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survival
package
version 3.0conf_level
argument that can set the width of
the confidence intervals for the predicted cumulative hazardsautoplot()
method on Linux systemsnlm()
to
optimize()
, which is generally more stable in these cases.
The reason why I do not switch all the time top optimize()
is because that one cannot be programmed with. Then, in combination with
numDeriv::hessian
, estimation would take forever and be
basically impossible to check.zph
option in emfrail_control()
so that the result of the cox.zph
for the frailty model is
also returned. This can be used to for goodness of fit. A guide on that
soon to come!Major update. Now stratified models are supported! Several improvements in the documentation and in the performance section.
Smaller fixes, as compared to the previoius CRAN release:
rev(cumsum(rev(rowsum)))
statement and replaced
with an Rcpp function rowsum_vec
solve
,
seems that this is way better for symmetric matrices (0.7.16)emfrail_control()
function
(0.7.15)summary()
that control what is
printed (if you want the output of a package to fit on one slide, for
example) (0.7.15)ca_test
that was not reading correctly
the input because of the partial matching of arguments in R
(0.7.14)As compared to the previous CRAN release, 0.7.2: - fixed a bug where
the estimation would go wrong when the data set was not ordered
according to the cluster - fixed a bug where emfrail
would
crash when the cluster colum would be a character vector - fixed a bug
where the test for dependent censoring would not work - part of the
output is now nicer (e.g. the frail
vector is named, the
autoplot.emfrail()
gives a nicer plot) - removed a bunch of
redundant calculations and old pieces of code - minor corrections in the
vignette
As compared to the previous CRAN release, 0.7.0:
ca_test()
now provides an interface to use the
Commenges-Andersen test for heterogeneity outside the
emfrail()
function. It takes as input a coxph
object. Therefore, it can work with other baseline hazard estimators and
with strata.As usual, feedback is welcome.
ca_test()
: no more model frame
needed, works well with strata.ca_test()
, a small bug that was leading to
wrong answers sometimes. Now it should give the sam result as the one in
emfrail
.ca_test()
now works for coxph
models
properly as long as they have covariatesemfrail
.coxph
objects. Basically
this is also done in emfrail()
, but now you can also use
strata
or other things that are not supported by
emfrail().
emfrail_dist()
rather than
emfrail_distribution()
emfrail
objects.predict.emfrail
method suffered some alterations:
first of all, it now gives predictions for each lp
or each
row of newdata
, and it also gained the argumnet
individual
. If true, then the newdata
argument
is taken as coming from the same individual. This can be used with
time-dependent covariates and adjusting the time at risk.emfrail
object type has been re-vamped into a more
conventional objectemfrail(formula, data, stuff)
phrasing of the main fitting
function..formula
or .data
arguments are still used.plot.emfrail()
and autoplot.emfrail()
(for
ggplot2
).control
argument and the
emfrail_control()
functionsummary()
, plots using ggplot2
, and numerous
bug fixes.optimize
+ numDeriv
to
nlm
ggplot_emfrail()
added! Now the same plots (and more)
can be done with the good looking ggplot2
engine.summary.emfrail()
now has a new argument
print_opts
that is used in
print.emfrail_summary()
; if the output becomes too big,
then some parts of the output may be ommittedemfrail_control()
and the .control
argument.theta
. This should be tuned somehow in the
future. The problem lies in the M step where agreg.fit
can’t deal with large offset values.TODO: - recover lost features in this update: measures of dependence
in summary.emfrail
, first of all - bring back the fast fit
for inverse gaussian or… who knows, maybe now - document
emfrail_control
properly - update vignette
Likelihood based confidence intervals are here!
Removed the maximization by optimx
and doing it with
optimize()
, since it’s one dimensional. A hessian estimate
is obtained from numDeriv()
.
Minor bug fixes
Some big changes in how the confidence intervals are constructed in predict.emfrail. Now - they are first constructed with the delta method for the log(cumulative hazard) and then exponentiated, so they do not have to be truncated at 0 or 1 any more.
Further improved compatibility with CRAN policies and added a bunch
of stuff in the examples in \dontrun
statements (now they
should be less than 5 seconds runtime)
Improved compatibility with R-devel 3.4.0. Registered C++ files to get rid of an R CMD check NOTE. Small modifications in the C++ file - for some reason a segfault started happening out of nowhere, think it’s fixed now.
Added vignette, fixed small things for R CMD check R CMD check: PASS, 0 warnings, 1 note / about new developer, that’s ok.
Added the Commenges-Andersen test for heterogeneity. The test is
implemented in a pretty non-efficient way, and it can be skipped with a
proper emfrail_control()
call, see
?emfrail_control
. Also there I added an option to
just calculate the test, instead of doing anything else, and
then just that is returned. A nice idea would be to implement this as a
post-hoc calculation for coxph
objects but that seems like
another project atm.
R CMD check: PASS, 0 warnings, 0 notes.
Changed name to the more professional frailtyEM
. Added
CI and SE for Kendall’s tau with gamma
bugfixes: CI for tau with stable is now ok
Added a newdata
option for the predict
method and for the plot
methods. This can be used instead
of lp
, and basically calculates the corresponding linear
predictor for the given covariate values.
bugfixes
There is an option now to calculate the unadjusted SE or no SE at all
?plot_emfrail
NEWS.md
file to track changes to the
package.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.