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.