f_fit_ti {drugDemand}  R Documentation 
Model Fitting for Gap Times
Description
Fits a linear regression model to the gap time between two consecutive drug dispensing visits.
Usage
f_fit_ti(df, model, nreps, showplot = TRUE)
Arguments
df 
The subjectlevel dosing data, including the following variables:

model 
The model used to analyze the gap time. Options include "least squares" and "least absolute deviations". 
nreps 
The number of simulations for drawing posterior model parameter values. 
showplot 
A Boolean variable that controls whether or not to
show the fitted gap time 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. 
beta
: The estimated regression coefficient for the covariate. 
vbeta
: The estimated variance ofbeta
. 
sigma
: The estimated residual standard deviation. 
df
: The residual degreesoffreedom. 
aic
: The Akaike Information Criterion value. 
bic
: The Bayesian Information Criterion value.


fit_plot
: A fitted gap time 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())
ti_fit < f_fit_ti(df_ti, model = "least squares", nreps = 200)