Merge branch 'main' of https://github.com/mbg-unsw/Rwtdttt

README.md

@@ -19,12 +19,32 @@ The parametric Waiting Time Distribution has been developed to provide a data-dr
 
 This implementation of estimation procedures mimics that found in the corresponding Stata package (wtdttt). The model is fit using maximum likelihood estimation.
 
+## Example
+
+```R
+library(haven)
+df <- read_dta(system.file("extdata", "ranwtddat_discdates.dta", package="Rwtdttt"))
+
+fit_r <- ranwtdttt(data = df,
+                   rxdate ~ dlnorm(logitp, mu, lnsigma),
+                   id = "pid",
+                   start = as.Date('2014-01-01'),
+                   end = as.Date('2014-12-31'),
+                   reverse = T, robust = F
+)
+
+summary(fit_r)
+```
+Please see [examples.R](sandpit/examples.R) for more.
+
 ## More
 
 ### Known bugs
 
 Robust variance calculation, used by default in the randwtdttt() function, is very slow. We are working on improving this.
 
+Other planned improvements are listed in [TODO](sandpit/TODO)
+
 ### Bug reports, feature requests etc.
 
 This is a young project with some rough edges. Please submit any bug reports, feature request and comments as a Github issue.
@@ -40,6 +60,8 @@ The package was developed by:
 * Malcolm Gillies
 * Olga Paoletti
 
+Thank you to Jesper Hallas for kindly allowing us to reproduce the synthetic dispensing data in the `drugpakud.dta` dataset.
+
 ### License
 
 Rwtdttt is licensed under the GNU General Public License, Version 3.

sandpit/try2.R → sandpit/examples.R

@@ -1,7 +1,7 @@
 # Quick test
 
 library(bbmle)
-library(Rwtdttt)
+#library(Rwtdttt)
 library(haven)
 library(data.table)
 library(tidyverse)
@@ -71,7 +71,6 @@ summary(fit1)
 df <- haven::read_dta(system.file("extdata", "wtddat_covar.dta", package="Rwtdttt"))
 
 ### fit waiting time distribution (exp)
-# XXXX need to change wtdttt(... subset= ...) to use non-standard evaluation
 fit3 <- wtdttt(data = df,
                last_rxtime ~ dexp(logitp, lnbeta),
                start = 0, end = 1, reverse = T, subset = packsize==200