These functions provide methods for rbind and
[ that may be used to combine emmGrid objects
together, or to extract a subset of cases. The primary reason for
doing this would be to obtain multiplicity-adjusted results for smaller
or larger families of tests or confidence intervals.
Usage
# S3 method for class 'emmGrid'
rbind(..., deparse.level = 1, adjust = "bonferroni")
# S3 method for class 'emmGrid'
e1 + e2
# S3 method for class 'emmGrid'
x[i, adjust, drop.levels = TRUE, ...]
# S3 method for class 'emmGrid'
head(x, n = 6, ...)
# S3 method for class 'emmGrid'
tail(x, n = 6, ...)
# S3 method for class 'emmGrid'
subset(x, subset, ...)
# S3 method for class 'emm_list'
rbind(..., which, adjust = "bonferroni")
# S3 method for class 'summary_emm'
rbind(..., which)
force_regular(object)Arguments
- ...
In
rbind, object(s) of classemmGridorsummary_emm. In others, additional arguments passed to other methods- deparse.level
(required but not used)
- adjust
Character value passed to
update.emmGrid- e1, e2, x, object
Objects of class
emmGrid- i
Integer vector of indexes
- drop.levels
Logical value. If
TRUE, the"levels"slot in the returned object is updated to hold only the predictor levels that actually occur- n
integer number of entries to include (or exclude if negative)
- subset
logical expression indicating which rows of the grid to keep
- which
Integer vector of subset of elements to use; if missing, we use all elements.
Value
A revised object of class emmGrid
The result of e1 + e2 is the same as rbind(e1, e2)
The rbind method for emm_list objects simply combines
the emmGrid objects comprising the first element of ....
Note that the returned object is not yet summarized, so any adjust
parameters apply to the combined emmGrid.
The rbind method for summary_emm objects (or a list thereof)
returns a single summary_emm object. This combined object
preserves any adjusted P values or confidence limits in the
original summaries, since those quantities have already been computed.
force_regular adds extra (invisible) rows to an emmGrid object
to make it a regular grid (all combinations of factors). This regular structure is
needed by emmeans. An object can become irregular by, for example,
subsetting rows, or by obtaining contrasts of a nested structure.
Note
rbind throws an error if there are incompatibilities in
the objects' coefficients, covariance structures, etc. But they
are allowed to have different factors; a missing level '.'
is added to factors as needed.
These functions generally reset by.vars to NULL;
so if you want to keep any “by” variables, you should follow-up
with update.emmGrid.
Examples
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
warp.rg <- ref_grid(warp.lm)
# Do all pairwise comparisons within rows or within columns,
# all considered as one faily of tests:
w.t <- pairs(emmeans(warp.rg, ~ wool | tension))
t.w <- pairs(emmeans(warp.rg, ~ tension | wool))
rbind(w.t, t.w, adjust = "mvt")
#> tension wool contrast estimate SE df t.ratio p.value
#> L . A - B 16.333 5.16 48 3.167 0.0206
#> M . A - B -4.778 5.16 48 -0.926 0.9119
#> H . A - B 5.778 5.16 48 1.120 0.8258
#> . A L - M 20.556 5.16 48 3.986 0.0019
#> . A L - H 20.000 5.16 48 3.878 0.0027
#> . A M - H -0.556 5.16 48 -0.108 1.0000
#> . B L - M -0.556 5.16 48 -0.108 1.0000
#> . B L - H 9.444 5.16 48 1.831 0.3795
#> . B M - H 10.000 5.16 48 1.939 0.3192
#>
#> P value adjustment: mvt method for 9 tests
update(w.t + t.w, adjust = "fdr") ## same as above except for adjustment
#> tension wool contrast estimate SE df t.ratio p.value
#> L . A - B 16.333 5.16 48 3.167 0.0080
#> M . A - B -4.778 5.16 48 -0.926 0.4614
#> H . A - B 5.778 5.16 48 1.120 0.4022
#> . A L - M 20.556 5.16 48 3.986 0.0014
#> . A L - H 20.000 5.16 48 3.878 0.0014
#> . A M - H -0.556 5.16 48 -0.108 0.9147
#> . B L - M -0.556 5.16 48 -0.108 0.9147
#> . B L - H 9.444 5.16 48 1.831 0.1319
#> . B M - H 10.000 5.16 48 1.939 0.1314
#>
#> P value adjustment: fdr method for 9 tests
# Show only 3 of the 6 cases
summary(warp.rg[c(2, 4, 5)])
#> wool tension prediction SE df
#> B L 28.2 3.65 48
#> B M 28.8 3.65 48
#> A H 24.6 3.65 48
#>
# After-the-fact 'at' specification
subset(warp.rg, wool == "A") ## or warp.rg |> subset(wool == "A")
#> wool tension prediction SE df
#> A L 44.6 3.65 48
#> A M 24.0 3.65 48
#> A H 24.6 3.65 48
#>
### Working with 'emm_list' objects
mod <- lm(conc ~ source + factor(percent), data = pigs)
all <- emmeans(mod, list(src = pairwise ~ source, pct = consec ~ percent))
rbind(all, which = c(2, 4), adjust = "mvt")
#> src.contrast pct.contrast estimate SE df t.ratio p.value
#> fish - soy . -9.47 2.33 23 -4.059 0.0028
#> fish - skim . -15.58 2.39 23 -6.526 <.0001
#> soy - skim . -6.11 2.34 23 -2.613 0.0783
#> . percent12 - percent9 6.36 2.47 23 2.570 0.0852
#> . percent15 - percent12 1.96 2.57 23 0.763 0.9397
#> . percent18 - percent15 3.31 3.04 23 1.088 0.7944
#>
#> Results are averaged over some or all of the levels of: percent, source
#> P value adjustment: mvt method for 6 tests
### Irregular object
tmp <- warp.rg[-1]
## emmeans(tmp, "tension") # will fail because tmp is irregular
emmeans(force_regular(tmp), "tension") # will show some results
#> Warning: emmeans() results may be corrupted by removal of a nesting structure
#> NOTE: Results may be misleading due to involvement in interactions
#> tension emmean SE df lower.CL upper.CL
#> L nonEst NA NA NA NA
#> M 26.4 2.58 48 21.2 31.6
#> H 21.7 2.58 48 16.5 26.9
#>
#> Results are averaged over the levels of: wool
#> Confidence level used: 0.95