View source: R/f_dispensing_models.R
f_fit_ki | R Documentation |
Fits a count model to the number of skipped visits between two consecutive drug dispensing visits.
f_fit_ki(df, model, nreps, showplot = TRUE)
df |
The subject-level dosing data, including |
model |
The count model used to analyze the number of skipped visits, with options including "constant", "poisson", "zero-inflated 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 |
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 of theta
.
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.
Kaifeng Lu, kaifenglu@gmail.com
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 = "zero-inflated poisson", nreps = 200)
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