NEWS.md
broom.helpers (>= 1.15.0) and gtsummary (>= 2.0.0) in DESCRIPTION to accord with an update in {gtsummary}.convergence.Rmd vignette to not run any actual code using other packages to fix issue on CRAN where not all packages are available on all platforms. Now the results are hard-coded in place.makeObsID() where table() was sorting the results stored in reps, which has to be manually undone. Fixes #50.predict() method to use the interval and level arguments of more standard predict() methods.ci() function.logit_probs() function.adjustFactorLevels() where the levels_orig object was being accidentally overwritten.adjustFactorLevels() to checkFactorLevels().tidy(), glance(), and augment() methods for use with the {broom} package.model.matrix.logitr(), terms.logitr(), and model.frame.logitr() methods for use with {broom.helpers}.panelID and clusterUD variables a sequentially increasing numeric vector and to stop the program if there are any repeated IDs in these variables.obsID variable a sequentially increasing numeric vector (this was previously done but was accidentally removed in prior updates).modelSpace variable along inside the vcov() method.runtimes, was included, which is exported from the colab notebook used for benchmarking here: https://colab.research.google.com/drive/1vYlBdJd4xCV43UwJ33XXpO3Ys8xWkuxx?usp=sharing
drawType argument.price to scalePar to be more general.randPrice to randScale to be more general.modelSpace argument is no longer required for specifying a WTP space model as it is redundant. Including a scalePar argument is enough to determine that it is a WTP space model.correlation argument to include correlated heterogeneity.obsID and outcome arguments.vcov.logitr() method now returns object$vcov if the user set vcov = TRUE during estimation (avoids a redundant calculation of vcov, which is more efficient).apolloModeChoiceData, electricity
fquantile() function, which is a faster implementation of the stats::quantile() function.predict.logitr() method was added for making probability and choice predictions from logitr class objects.predictProbs() and predictChoices() functions were depreciated.fitted.logitr() and residuals.logitr() methods.predict argument to the main logitr() function which controls whether predicted probabilities, fitted.values, and residuals are included in the returned object. Default setting is TRUE.se.logitr() method.vcov argument to the logitr() function.options argument and are now passed directly as arguments to logitr(). These include: numMultiStarts, useAnalyticGrad, scaleInputs, startParBounds, standardDraws, numDraws, startVals. The options argument is now only used for options to control the optimization handled by nloptr().logitr() function were changed to make them easier to understand: choiceName became choice, obsIDName became obsID, parNames became pars, priceName became price, weightsName became weights, clusterName became cluster. If used, old names will be passed to the new argument names and a warning will be displayed.print.logitr(), logLik.logitr(), coef.summary.logitr(), vcov.logitr(), terms.logitr()
summary.logitr() and coef.logitr() methods for better printing, now using printCoefmat().wtp() and wtpCompare() functionsaltIDName argument to predictChoices() and predictProbs() to preserve the row order of predictions for each alternative in each set of alternatives. Closes issue #13.Major changes were made to the gradient functions, which dramatically improved computational efficiency. MNL and MXL models in either preference or WTP spaces now use the faster implementation of the logit calculations.
This version was the first implementation of an alternative approach for computing the logit probabilities, which increased computational speed. Specifically, the formulation was to compute P = 1 / (1 + sum(exp(V - V_chosen)))
The vcov() method was modified such that it computes the covariance post model estimation. Previously, the covariance matrix was being computed internally in the logitr() function, and vcov() just returned this value, which was computationally much slower.
Several breaking changes in this version.
choiceName –> choice, obsIDName –> obsID, parNames –> pars, priceName –> price, weightsName –> weights, clusterName –> cluster.options argument and are now passed directly as arguments to logitr(). These include: numMultiStarts, useAnalyticGrad, scaleInputs, startParBounds, standardDraws, numDraws, startVals.summary.logitr() and coef.logitr() methods for better printing, using printCoefmat()
print.logitr(), logLik.logitr(), coef.summary.logitr(), vcov.logitr()
wtp() and wtpCompare() functionsaltIDName argument to predictChoices() and predictProbs() to preserve the row order of predictions for each alternative in each set of alternatives. Closes issue #13.predictChoices() and predictProbs(), and , depreciated simulateShares().recodeData() function to improve encoding efficiency.dummyCode()
predictChoices() function.predictShares() function, depreciating simulateShares().recodeData() and dummyCode() functions for improved speed.simulateShares() to work with the automatic dummy coding from the revised recodeData() and dummyCode() functions.simulateShares() to compute shares for multiple sets of alternatives.simulateShares(), the shares were not correctly computed with a WTP space model because price was still being multiplied by -1. This has been corrected.standardDraws() functiongetCatVarDummyNames() - previously used string matching, which can accidentally match with other similarly-named variables.rowsum() where the reorder argument was set to TRUE, which resulted in wrong logit calculations unless the obsID happened to be already sorted.algorithm to the options input, with the default being set to "NLOPT_LD_LBFGS".getParTypes() function - previously was not returning the correct parNames for continuous vs. discrete variables."pref" or "wtp".priceName argument is only used when the modelSpace argument is set to "wtp".Weighted models, new dataset, new encoding features
yogurt, cars_china, cars_us
dummyCode() function for automatically creating dummy-coded variables in a data frame.New simulation functionality
simulateShares(). This is similar to the predict() function in mlogit.options() function. I found this just far too confusing, and instead encourage users to supply a WTP space model with the computed WTP from a preference space model as starting parameters.Updates to options and a few small bug fixes
logitr.summary() function and instead added the logitr class to all the models and renamed the summary function to summary.logitr(). Now you can just use the standard summary() function to summarize model results.TRUE.Full reboot of logitr!
Long overdue, I decided to give the logitr program a full overhaul. This is the first version that is compiled as a proper R package that can be directly installed from GitHub. This version is much more robust and flexible than the prior, clunky collection of R files that I had previously been using to estimate logit models.