emmGrid object from scratchThis 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.
emmobj(bhat, V, levels, linfct = diag(length(bhat)), df = NA, dffun,
dfargs = list(), post.beta = matrix(NA), nesting = NULL, ...)An emmGrid object
Numeric. Vector of regression coefficients
Square matrix. Covariance matrix of bhat
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"
Matrix. Linear functions of bhat for each combination
of levels.
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 for sum(x*bhat), with any additional
parameters in dfargs.
Overrides df if specified. This is a convenience
to match the slot names of the returned object.
List containing arguments for df.
This is ignored if df is numeric.
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 of NA indicates that
such a sample is unavailable.
Nesting specification as in ref_grid. This is
ignored if model.info is supplied.
Arguments passed to update.emmGrid
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.
qdrg, an alternative that is useful when starting
with a fitted model not supported in emmeans.
# 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") )
( dose.emm <- emmeans(expt.rg, "dose") )
rbind(pairs(trt.emm), pairs(dose.emm), adjust = "mvt")
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