msmtools
package
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 msm() function
plot.prevalence.msm(), but with more things. Once you ran a
multi-state model, use this function to plot a comparison between
observed and expected prevalences;
survplot(): the aims of this function are double.
You can use survplot() 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
survplot() and 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 work.