This allows the user to incorporate results obtained by some analysis
into an emmGrid
object, enabling the use of emmGrid
methods
to perform related follow-up analyses.
Arguments
- bhat
Numeric. Vector of regression coefficients
- V
Square matrix. Covariance matrix of
bhat
- levels
Named list or vector. Levels of factor(s) that define the estimates defined by
linfct
. If not a list, we assume one factor named"level"
- linfct
Matrix. Linear functions of
bhat
for each combination oflevels
.- df
Numeric value or function with arguments
(x, dfargs)
. If a number, that is used for the degrees of freedom. If a function, it should return the degrees of freedom forsum(x*bhat)
, with any additional parameters indfargs
.- dffun
Overrides
df
if specified. This is a convenience to match the slot names of the returned object.- dfargs
List containing arguments for
df
. This is ignored if df is numeric.- post.beta
Matrix whose columns comprise a sample from the posterior distribution of the regression coefficients (so that typically, the column averages will be
bhat
). A 1 x 1 matrix ofNA
indicates that such a sample is unavailable.- nesting
Nesting specification as in
ref_grid
. This is ignored ifmodel.info
is supplied.- ...
Arguments passed to
update.emmGrid
Details
The arguments must be conformable. This includes that the length of
bhat
, the number of columns of linfct
, and the number of
columns of post.beta
must all be equal. And that the product of
lengths in levels
must be equal to the number of rows of
linfct
. The grid
slot of the returned object is generated
by expand.grid
using levels
as its arguments. So the
rows of linfct
should be in corresponding order.
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 an unsupported fitted model.
See also
qdrg
, an alternative that is useful when starting
with a fitted model not supported in emmeans.
Examples
# Given summary statistics for 4 cells in a 2 x 2 layout, obtain
# marginal means and comparisons thereof. Assume heteroscedasticity
# and use the Satterthwaite method
levels <- list(trt = c("A", "B"), dose = c("high", "low"))
ybar <- c(57.6, 43.2, 88.9, 69.8)
s <- c(12.1, 19.5, 22.8, 43.2)
n <- c(44, 11, 37, 24)
se2 = s^2 / n
Satt.df <- function(x, dfargs)
sum(x * dfargs$v)^2 / sum((x * dfargs$v)^2 / (dfargs$n - 1))
expt.rg <- emmobj(bhat = ybar, V = diag(se2),
levels = levels, linfct = diag(c(1, 1, 1, 1)),
df = Satt.df, dfargs = list(v = se2, n = n), estName = "mean")
plot(expt.rg)
( trt.emm <- emmeans(expt.rg, "trt") )
#> trt mean SE df lower.CL upper.CL
#> A 73.2 2.08 52.6 69.1 77.4
#> B 56.5 5.30 33.0 45.7 67.3
#>
#> Results are averaged over the levels of: dose
#> Confidence level used: 0.95
( dose.emm <- emmeans(expt.rg, "dose") )
#> dose mean SE df lower.CL upper.CL
#> high 50.4 3.08 12.0 43.7 57.1
#> low 79.3 4.79 31.4 69.6 89.1
#>
#> Results are averaged over the levels of: trt
#> Confidence level used: 0.95
rbind(pairs(trt.emm), pairs(dose.emm), adjust = "mvt")
#> contrast estimate SE df t.ratio p.value
#> A - B 16.8 5.69 23.23 2.941 0.0143
#> high - low -28.9 5.69 7.49 -5.084 0.0027
#>
#> Results are averaged over some or all of the levels of: dose, trt
#> P value adjustment: mvt method for 2 tests