. 
theta
: Posterior draws of model parameters.
Author(s)
Kaifeng Lu, kaifenglu@gmail.com
Examples
library(dplyr)
observed < f_dose_observed(df2, visitview2, showplot = FALSE)
vf < observed$vf
vf < vf %>% left_join(dosing_schedule_df, by = "kit")
df_ti < vf %>%
mutate(time = lead(day)  day,
skipped = pmax(floor((time  target_days/2)/target_days), 0),
k1 = skipped + 1) %>%
filter(row_id < n())
ki_fit < f_fit_ki(df_ti, model = "zeroinflated poisson", nreps = 200)