How can I get lsmeans () pairwise contrasts with custom vcov?
I would like to obtain paired comparisons of the adjusted means using lsmeans()
while still providing a robust covariance matrix of the coefficients (e.g. vcovHC
). Usually functions on regression models provide an argument vcov
, but I cannot find such an argument in the package lsmeans
.
Consider this dummy example, originally from CAR:
require(car)
require(lmtest)
require(sandwich)
require(lsmeans)
mod.moore.2 <- lm(conformity ~ fcategory + partner.status, data=Moore)
coeftest(mod.moore.2)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.197778 1.372669 7.4292 4.111e-09 ***
## fcategorymedium -1.176000 1.902026 -0.6183 0.539805
## fcategoryhigh -0.080889 1.809187 -0.0447 0.964555
## partner.statushigh 4.606667 1.556460 2.9597 0.005098 **
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
coeftest(mod.moore.2, vcov.=vcovHAC)
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.197778 0.980425 10.4014 4.565e-13 ***
## fcategorymedium -1.176000 1.574682 -0.7468 0.459435
## fcategoryhigh -0.080889 2.146102 -0.0377 0.970117
## partner.statushigh 4.606667 1.437955 3.2036 0.002626 **
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
lsmeans(mod.moore.2, list(pairwise ~ fcategory), adjust="none")[[2]]
## contrast estimate SE df t.ratio p.value
## low - medium 1.17600000 1.902026 41 0.618 0.5398
## low - high 0.08088889 1.809187 41 0.045 0.9646
## medium - high -1.09511111 1.844549 41 -0.594 0.5560
##
## Results are averaged over the levels of: partner.status
As you can see, lsmeans()
estimates the p values using the default variance-covariance matrix.
How to get pairwise contrasts using variance estimation vcovHAC
?
source to share
It turns out that there is a beautiful and seamless interface between packages lsmeans
and multcomp
(see ?lsm
), while it lsmeans
supports glht()
.
require(multcomp)
x <- glht(mod.moore.2, lsm(pairwise ~ fcategory), vcov=vcovHAC)
## Note: df set to 41
summary(x, test=adjusted("none"))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lm(formula = conformity ~ fcategory + partner.status, data = Moore)
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
## low - medium == 0 1.17600 1.57468 0.747 0.459
## low - high == 0 0.08089 2.14610 0.038 0.970
## medium - high == 0 -1.09511 1.86197 -0.588 0.560
## (Adjusted p values reported -- none method)
This is at least one way to achieve this. I still hope someone knows of an approach using only lsmeans
...
Another way to approach this is to hack into the object lsmeans
and manually replace the variance-covariance matrix to the summary
-object.
mod.lsm <- lsmeans(mod.moore.2, ~ fcategory)
mod.lsm@V <- vcovHAC(mod.moore.2) ##replace default vcov with custom vcov
pairs(mod.lsm, adjust = "none")
## contrast estimate SE df t.ratio p.value
## low - medium 1.17600000 1.574682 41 0.747 0.4594
## low - high 0.08088889 2.146102 41 0.038 0.9701
## medium - high -1.09511111 1.861969 41 -0.588 0.5597
##
## Results are averaged over the levels of: partner.status
source to share