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The aim of this document is to keep track of the changes made to the
different versions of the R package
ptmixed.
The numbering of package versions follows the convention a.b.c, where a and b are non-negative integers and c is a positive integer. When minor changes are made to the package, a and b are kept fixed and only c is increased. Major changes to the package, instead, are made apparent by changing a or b.
Each section of this document corresponds to a major change in the package - in other words, within a section you will find all those package versions a.b.x where a and b are fixed whereas x = 1, 2, 3, … Each subsection corresponds to a specific package version.
xlim argument to
make.spaghetti()xlab default in pmf()aod package (which is scheduled
to be archived by CRAN)NEWS file, which was not visible on
CRAN any moremake.spaghetti() function (rows with
NAs on either x or y do not cause
problems any more).checkmle() step in ptmixed() to
flag as not converged problematic cases on the boundary of the parameter
spacemake.spaghetti() code to restore
bty, mar and xpd values as they
were before the function callna.rm = T in computation of ylim
within make.spaghetti()simulate_ptglmmmargins and legend.space arguments
to make.spaghetti(). Added automatic sorting of provided
dataframe ( = no need to pre-sort it any more!)make.spaghetti();
cex.lab argument fixedptmixed(), ptglm(),
nbmixed() and nbglm() (wrt the id
and offset arguments). ranef() function
updated accordinglydf1, used in the
ptmixed() and nbmixed() help pages. Examples
in help pages revisedsimulate_ptglmm() function, to be used for
illustration purposes (in the vignettes)pmf() function to visualize the pmf of a discrete
variablemake.spaghetti(): fixed minor bug in that arose when
the col argument was specified + added
legend.inset argumentnpoints = 1 in
ptmixed() or nbmixed()). Note: use of the
Laplace is not recommended, because it is less accurate than the
adaptive GH, results in lower convergence rates and can yield biased
parameter estimates! We recommend using a sufficient number of
quadrature points (5 typically produces a good likelihood
approximation)make.spaghetti() function to create a spaghetti
plot / trajectory plot to visualize longitudinal datadf1silent argument to summary.ptglmm().
Furthermore, printed output table with parameter estimates and Wald test
is now presented with at most 4 decimalsptglm() and
nbglm()to print detailed optimization info also when
trace = Twald.test() to prevent
problems with future R release (4.0.0)freq.updates = 1 was set
in ptmixed() and nbmixed()ptmixed() and
nbmixed() improvedwald.test() function for computation of the
multivariate Wald testmaxit[1] == 0 within
ptglm() and nbglm() so as to make it possible
to skip BFGS optimization and go straight to Nelder-Meadsummary.ptglmm() and
summary.ptglm() (to verify that the smallest eigenvalue is
not too small)ptglm() function for the estimation of a
Poisson-Tweedie GLMnbmixed() and nbglm() functions for
the estimation of negative binomial GLMM and GLM using the
Poisson-Tweedie parametrization (negative binomial: a = 0)ptglmm for
objects obtained from ptmixed() and nbmixed(),
and ptglm for objects obtained from ptglm()
and nbglm(). Summary functions for objects of both classes
have been implementedmin.var.init argument added to
ptmixed()ptmixed() output changed from
ptmm to ptglmmsummary.ptglmm() function (the MLE of
the dispersion parameter was wrongly called “deviance” instead of
dispersion in the previous versions)ptmixed() is called, it first attempts
to maximize the loglikelihood with the Nelder-Mead algorithm and then,
if this fails, with the BFGS algorithm. Until version 0.0.4 the
quadrature points were updated at every iteration for both Nelder-Mead
and BFGS. Starting from this version, when Nelder-Mead is called it is
possible to update the positioning of the quadrature points every
n iterations by setting the freq.updates argument
equal to n. Default is set to freq.updates = 200
(this typically makes the optimization about 10 times faster than when
freq.updates = 1)ptmixed() now outputs extra information (number of
quadrature points used, initial values, warnings)trace = T in ptmixed()
functionmaxit[1] and/or maxit[2] are set = 0ptmixed() does not require the specification
of a time argument any moremaxit argument default value in function
ptmixed() increased to c(1e4, 100)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.