emmobj {emmeans} | R Documentation |
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, df = NA, dffun, dfargs = list(), post.beta = matrix(NA), ...)
bhat |
Numeric. Vector of regression coefficients |
V |
Square matrix. Covariance matrix of |
levels |
Named list or vector. Levels of factor(s) that define the
estimates defined by |
linfct |
Matrix. Linear functions of |
df |
Numeric value or function with arguments |
dffun |
Overrides |
dfargs |
List containing arguments for |
post.beta |
Matrix whose columns comprise a sample from the posterior
distribution of the regression coefficients (so that typically, the column
averages will be |
... |
Arguments passed to |
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.
An emmGrid
object
# 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")