This function may make it possible to compute a reference grid for a model object that is otherwise not supported.
Usage
qdrg(formula, data, coef, vcov, df, mcmc, object, subset, weights, contrasts,
link, qr, ordinal, ...)
Arguments
- formula
Formula for the fixed effects
- data
Dataset containing the variables in the model
- coef
Fixed-effect regression coefficients (must conform to formula)
- vcov
Variance-covariance matrix of the fixed effects
- df
Error degrees of freedom
- mcmc
Posterior sample of fixed-effect coefficients
- object
Optional model object. This rarely works!; but if provided, we try to set other arguments based on an expectation that `object` has a similar structure to `lm` objects. See Details.
- subset
Subset of
data
used in fitting the model- weights
Weights used in fitting the model
- contrasts
List of contrasts specified in fitting the model
- link
Link function (character or list) used, if a generalized linear model. (Note: response transformations are auto-detected from
formula
)- qr
QR decomposition of the model matrix; used only if there are
NA
s incoef
.- ordinal
list
with elementsdim
andmode
.ordinal$dim
(integer) is the number of levels in an ordinal response. Ifordinal
is provided, the intercept terms are modified appropriate to predicting an ordinal response, as described invignette("models")
, Section O, usingordinal$mode
as themode
argument (if not provided,"latent"
is assumed). (All modes are supported except `scale`) For this to work, we expect the firstordinal$dim - 1
elements ofcoef
to be the estimated threshold parameters, followed by the coefficients for the linear predictor.- ...
Optional arguments passed to
ref_grid
Details
Usually, you need to provide either object
; or
formula
, coef
, vcov
, data
, and perhaps other
parameters. It is usually fairly straightforward to figure out how to get
these from the model object
; see the documentation for the model class that
was fitted. Sometimes one or more of these quantities contains extra parameters,
and if so, you may need to subset them to make everything conformable. For a given formula
and data
,
you can find out what is needed via colnames(model.matrix(formula, data))
.
(However, for an ordinal model, we expect the first ordinal.dim - 1
coefficients
to replace (Intercept)
. And for a multivariate model, we expect coef
to be a matrix with these row names, and vcov
to have as many rows and columns as
the total number of elements of coef
.)
If your model object follows fairly closely the conventions of an lm
or glm
object, you may be able to get by providing the model as object
,
and perhaps some other parameters to override the defaults.
When object
is specified, it is used as detailed below to try to obtain the
other arguments. The user should ensure that the defaults
shown below do indeed work.
The default values for the arguments are as follows:
formula
:formula(object)
data
:recover_data.lm(object)
is tried, and if an error is thrown, we also checkobject$data
.coef
:coef(object)
vcov
:vcov(object)
df
: Set toInf
if not available indf.residual(object)
mcmc
:object$sample
subset
:NULL
(so that all observations indata
are used)contrasts
:object$contrasts
The functions qdrg
and emmobj
are close cousins, in that
they both produce emmGrid
objects. When starting with summary
statistics for an existing grid, emmobj
is more useful, while
qdrg
is more useful when starting from a fitted model.
Note
For backwards compatibility, an argument ordinal.dim
is invisibly
supported as part of ...
, and if present, sets
ordinal = list(dim = ordinal.dim, mode = "latent")
Rank deficiencies
Different model-fitting packages take different approaches when the model
matrix is singular, but qdrg
tries to reconcile them by comparing the
linear functions created by formula
to coefs
and vcov
.
We may then use the estimability package to determine what quantities
are estimable. For reconciling to work properly, coef
should be named
and vcov
should have dimnames. To disable this name-matching
action, remove the names from coef
, e.g., by calling unname()
.
No reconciliation is attempted in multivariate-response cases. For more
details on estimability, see the documentation in the estimability
package.
See also
emmobj
for an alternative way to construct an emmGrid
.
Examples
# In these examples, use emm_example(..., list = TRUE) # to see just the code
if (require(biglm, quietly = TRUE))
emm_example("qdrg-biglm")
#>
#> --- Running code from 'system.file("extexamples", "qdrg-biglm.R", package = "emmeans")'
#>
#> > bigmod <- biglm(log(conc) ~ source + factor(percent),
#> + data = pigs)
#>
#> > rg1 <- qdrg(log(conc) ~ source + factor(percent),
#> + data = pigs, coef = coef(bigmod), vcov = vcov(bigmod), df = bigmod$df.residual)
#>
#> > emmeans(rg1, "source", type = "response")
#> source response SE df asymp.LCL asymp.UCL
#> fish 29.8 1.09 Inf 27.7 32.0
#> soy 39.1 1.47 Inf 36.4 42.1
#> skim 44.6 1.75 Inf 41.2 48.1
#>
#> Results are averaged over the levels of: percent
#> Confidence level used: 0.95
#> Intervals are back-transformed from the log scale
#>
if(require(coda, quietly = TRUE) && require(lme4, quietly = TRUE))
emm_example("qdrg-coda")
#>
#> Attaching package: ‘lme4’
#> The following object is masked from ‘package:nlme’:
#>
#> lmList
#>
#> --- Running code from 'system.file("extexamples", "qdrg-coda.R", package = "emmeans")'
#>
#> > post <- readRDS(system.file("extdata", "cbpplist",
#> + package = "emmeans"))$post.beta
#>
#> > rg2 <- qdrg(~size + period, data = lme4::cbpp, mcmc = post,
#> + link = "logit")
#>
#> > summary(rg2, type = "response")
#> size period response lower.HPD upper.HPD
#> 15 1 0.1930 0.1214 0.288
#> 15 2 0.0836 0.0398 0.137
#> 15 3 0.0748 0.0369 0.129
#> 15 4 0.0489 0.0138 0.101
#>
#> Point estimate displayed: median
#> Results are back-transformed from the logit scale
#> HPD interval probability: 0.95
#>
if(require(ordinal, quietly = TRUE))
emm_example("qdrg-ordinal")
#>
#> --- Running code from 'system.file("extexamples", "qdrg-ordinal.R", package = "emmeans")'
#>
#> > wine.clm <- clm(rating ~ temp * contact, data = wine)
#>
#> > ref_grid(wine.clm)
#> 'emmGrid' object with variables:
#> temp = cold, warm
#> contact = no, yes
#>
#> > qdrg(object = wine.clm, ordinal.dim = 5)
#> 'emmGrid' object with variables:
#> temp = cold, warm
#> contact = no, yes
#>