[R-sig-ME] wider than expected confidence intervals with lsmeans and predict.glmmadmb
Lenth, Russell V
russell-lenth at uiowa.edu
Sun May 28 06:00:56 CEST 2017
If the SE of a mean is exactly 1/2 the SE of the difference of two means -- which is almost never the case -- it would be appropriate to use overlapping confidence intervals to test comparisons of means. So, you should almost never try to do that. In mixed models, it is not at all unusual to have huge discrepancies among standard errors.
However, 'lsmeans' does offer an ad hoc method for the graphical comparisons you have in mind. Try this:
lsm.TMB<- lsmeans(m.nb2, ~FoodTreatment)
plot(lsm.TMB, comparisons = TRUE)
This will plot both confidence intervals (in blue) and "comparison arrows" (in red). Non-overlapping comparison arrows will indicate cases where differences are significant. You can have just the comparison arrows by using:
plot(lsm.TMB, intervals = FALSE, comparisons = TRUE)
In either case, as I say, it is an ad hoc method, and it doesn't always work, especially when there are widely variable standard errors. A warning is issued if it can't figure out a solution.
Russ
--
Russell V. Lenth - Professor Emeritus
Department of Statistics and Actuarial Science
The University of Iowa - Iowa City, IA 52242 USA
Voice (319)335-0712 - FAX (319)335-3017
russell-lenth at uiowa.edu - http://www.stat.uiowa.edu/~rlenth/
-----Original Message-----
Date: Fri, 26 May 2017 17:29:52 -0400
From: Evan Palmer-Young <ecp52 at cornell.edu>
To: John Maindonald <john.maindonald at anu.edu.au>
Cc: R-mixed models mailing list <r-sig-mixed-models at r-project.org>
Subject: Re: [R-sig-ME] wider than expected confidence intervals with
lsmeans and predict.glmmadmb
Message-ID:
<CAAge6+7v1KY=8GLNqi1Hzg4zyQY0kfSjGvMXM-rhRFC9ER8Kcw at mail.gmail.com>
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Thanks very much for your reply, Prof. Maindonald.
I agree that the pairwise comparisons are informative, but it would be easiest for readers to see the data on the original scale to show differences between groups.
When the lsmeans are plotted from glmmTMB, which fits a model with fixed effects identical to those in glmmADMB, the estimates are identical but the SE's differ by a factor of 8.
So I am still confused about why the lsmeans plots would reflect pairwise differences with some packages but not with glmmADMB.
In my experience, lsmeans plots of group means from glmer() models are also non-overlapping when pairwise comparisons are highly significant.
I have extended the code to illustrate the differences.
library(glmmADMB)
library(lsmeans)
#Use data from worked example
#http://glmmadmb.r-forge.r-project.org/glmmADMB.html
library(glmmADMB)
data(Owls)
str(Owls)
Owls <- transform(Owls,
Nest=reorder(Nest,NegPerChick),
logBroodSize=log(BroodSize),
NCalls=SiblingNegotiation)
m.nb<- glmmadmb(NCalls~FoodTreatment+ArrivalTime+
+(1|Nest),
data=Owls,
zeroInflation=FALSE,
family="nbinom")
summary(m.nb)
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) 4.2674 0.4705 9.07 < 2e-16 ***
# FoodTreatmentSatiated -0.2602 0.0845 -3.08 0.0021 **
# ArrivalTime -0.0840 0.0190 -4.42 9.8e-06 ***
#Plot lsmeans by FoodTreatment
owls.lsm<-lsmeans(m.nb, ~FoodTreatment)
owls.lsm
# FoodTreatment lsmean SE df asymp.LCL asymp.UCL
# Deprived 2.188727 0.7205142 NA 0.7765454 3.600909
# Satiated 1.928499 0.7498151 NA 0.4588887 3.398110
#SE is much higher than for fixed effects in model
plot(owls.lsm)
#95% confidence bands overlap almost entirely
#Confirm with predict.glmmadmb:
New.data<-expand.grid(FoodTreatment= levels(Owls$FoodTreatment),
ArrivalTime = mean(Owls$ArrivalTime))
New.data$NCalls <- predict(m.nb, New.data, re.form=NA, SE.fit = TRUE)
#Get standard errors:
calls.pred<- predict(m.nb, New.data, re.form = NA, se.fit = TRUE)
calls.pred<-data.frame(calls.pred)
New.data$SE<-calls.pred$se.fit
New.data
# FoodTreatment ArrivalTime NCalls SE
# 1 Deprived 24.75763 2.188727 0.7205142
# 2 Satiated 24.75763 1.928499 0.7498151
#Matches with lsmeans output
################## Compare to glmmTDMB ####################
#install.packages("glmmTMB")
library(glmmTMB)
m.nb2<- glmmTMB(NCalls~FoodTreatment+ArrivalTime+
+(1|Nest),
data=Owls,
family="nbinom2")
summary(m.nb2)
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) 4.91011 0.63343 7.752 9.07e-15 ***
# FoodTreatmentSatiated -0.69238 0.10692 -6.476 9.44e-11 ***
# ArrivalTime -0.11540 0.02526 -4.569 4.90e-06 ***
#Compare to glmmADMB model:Fixed effects are identical
summary(m.nb)
# Estimate Std. Error z value Pr(>|z|)
# (Intercept) 4.9101 0.6334 7.75 9.1e-15 ***
# FoodTreatmentSatiated -0.6924 0.1069 -6.48 9.4e-11 ***
# ArrivalTime -0.1154 0.0253 -4.57 4.9e-06 ***
#Plot lsmeans by FoodTreatment
owls.lsm<-lsmeans(m.nb2, ~FoodTreatment) #oops, lsmeans can't use glmmTMB object!
######## Interlude #######
#Ben Bolker wrote a function to talk to lsmeans-- incredible!
# https://github.com/glmmTMB/glmmTMB/issues/205
recover.data.glmmTMB <- function(object, ...) {
fcall <- getCall(object)
recover.data(fcall,delete.response(terms(object)),
attr(model.frame(object),"na.action"), ...) } lsm.basis.glmmTMB <- function (object, trms, xlev, grid, vcov.,
mode = "asymptotic", component="cond", ...) {
if (mode != "asymptotic") stop("only asymptotic mode is available")
if (component != "cond") stop("only tested for conditional component")
if (missing(vcov.))
V <- as.matrix(vcov(object)[[component]])
else V <- as.matrix(.my.vcov(object, vcov.))
dfargs = misc = list()
if (mode == "asymptotic") {
dffun = function(k, dfargs) NA
}
## use this? misc = .std.link.labels(family(object), misc)
contrasts = attr(model.matrix(object), "contrasts")
m = model.frame(trms, grid, na.action = na.pass, xlev = xlev)
X = model.matrix(trms, m, contrasts.arg = contrasts)
bhat = fixef(object)[[component]]
if (length(bhat) < ncol(X)) {
kept = match(names(bhat), dimnames(X)[[2]])
bhat = NA * X[1, ]
bhat[kept] = fixef(object)[[component]]
modmat = model.matrix(trms, model.frame(object), contrasts.arg =
contrasts)
nbasis = estimability::nonest.basis(modmat)
}
else nbasis = estimability::all.estble
list(X = X, bhat = bhat, nbasis = nbasis, V = V, dffun = dffun,
dfargs = dfargs, misc = misc)
}
##### End interlude ###
lsm.TMB<- lsmeans(m.nb2, ~FoodTreatment)
plot(lsm.TMB) #non-overlapping CI's
#Compare SE's
owls.lsm
# FoodTreatment lsmean * SE* df asymp.LCL asymp.UCL
# Deprived 2.053073 *0.8952071* NA 0.2984988 3.807646
# Satiated 1.360690 *0.9037320 *NA -0.4105918 3.131973
lsm.TMB
# FoodTreatment lsmean *SE* df asymp.LCL asymp.UCL
# Deprived 2.053065 *0.1068562* NA 1.843631 2.262500
# Satiated 1.360683 *0.1161322* NA 1.133068 1.588298
#lsmeans are identical but SE's differ by factor of 8?!
Thank you again.
Evan
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