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r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-222- `estHook`; `vcovHook`; `postGridHook` -->
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd:223:Most linear models supported by **emmeans** have straightforward structure: Regression coefficients, their covariance matrix, and a set of linear functions that define the reference grid. However, a few are more complex. An example is the `clm` class in the **ordinal** package, which allows a scale model in addition to the location model. When a scale model is used, the scale parameters are included in the model matrix, regression coefficients, and covariance matrix, and we can't just use the usual matrix operations to obtain estimates and standard errors. To facilitate using custom routines for these tasks, the `emm_basis.clm` function function provided in **emmeans** includes, in its `misc` part, the names (as character constants) of two "hook" functions: `misc$estHook` has the name of the function to call when computing estimates, standard errors, and degrees of freedom (for the `summary` method); and `misc$vcovHook` has the name of the function to call to obtain the covariance matrix of the grid values (used by the `vcov` method). These functions are called in lieu of the usual built-in routines for these purposes, and return the appropriately sized matrices.
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-224-
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd:225:In addition, you may want to apply some form of special post-processing after the reference grid is constructed. To provide for this, give the name of your function to post-process the object in `misc$postGridHook`. Again, `clm` objects (as well as `polr` in the **MASS** package) serve as an example. They allow a `mode` specification that in two cases, calls for post-processing. The `"cum.prob"` mode uses the `regrid` function to transform the linear predictor to the cumulative-probability scale. And the `"prob"` mode performs this, as well as applying the contrasts necessary to convert the cumulative probabilities into the class probabilities.
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-226-
##############################################
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-244- names, `base::all.vars` will probably not give you what you need. For
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd:245: example, if `form = ~ data$x + data[[5]]`, then `base::all.vars(form)`
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-246- returns the names `"data"` and `"x"`, whereas `emmeans::.all.vars(form)`
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd:247: returns the names `"data$x"` and `"data[[5]]"`. The `retain` argument may be
r-cran-emmeans-1.5.2-1/vignettes/xtending.Rmd-248- used to specify regular expressions for patterns to retain as parts of
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-54-| | | |`what = c("location", "scale", "shape", "rate", "prob.gt.0")`|
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:55:|gamm |mgcv |[G](#G) |`call = object$gam$call` |
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-56-|Gam |gam |[G](#G) |`nboot = 800` |
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-76-| | | |`emm_options(lmer.df =, pbkrtest.limit =, disable.pbkrtest =)` |
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:77:|lqm,lqmm |lqmm |[A](#A) |`tau = "0.5"` (must match an entry in `object$tau`) |
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-78-|manova |stats |[M](#M) |`mult.name`, `mult.levs` |
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-82-| | | |`mode = c("default", "multinomial")`; `data` is required |
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:83:|mira |mice |[I](#I) |Optional arguments per class of `$analyses` elements |
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-84-|mixed |afex |[P](#P) |Supported in **afex** package |
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-187-`mgcv::vcov.gam()`. Both default to `FALSE`. The value of `unconditional`
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:188:matters only if `freq = FALSE` and `object$Vc` is non-null.
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-189-
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:190:For `mgcv::gamm` objects, `emmeans()` results are based on the `object$gam`
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-191-part. Unfortunately, that is missing its `call` component, so the user
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-193-or give the dataset in the `data` argument. Alternatively (and recommended),
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:194:you may first set `object$gam$call` to the quoted call ahead of time.
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-195-The `what` arguments are used to select which model formula to use:
##############################################
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-429-In `MCMCglmm` objects, the `data` argument is required; however, if you
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd:430:save it as a member of the model object (e.g., `object$data = quote(mydata)`),
r-cran-emmeans-1.5.2-1/vignettes/models.Rmd-431-that removes the necessity of specifying it in each call.
##############################################
r-cran-emmeans-1.5.2-1/R/helpers.R-360-# class(conLin) = class(obj)
r-cran-emmeans-1.5.2-1/R/helpers.R:361:# X = model.matrix(eval(call$fixed), data = data)
r-cran-emmeans-1.5.2-1/R/helpers.R-362-# y = data[[all.vars(call)[1]]]
##############################################
r-cran-emmeans-1.5.2-1/R/helpers.R-386- class(conLin) = class(obj)
r-cran-emmeans-1.5.2-1/R/helpers.R:387: X = model.matrix(eval(call$model), data = data)
r-cran-emmeans-1.5.2-1/R/helpers.R-388- y = data[[all.vars(call)[1]]]
##############################################
r-cran-emmeans-1.5.2-1/R/helpers.R-437- data = if(is.null(misc$data))
r-cran-emmeans-1.5.2-1/R/helpers.R:438: eval(object$call$data, parent.frame(2))
r-cran-emmeans-1.5.2-1/R/helpers.R-439- else
##############################################
r-cran-emmeans-1.5.2-1/R/helpers.R-711- if (!is.null(args$data)) {
r-cran-emmeans-1.5.2-1/R/helpers.R:712: data = eval(args$data, parent.frame())
r-cran-emmeans-1.5.2-1/R/helpers.R-713- trms = terms(model.frame(fcall$formula, data = data))
##############################################
r-cran-emmeans-1.5.2-1/R/qdrg.R-167- if (!is.null(object$link)) {
r-cran-emmeans-1.5.2-1/R/qdrg.R:168: misc = .std.link.labels(eval(list(link = object$link)), misc)
r-cran-emmeans-1.5.2-1/R/qdrg.R-169- dffun = function(k, dfargs) Inf
##############################################
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-346- if (is.null(data) && !is.null(object$data)) # allow for including data in object
r-cran-emmeans-1.5.2-1/R/MCMC-support.R:347: data = eval(object$data)
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-348- # if a multivariate response, stack the data with `trait` variable
##############################################
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-463- }
r-cran-emmeans-1.5.2-1/R/MCMC-support.R:464: trms = delete.response(terms(eval(cl$formula, parent.frame())))
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-465- recover_data(cl, trms, NULL, data, ...)
##############################################
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-505- cl = call("carbayes.proxy", formula = object$formula, data = quote(data))
r-cran-emmeans-1.5.2-1/R/MCMC-support.R:506: trms = delete.response(terms(eval(object$formula, parent.frame())))
r-cran-emmeans-1.5.2-1/R/MCMC-support.R-507- recover_data(cl, trms, NULL, data, ...)
##############################################
r-cran-emmeans-1.5.2-1/R/lqm-support.R-25- fcall = object$call
r-cran-emmeans-1.5.2-1/R/lqm-support.R:26: trms = delete.response(terms(eval(fcall$fixed)))
r-cran-emmeans-1.5.2-1/R/lqm-support.R-27- recover_data(fcall, trms, object$mfArgs$na.action, data = data, ...)
##############################################
r-cran-emmeans-1.5.2-1/NEWS.md-94- * Fixed error in `emmeans(obj, "1", by = "something")` (#197)
r-cran-emmeans-1.5.2-1/NEWS.md:95: * `eff_size()` now supports `emm_list` objects with a `$contrasts`
r-cran-emmeans-1.5.2-1/NEWS.md-96- component, using those contrasts. This helps those who
##############################################
r-cran-emmeans-1.5.2-1/NEWS.md-399-
r-cran-emmeans-1.5.2-1/NEWS.md:400: * Support for model formulas such as `df$y ~ df$treat + df[["cov"]]`. This had
r-cran-emmeans-1.5.2-1/NEWS.md-401- failed previously for two obscure reasons, but now works correctly.
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-222- `estHook`; `vcovHook`; `postGridHook` -->
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd:223:Most linear models supported by **emmeans** have straightforward structure: Regression coefficients, their covariance matrix, and a set of linear functions that define the reference grid. However, a few are more complex. An example is the `clm` class in the **ordinal** package, which allows a scale model in addition to the location model. When a scale model is used, the scale parameters are included in the model matrix, regression coefficients, and covariance matrix, and we can't just use the usual matrix operations to obtain estimates and standard errors. To facilitate using custom routines for these tasks, the `emm_basis.clm` function function provided in **emmeans** includes, in its `misc` part, the names (as character constants) of two "hook" functions: `misc$estHook` has the name of the function to call when computing estimates, standard errors, and degrees of freedom (for the `summary` method); and `misc$vcovHook` has the name of the function to call to obtain the covariance matrix of the grid values (used by the `vcov` method). These functions are called in lieu of the usual built-in routines for these purposes, and return the appropriately sized matrices.
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-224-
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd:225:In addition, you may want to apply some form of special post-processing after the reference grid is constructed. To provide for this, give the name of your function to post-process the object in `misc$postGridHook`. Again, `clm` objects (as well as `polr` in the **MASS** package) serve as an example. They allow a `mode` specification that in two cases, calls for post-processing. The `"cum.prob"` mode uses the `regrid` function to transform the linear predictor to the cumulative-probability scale. And the `"prob"` mode performs this, as well as applying the contrasts necessary to convert the cumulative probabilities into the class probabilities.
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-226-
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-244- names, `base::all.vars` will probably not give you what you need. For
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd:245: example, if `form = ~ data$x + data[[5]]`, then `base::all.vars(form)`
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-246- returns the names `"data"` and `"x"`, whereas `emmeans::.all.vars(form)`
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd:247: returns the names `"data$x"` and `"data[[5]]"`. The `retain` argument may be
r-cran-emmeans-1.5.2-1/inst/doc/xtending.Rmd-248- used to specify regular expressions for patterns to retain as parts of
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-54-| | | |`what = c("location", "scale", "shape", "rate", "prob.gt.0")`|
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:55:|gamm |mgcv |[G](#G) |`call = object$gam$call` |
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-56-|Gam |gam |[G](#G) |`nboot = 800` |
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-76-| | | |`emm_options(lmer.df =, pbkrtest.limit =, disable.pbkrtest =)` |
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:77:|lqm,lqmm |lqmm |[A](#A) |`tau = "0.5"` (must match an entry in `object$tau`) |
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-78-|manova |stats |[M](#M) |`mult.name`, `mult.levs` |
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-82-| | | |`mode = c("default", "multinomial")`; `data` is required |
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:83:|mira |mice |[I](#I) |Optional arguments per class of `$analyses` elements |
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-84-|mixed |afex |[P](#P) |Supported in **afex** package |
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-187-`mgcv::vcov.gam()`. Both default to `FALSE`. The value of `unconditional`
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:188:matters only if `freq = FALSE` and `object$Vc` is non-null.
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-189-
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:190:For `mgcv::gamm` objects, `emmeans()` results are based on the `object$gam`
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-191-part. Unfortunately, that is missing its `call` component, so the user
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-193-or give the dataset in the `data` argument. Alternatively (and recommended),
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:194:you may first set `object$gam$call` to the quoted call ahead of time.
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-195-The `what` arguments are used to select which model formula to use:
##############################################
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-429-In `MCMCglmm` objects, the `data` argument is required; however, if you
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd:430:save it as a member of the model object (e.g., `object$data = quote(mydata)`),
r-cran-emmeans-1.5.2-1/inst/doc/models.Rmd-431-that removes the necessity of specifying it in each call.
##############################################
r-cran-emmeans-1.5.2-1/debian/tests/run-unit-test-5-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-emmeans-1.5.2-1/debian/tests/run-unit-test:6: AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-cran-emmeans-1.5.2-1/debian/tests/run-unit-test-7- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM