This is a maintenance update. There are no major updates worth of notice besides few tweaks in the vignette which was not rendered appropriately.
msmtools sees several updates which come with potential breaking changes due to the dropping of several arguments in some functions. The most relevant feature being shipped with v2.0.0 is that both survplot() and prevplot() now support ggplot2. All in all, this justifies the jump in versions thus bringing msmtools to version 2.0.0.
survplot() requires much less arguments now but at the same time is a bit less flexible. This is particularly reflected in plot customization which is now “self-imposed”. I summarize what’s different w.r.t. v1.3 below:
gg and ggplot object.survplot() returns nothing but the rendered plot by default. The user can tell the function to return additional objects like the fitted data, the Kaplan-Meier data km, or one of them with the argument out.survplot() always returns a data.table when out requires such object.survplot() twice with different input parameters and the combine the plots afterwards. The underlying data structures for the plot are always returned and made available through a gg/ggplot object.prevplot() has been greatly improved. There are no more loops in the function and everything is done by reference so it is very efficient. I briefly summarize what’s different w.r.t. v1.3 below:
prevplot() always returns a gg/ggplot object.prevalence.msm().facet_wrap() to automatically decide how many grobs to render. For now, there is no customization possible since I expect that for most cases the number of states is limited in number anyway. There might be some flexibility added in future releases.M = TRUE, prevplot() uses patchwork to wrap the two plots in two different rows. The first row has the standard prevalence plot, the second row has the Deviance M.survplot() lets you specify the type of confidence intervals for the Kaplan-Meier in addition to the already available types for the fitted curve. The argument is ci_km.out which takes a vector of characters.survplot() always renders the plot and returns it.prevplot() drops plot layering in favor of direct ggplot2 support.prevplot() has become faster due to smarter data extraction and binding.polish() is introduced. This adds support in the preprocessing part of the analysis. polish() addresses the specific problem of different transitions occurring at the same exact time within a given subject. This is a case for which a multi-state model fails to estimate the probability associated with the two transitions. At the moment, the whole subject specified by data_key is deleted.Global variables are now correctly declared on top of functions using utils::globalVariables(). This prevents the assignment to NULL in the preamble of functions which decreases the elegance of the code.
The printing of information is now way more simpler and is not based on sink() anymore. This has been done to be less intrusive into the OS when redirecting on console messages. Also, no more OS type check is done so that the control with argument verbose is the most general possible. Warnings are still controlled as usual, so that they get printed, if any, just after the function call.
The vignette has been updated to include new features and it is now in HTML format which provides a faster and lighter access.
Improved the documentation’s readability in the ‘Value’ section for augment() and survplot().
In the documentation of augment() now there is an explicit example which returns a data.frame.
Windows builds of msmtools are now constantly checked through Appveyor.
The author/maintainer e-mail has changed to match his new affiliation and now is francesco.grossetti@unibocconi.it.
After augment() has been run, results are now visible at the very first call. This means that you can finally print on console the augmented dataset right away.
pandoc versions prior 1.17 does not fully support spaces in file names and caused a warning when compiling msmtools under Fedora using both clang and gcc. Now all file names are without spaces. msmtools 1.3 has been built using pandoc 1.19.2 and pandoc-citeproc 0.10.4.1
msmtools can now run with R 3.0.0 and above for retro compatibility reasons.
augment() gains the new argument check_NA which allows the user to decide if the function should run some checks to find missing data in the following arguments: data_key, n_events, pattern, t_start and t_end. Default is FALSE. Missing data checks are always carried out on more_status.
augment() gains the new argument convert which if set to TRUE efficiently converts the output to the old school data.frame class.
survplot() gains the new argument return.all which saves you some typing time when requesting both the data of the Kaplan-Meier and the fitted survival. Arguments return.km and return.p now are set to NULL by default instead of FALSE.
survplot() gains the new argument convert which if set to TRUE efficiently converts any object returned to the old school data.frame class.
augment() now also accepts an object of class data.frame as input. If so, the function internally converts it to a data.table.
augment() now accepts t_augmented without quotes too. Default name is still “augmented”.
augment() gets a whole new implementation which comes into play when pattern has only 2 values (‘alive’ and ‘dead’). Now the procedure runs with computational time only slightly longer than the standard 3 values in pattern. This is due thanks to the fast joins method adopted.
augment() now is much faster when defining the target size for the reshaping. This was a bottleneck which caused memory issues and wasted time.
General memory optimization in the function augment(). Now the function uses ~ 30% less memory.
All the functions now have more detailed and better written helps.
Some minor changes in the vignette to encapsulate new functionalities.
In augment(), the sequential status is now correctly computed. There was a wrong call which blocked the object defined by n_events.
In augment(), when pattern was detected with two unique values, inconsistent results were produced during the status flag assignment. This was due to a wrong rounding of the amount of augmenting factor for each unit.
augment() now is way faster then in v1.0 thanks to a new implementation when defining patterns.
augment() now uses the faster uniqueN() to extract the number of unique values in a vector.
augment() now correctly positions new created variables.augment() in-line help now provides correct information on what it returns.