These functions and methods provide an interface between emmeans and
the multcomp::glht function for simultaneous inference provided
by the multcomp package.
Arguments
- ...
In
emm, thespecs,by, andcontrarguments you would normally supply toemmeans. Onlyspecsis required. Otherwise, arguments are passed to other methods. You may also include awhichargument; see Details.- object
An object of class
emmGridoremm_list
Value
emm returns an object of an intermediate class for which
there is a multcomp::glht method.
as.glht returns an object of class glht or glht_list
according to whether object is of class emmGrid or emm_list.
See Details below for more on glht_lists.
Note
The multivariate-\(t\) routines used by glht require that all
estimates in the family have the same integer degrees of freedom. In cases
where that is not true, a message is displayed that shows what df is used.
The user may override this via the df argument.
Details for emm
emm is meant to be called only from "glht" as its second
(linfct) argument. It works similarly to multcomp::mcp,
except with specs (and optionally by and contr
arguments) provided as in a call to emmeans.
If the specifications in ... would result in a list (i.e., an
emm_list object), then by default, only the last element of that list
is passed to glht. However, if ... contains a which
argument consisting of integer values, the list elements with those indexes
are selected and combined and passed on to glht. No checking is done
on whether the indexes are valid, and the keyword which must be spelled-out.
Details for as.glht
When no by variable is in force, we obtain a glht object; otherwise
it is a glht_list. The latter is defined in emmeans, not multcomp,
and is simply a list of glht objects.
Appropriate convenience methods coef,
confint, plot, summary, and vcov are provided,
which simply apply the corresponding glht methods to each member.
Examples
if(require(multcomp, quietly = TRUE))
emm_example("glht-multcomp")
#>
#> Attaching package: ‘TH.data’
#> The following object is masked from ‘package:MASS’:
#>
#> geyser
#>
#> --- Running code from 'system.file("extexamples", "glht-multcomp.R", package = "emmeans")'
#>
#> > warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
#>
#> > summary(glht(warp.lm, emm(pairwise ~ tension | wool)))
#> $`wool = A`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: lm(formula = breaks ~ wool * tension, data = warpbreaks)
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L - M == 0 20.5556 5.1573 3.986 <0.001 ***
#> L - H == 0 20.0000 5.1573 3.878 <0.001 ***
#> M - H == 0 -0.5556 5.1573 -0.108 0.994
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>
#>
#> $`wool = B`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: lm(formula = breaks ~ wool * tension, data = warpbreaks)
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L - M == 0 -0.5556 5.1573 -0.108 0.994
#> L - H == 0 9.4444 5.1573 1.831 0.170
#> M - H == 0 10.0000 5.1573 1.939 0.139
#> (Adjusted p values reported -- single-step method)
#>
#>
#>
#> > summary(glht(warp.lm, emm(pairwise ~ tension | wool,
#> + which = 1:2, by = "wool")))
#> $`wool = A`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: lm(formula = breaks ~ wool * tension, data = warpbreaks)
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L, . == 0 44.5556 3.6468 12.218 < 0.001 ***
#> M, . == 0 24.0000 3.6468 6.581 < 0.001 ***
#> H, . == 0 24.5556 3.6468 6.734 < 0.001 ***
#> ., L - M == 0 20.5556 5.1573 3.986 0.00103 **
#> ., L - H == 0 20.0000 5.1573 3.878 0.00174 **
#> ., M - H == 0 -0.5556 5.1573 -0.108 0.99949
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>
#>
#> $`wool = B`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Fit: lm(formula = breaks ~ wool * tension, data = warpbreaks)
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L, . == 0 28.2222 3.6468 7.739 <0.001 ***
#> M, . == 0 28.7778 3.6468 7.891 <0.001 ***
#> H, . == 0 18.7778 3.6468 5.149 <0.001 ***
#> ., L - M == 0 -0.5556 5.1573 -0.108 0.999
#> ., L - H == 0 9.4444 5.1573 1.831 0.253
#> ., M - H == 0 10.0000 5.1573 1.939 0.210
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>
#>
#>
#> > warp.emm <- emmeans(warp.lm, ~tension | wool)
#>
#> > summary(as.glht(pairs(warp.emm)))
#> $`wool = A`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L - M == 0 20.5556 5.1573 3.986 <0.001 ***
#> L - H == 0 20.0000 5.1573 3.878 <0.001 ***
#> M - H == 0 -0.5556 5.1573 -0.108 0.994
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>
#>
#> $`wool = B`
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L - M == 0 -0.5556 5.1573 -0.108 0.994
#> L - H == 0 9.4444 5.1573 1.831 0.170
#> M - H == 0 10.0000 5.1573 1.939 0.139
#> (Adjusted p values reported -- single-step method)
#>
#>
#>
#> > summary(as.glht(pairs(warp.emm), by = NULL))
#>
#> Simultaneous Tests for General Linear Hypotheses
#>
#> Linear Hypotheses:
#> Estimate Std. Error t value Pr(>|t|)
#> L - M, A == 0 20.5556 5.1573 3.986 0.00129 **
#> L - H, A == 0 20.0000 5.1573 3.878 0.00183 **
#> M - H, A == 0 -0.5556 5.1573 -0.108 0.99996
#> L - M, B == 0 -0.5556 5.1573 -0.108 0.99996
#> L - H, B == 0 9.4444 5.1573 1.831 0.30796
#> M - H, B == 0 10.0000 5.1573 1.939 0.25532
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> (Adjusted p values reported -- single-step method)
#>
#>
# Use emm_example("glht-multcomp", list = TRUE) # to see just the code