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.
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)