pltree to allow option of modelling log-worth with a linear predictor (via pladmm()).pladmm(), including possibility to specify contrasts for any factors in the formula.weights argument to pladmm(), allowing aggregated rankings to be modelled, optionally using an aggregate_rankings object to specify rankings and weights together.predict.PLADMM(vcov = FALSE) and AIC when new data specified (partial fix to #50).pladmm function to fit the Plackett-Luce model with log-worth modelled by item covariates.beans data. The planting date, and geographical coordinates (@kauedesousa, #41).all.equal().vcov.PlackettLuce() works again for ref = NULL (bug introduced with vcov method in version 0.2-4)asplit()read.soi() and read.toi() now handle incomplete rankings with very irregular lengths correctly.read.*() functions for Preflib formats now give a meaningful error when the file or URL does not exist, and a warning if the file is corrupt.as.rankings with input = "orderings" now checks coded values can be matched to item names, if provided.PlackettLuce() now works with nspeudo > 0 when there are no observed paired comparisons.?PlackettLuce now gives advice on analysing data with higher order ties.as.rankings.matrix() introduced in version 0.2-7.eigs from RSpectra vs rARPACK."aggregated_rankings" object to store aggregated rankings with the corresponding frequencies. Objects of class "rankings" can be aggregated via the aggregate method; alternatively rankings() and as.rankings() will create an "aggregated_rankings" object when aggregate = TRUE. as.rankings() also handles pre-aggregated data, accepting frequencies via the freq argument.freq() function to extract frequencies from aggregated rankings.as.rankings() can now create a "grouped_rankings" object, if a grouping index is passed via the index argument.as.matrix() methods for rankings and aggregated rankings to extract the underlying matrix of rankings, with frequencies in the final column if relevant. This means rankings can be saved easily with write.table().complete() and decode() functions to help pre-process orderings before converting to rankings, complete() infers the item(s) in r’th rank given the items in the other (r - 1) ranks. decode() converts coded (partial) orderings to orderings of the items in each ordering.read.soi(), read.toc() and read.toi() to read the corresponding PrefLib file formats (for data types “Strict Orders - Incomplete List”, “Orders with Ties - Complete List” and “Orders with Ties - Incomplete List”). An as.aggregated_rankings() method is provided to convert the data frame of aggregated orderings to an "aggregated_rankings" object.pltree() now respects na.action and will pad predictions and fitted values for na.action = "na.exclude" if the rankings are missing for a whole group or one of the model covariates has a missing value.PlackettLuce() now has an na.action argument for handling of missing rankings.fitted() and choices() now return data frames, with list columns as necessary.rankings() now sets redundant/inconsistent ranks to NA rather than dropping them. This does not affect the final ranking, unless it is completely NA.read.soc() is now named Freq rather than n."item" attribute of the data frame returned by read.soc() is now named "items".labels argument in as.rankings() has been deprecated and replaced by items.grouped_ranking() has been deprecated and replaced by group().nascar data have been dropped.isFALSE() for compatibility with R < 3.5.vcov() for CRAN Windows test machine.PlackettLuce() now supports MAP estimation with a multivariate normal prior on log-worths and/or a gamma prior on ranker adherence.PlackettLuce() now returns log-likelihood and degrees of freedom for the null model (where all outcomes, including ties, have equal probability).vcov method for Plackett-Luce trees.itempar.PlackettLuce() now always returns a matrix, even for a single node tree.pltree() or PlackettLuce() with grouped rankings now work correctly with weights."PlackettLuce" and "summary.PlacketLuce" objects now respect options("width").fitted always returns n which is now weighted count of rankings (previously only returned unweighted count with argument aggregate = TRUE).AIC.pltree to work on "pltree" object with one node.AIC.pltree to enable computation of AIC on new observations (e.g. data held out in cross-validation).fitted.pltree to return combined fitted probabilities for each choice within each ranking, for each node in a Plackett-Luce tree.vcov.PlackettLuce now works for models with non-integer weights (fixes #25).plot.pltree now works for worth = TRUE with psychotree version 0.15-2 (currently pre-release on https://r-forge.r-project.org/R/?group_id=330)PlackettLuce and plfit now work when start argument is set.itempar.PlackettLuce now works with alias = FALSEplot.PlackettLuce method so that plotting works for a saved "PlackettLuce" objectbeans data (which has been updated).?PlackettLuce and new package?PlackettLuce. (Fixes #14 and #21).maxit defaults to 500 in PlackettLuce.steffensen argument).coef.pltree() now respects log = TRUE argument (fixes #19).[.grouped_rankings] now works for replicated indices.pudding, nascar and beans.pltree() function for use with partykit::mob(). Requires new objects of type "grouped_rankings" that add a grouping index to a "rankings" object and store other derived objects used by PlackettLuce. Methods to print, plot and predict from Plackett-Luce tree are provided.connectivity() function to check connectivity of a network given adjacency matrix. New adjacency() function computes adjacency matrix without creating edgelist, so remove as.edgelist generic and method for `“PlackettLuce” objects.as.data.frame methods so that rankings and grouped rankings can be added to model frames.format methods for rankings and grouped_rankings, for pretty printing.[ methods for rankings and grouped_rankings, to create valid rankings from selected rankings and/or items.itempar method for “PlackettLuce” objects to obtain different parameterizations of the worth parameters.read.soc function to read Strict Orders - Complete List (.soc) files from https://www.preflib.org.Old behaviour should be reproducible with arguments
npseudo = 0, steffensen = 0, start = c(rep(1/N, N), rep(0.1, D))where N is number of items and D is maximum order of ties.
ref argument from PlackettLuce; should be specified instead when calling coef, summary, vcov or itempar.qvcalc generic now imported from qvcalcPlackettLuce.log argument to coef so that worth parameters (probability of coming first in strict ranking of all items) can be obtained easily.