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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.