msmtools introduces a fast and general method for restructuring classical longitudinal datasets into augmented ones. The reason for this is to facilitate the modeling of longitudinal data under a multi-state framework using the msm package.
# Install the released version from CRAN: install.packages("msmtools") # Install the development version from GitHub: devtools::install_github("contefranz/msmtools")
msmtools comes with 4 functions:
augment(): the main function of the package. This is the workhorse which
takes care of the data reshaping. It is very efficient and fast so highly
dimensional datasets can be processed with ease;
polish(): it helps in find and remove those transition which occur at the
same time but lead to different states within a given subject;
prevplot(): this is a plotting function which mimics the usage of
plot.prevalence.msm(), but with more things. Once you ran a
multi-state model, use this function to plot a comparison between observed and
survplot(): the aims of this function are double. You can use
as a plotting tool for comparing the empirical and the fitted survival curves.
Or you can use it to build and get the datasets used for the plot.
The function is based on msm
plot.survfit.msm(), but does more things and
it is considerably faster.
For more information about msmtools, please check out the vignette with
vignette( "msmtools" ).
Bugs and issues can be reported at https://github.com/contefranz/msmtools/issues.
msmtools has received a lot of improvements in the plotting functions. In particular, from
version 2.0.0 both
prevplot() support ggplot2.
This inevitably introduces
several breaking changes. Overall, both functions have been greatly simplified, but I encourage
to go over each function's documentation and the vignette to get a correct understanding on how they
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