stanreg objects (#202)emmip() to be consistent between one curve and several, in whether points are displayed (style option)"scale" option to make.tran()emtrends() (#201)trt.vs.ctrl.emmc() now throws an error (#208)linfct (the identity) to emmobjemm_options "sep" and "parens", and a parens argument in contrast(). sep controls how factor levels are combined when ploted or contrasted, and parens sets whether, what, and how labels are parenthesized in contrast(). In constructing contrasts of contrasts, for example, labels like A - B - C - D are now (A - B) - (C - D), by default. To reproduce old labeling, do `emm_options(sep = “,”, parens = “a^”)pwpp() so it plays nice with nonestimable cases"xplanations" vignette with additional documentation on methods used. (comparison arrows, for starters)plot(), especially regarding comparison arrowsstanreg models (#196)emmeans(obj, "1", by = "something") (#197)eff_size() now supports emm_list objects with a $contrasts component, using those contrasts. This helps those who specify pairwise ~ treatment.contrast() for factor combinations with by groups were wacky (#199)emtrends() screwed up with multivariate models (#200).calc to summary(). For example, calc = c(n = ~.wgt.) will add a column of sample sizes to the summary.coxph support for models with strataemmeans() with specs of class list now passes any offset and trend arguments (#179)plim argument to pwpp() to allow controlling the scaleparams (#180)gls objects when data are incomplete (#181)joint_tests() and test(..., joint = TRUE) that can occur with nontrivial @dffun() slots (#184)gls (#185) and renamed boot-satterthwaite to appx-satterthwaite (#176)transform argument in ref_grid() so it is same as in regrid() (#188)pwpm() function for displaying estimates, pairwise comparisons, and P values in matrix form.all.vars() that addresses #170scheffe.rank in summary.emmGrid() to manually specify the desired dimensionality of a Scheffe adjustment (#171)... to be included in options in calls to emmeans() and contrast(). This allows passing any summary() argument more easily, e.g., emmeans(..., type = "response", bias.adjust = TRUE, infer = c(TRUE, TRUE)) (Before, we would have had to wrap this in summary())plotit argument to plot.emmGrid() that works similarly to that in emmip().character predictors inat` (#175)emmeans() associated with non-factors such as Date (#162)nesting.order option to emmip() (#163)style argument for emmip() allows plotting on a numeric scalepwpp() has tick marks on P-value axis (#167)regrid() for error when estimates exceed boundsformula.tools:::as.character.formula messes me up (thanks to Berwin Turloch, UWA, for alerting me)dqrg() more visible in the documentation (because it’s often useful)emm_list objects, e.g. rbind() and as.data.frame(), as.list(), and as.emm_list()"bcnPower" option to make.tran() (per car::bcnPower())emmtrends() (#153)... to hook functions (need exposed by #154)regrid() whereby we can fake any response transformation – not just "log" (again inspired by #154)merMod objects) (#157)pwpp() to make extremely small P values more distinguishableemtrends() is now object, not model, to avoid potential mis-matching of the latter with optional mode argumentemtrends() now uses more robust and efficient code whereby a single reference grid is constructed containing all needed values of var. The old version could fail, e.g., in cases where the reference grid involves post-processing. (#145)scale argument to contrast()"identity" contrast methodeff_size() function for Cohen effect sizescov.keep argument in ref_grid() for specifying covariates to be treated just like factors (#148). A side effect is that the system default for indicator variables as covariates is to treat them like 2-level factors. This could change the results obtained from some analyses using earlier versions. To replicate old analyses, set emm_options(cov.keep = character(0)).regrid ignored offsets with Bayesian models; emtrends() did not supply options and misc arguments to emm_basis() (#143)stanreg in particular (#114)max.degree argument in emtrends() making it possible to obtain higher-order trends (#133). Plus minor tuneups, e.g., smaller default increment for difference quotientsemmeans() more forgiving with ’byvariables; e.g.,emmeans(model, ~ dose | treat, by = “route”)will find bothbyvariables whereas previously“route”` would be ignored.emm_basis() and recover_data() methods are used in preference to internal ones - so package developers can provide improvements over what I’ve cobbled together.recover_data() failscontrast() in identifying true contrasts (#134)plot.summary_emm() regarding CIs and intervals (#137)log(y + 1) ~ ... and 2*sqrt(y + 0.5) ~ ... are now auto-detected. [This may cause discrepancies with examples in past usages, but if so, that would be because the response transformation was previously incorrectly interpreted.]ratios argument to contrast() to decide how to handle log and logittype = "response" but there is no way to back-transform them (or we opted out with ratios = FALSE).emm_register() to make it easier for other packages to register their emmeans support methodsinfer, explaining that Bayesian models are handled differently (#128)PIs option to plot.emmGrid() and emmip() (#131). Also, in plot.emmGrid(), the intervals argument has been changed to CIs for sake of consistency and less confusion; intervals is still supported for backaward compatibility.plot.emmGrid gains a colors argument so we can customize colors used.glht support (#132 contributed by Balsz Banfai)regrid gains sim and N.sim arguments whereby we can generate a fake posterior sample from a frequentist model.gls objects with non-matrix apVar member (#119)sigma argument to ref_grid() (defaults to sigma(object) if available)interval argument in predict.emmGrid()likelihood argument in as.mcmc to allow for simulating from the posterior predictive distributionsigma in objectcld() and CLD()exclude (#107)recover_data to emm_basisMCMCglmm supportdo.call(paste, ...) and do.call(order, ...), to prevent problems with factor names like method that are argument names for these functions (#94)summary.emmGrid() whereby transformations of class list were ignored.update.emmGrid(..., levels = levs) whereby we can easily relabel the reference grid and ensure that the grid and roles slots stay consistent. Added vignette example.emmeans(). We now ensure that the original order of the reference grid is preserved. Previously, the grid was re-ordered if any numeric or character levels occurred out of order, per order()CLD() due to its misleading display of pairwise-comparison tests.betareg objects, where the wrong terms component was sometimes used.by variables are present (#98).pwpp() function to plot P values of comparisonssummary(..., adjust = "scheffe"). We now actually compute and use the rank of the matrix of linear functions to obtain the F numerator d.f., rather than trying to guess the likely correct value.contrast() results if they are later used by emmeans(). This was first noticed with ordinal models in prob mode (#83).sommer::mmer, MuMIn::averaging, and mice::mira objectsnnet::multinom support when there are 2 outcomes (#19)gls objectsfamSize now correct when exclude or include is used in a contrast function (see #68)aovList objects, in part due to the popularity of afex::aov_ez() which uses these models.emm_options(opt.digits = FALSE)include argument to most .emmc functions (#67)ref, exclude, and include in .emmc functions (#68)... arguments in emmeans() when two-sided formulas are presentclm support when model is rank-deficientregrid(..., transform = "log") error when there are existing non-estimable cases (issue #65)brmsfit support (#43)mgcv::gam and mgcv::gamm models.my.vcov() now passes ... to clientsmanova object no longer requires data keyword (#72)aovlist models (#73)CLD fatal error when sort = TRUE (#77)lme objects (#75)"mvt" adjustment ignored by groupingcontrast() mis-labeled estimates when levels varied among by groups (most prominently this happened in CLD(..., details = TRUE))aovlist support so it re-fits the model when non-sum-to-zero contrasts were usedprint.summary_emm() now cleans up numeric columns with zapsmall()nesting in ref_grid() and update(), and addition of covnest argument for whether to include covariates when auto-detecting nestinghpd.summary() and handoff to it from summary()ref_grid() ignored mult.levs... where it shouldn’tCLD() now works for MCMC models (uses frequentist summary)opt.digits optionref.grid() put to final rest, and we no longer support packages that provide recover.data or lsm.basis methods.recover_data() and .emm_basis() to provide access for extension developers to all available methodsinst/extdata.all.vars() that could cause errors when response variable has a function call with character constants.regrid() (so results match summary() labeling with type = "response").plot.emmGrid(..., comparisons = TRUE, type = "response") produced incorrect comparison arrows; now fixeddf$y ~ df$treat + df[["cov"]]. This had failed previously for two obscure reasons, but now works correctly.simplify.names option for above types of modelsemm_options() with no arguments now returns all options in force, including the defaults. This makes it more consistent with options()emtrends(); produced incorrect results in models with offsets.update.emmGrid() and emm_options()qdrg() function (quick and dirty reference grid) for help with unsupported model objectscld() has been deprecated in favor of CLD(). This had been a headache. multcomp is the wrong place for the generic to be; it is too fancy a dance to export cld with or without having multcomp installed.xtending.Rmd vignette on how to export methodsrevpairwise.emmc and cld regarding comparing only 1 EMMcld.emm_list now returns results only for object[[ which[1] ]], along with a warning message.emmeans specs like cld ~ group, a vestige of lsmeans as it did not work correctly (and was already undocumented)Suggests (dozens and dozens fewer dependencies)lme models in “models” vignette.emmc functions (#22)exclude argument to most .emmc functions: allows user to omit certain levels when computing contrastshpd.summary() function for Bayesian models to show HPD intervals rather than frequentist summary. Note: summary() automatically reroutes to it. Also plot() and emmip() play along.nlme::lme modelsSurv() was interpreted as a response transformation.cld() is applied to an emm_list (issue #24)offset argument to ref_grid() (scalar offset only) and to emmeans() (vector offset allowed) – (issue #18)[.summary_emm to choose whether to retain its class or coerce to a data.frame (relates to issue #14)reverse option for trt.vs.ctrl and relatives (#27)terms is accessed with lme objects to make it more robustemmeans:::convert_scripts() renames output file more simply[ method for class summary_emmsimple argument for contrast - essentially the complement of byjoint_tests()ref_grid() accept ylevs list of length > 1; also slight argument change: mult.name -> mult.namesemmeans() wherein weights was ignored when specs is a listdata argument, if supplied to a data.frame (recover_data() doesn’t like tibbles…)as.data.frame method for emmGrid objects, making it often possible to pass it directly to other functions as a data argument.contrast() where by was ignored for interaction contrastsas.glht() where it choked on df = Infdata or subsetjoint_tests() function tests all [interaction] contrastsgamlss objects (but doesn’t support smoothing). Additional argument is what = c("mu", "sigma", "nu", "tau") It seems to be flaky when the model of interest is just ~ 1.emmeans() might pass data to contrast()summary.emmGrid()emm_options(summary = ...) to work as advertised.emmGrid() function to emm() as had been intended as alternative to mcp() in multcomp::glht() (result of ditto).cld.emm_list()Inf to display d.f. for asymptotic (z) tests. (NA will still work too but Inf is a better choice for consistency and meaning.)recover_data() now throws an error when it finds recovered data not reproduciblevcov() calls to comply with recent R-devel changesThis is the initial major version that replaces the lsmeans package. Changes shown below are changes made to the last real release of lsmeans (version 2.27-2). lsmeans versions greater than that are transitional to that package being retired.
emmeans(), emtrends(), emmip(), etc. But lsmeans(), lstrends(), etc. as well as pmmeans() etc. are mapped to their corresponding emxxxx() functions.ref.grid -> ref_grid, lsm.options -> emm_options, etc.ref.grid and lsmobj are gone. Both are replaced by class emmGrid. An as.emmGrid() function is provided to convert old objects to class emmGrid.lmerMod models. Also added options disable.lmerTest and lmerTest.limit, similar to those for pbkrtest.neuralgia and pigs datasetsemmmeans() methods is now top-down rather than convoluted intermingling of S3 methods-s in labels to /s to emphasize that thnese results are ratios.ref_grid. (Can be disabled via emm_options())plot() and emmip() are now ggplot2-based. Old lattice-based functionality is still available too, and there is a graphics.engine option to choose the default.Suggests pkgs to Enhances when not needed for building/testingNew developments will take place in emmeans, and lsmeans will remain static and eventually will be archived.