The problem seems to be that when we add -1 to the formula, that creates an extra column in the model matrix that is not included in the regression coefficients. (This is a byproduct of the way that R creates factor codings.)
So I can work around this by adding a strategically placed coefficient of zero. We also have to fix up the covariance matrix the same way:
library(emmeans)
library(plm)
data("Males")
mod <- plm(wage ~ exper + residence + health + residence*union,
model = "within",
index = "nr",
data = Males)
BB <- c(coef(mod)[1], 0, coef(mod)[-1])
k <- length(BB)
VV <- matrix(0, nrow = k, ncol = k)
VV[c(1, 3:k), c(1, 3:k)] <- vcov(mod)
RG <- qdrg(~ -1 + exper + residence + health + residence*union,
data = Males, coef = BB, vcov = VV, df = df.residual(mod))
Verify that things line up:
> names(RG@bhat)
[1] "exper" ""
[3] "residencenorth_east" "residencenothern_central"
[5] "residencesouth" "healthyes"
[7] "unionyes" "residencenorth_east:unionyes"
[9] "residencenothern_central:unionyes" "residencesouth:unionyes"
> colnames(RG@linfct)
[1] "exper" "residencerural_area"
[3] "residencenorth_east" "residencenothern_central"
[5] "residencesouth" "healthyes"
[7] "unionyes" "residencenorth_east:unionyes"
[9] "residencenothern_central:unionyes" "residencesouth:unionyes"
They do line up, so we can get the results we need:
(EMM <- emmeans(RG, ~ residence * union))
residence union emmean SE df lower.CL upper.CL
rural_area no 0.378 0.0335 2677 0.31201 0.443
north_east no 0.330 0.1636 2677 0.00929 0.651
nothern_central no 0.192 0.1483 2677 -0.09834 0.483
south no 0.260 0.1514 2677 -0.03732 0.557
rural_area yes 0.287 0.1473 2677 -0.00144 0.576
north_east yes 0.385 0.1647 2677 0.06155 0.708
nothern_central yes 0.333 0.1539 2677 0.03091 0.634
south yes 0.341 0.1534 2677 0.04024 0.642
Results are averaged over the levels of: health
Confidence level used: 0.95
In general, the key is to identify where the added column occurs. It's going to be the position of the first level of the first factor in the model formula. You can check it by looking at names(coef(mod)) and colnames(model.matrix(formula), data = data) where formula is the model formula with intercept removed.
Update: a general function
Here's a function that may be used to create a reference grid for any plm object. It turns out that sometimes these objects do have an intercept (e.g., random-effects models) so we have to check. For models lacking an intercept, you really should use this only for contrasts.
plmrg = function(object, ...) {
form = formula(formula(object))
if (!("(Intercept)" %in% names(coef(object))))
form = update(form, ~ . - 1)
data = eval(object$call$data, environment(form))
mmat = model.matrix(form, data)
sel = which(colnames(mmat) %in% names(coef(object)))
k = ncol(mmat)
b = rep(0, k)
b[sel] = coef(object)
v = matrix(0, nrow = k, ncol = k)
v[sel, sel] = vcov(object)
emmeans::qdrg(formula = form, data = data,
coef = b, vcov = v, df = df.residual(object), ...)
}
Test run:
> (rg = plmrg(mod, at = list(exper = c(3,6,9))))
'emmGrid' object with variables:
exper = 3, 6, 9
residence = rural_area, north_east, nothern_central, south
health = no, yes
union = no, yes
> emmeans(rg, "residence")
NOTE: Results may be misleading due to involvement in interactions
residence emmean SE df lower.CL upper.CL
rural_area 0.313 0.0791 2677 0.1579 0.468
north_east 0.338 0.1625 2677 0.0190 0.656
nothern_central 0.243 0.1494 2677 -0.0501 0.536
south 0.281 0.1514 2677 -0.0161 0.578
Results are averaged over the levels of: exper, health, union
Confidence level used: 0.95
