Error with predict.merMod and random slopes

Hello All,

This is my first time posting, and I'm not a very experienced user of R and
lme4, so I apologise in advance if I've missed something important or this
issue has been addressed previously.

I'm trying to use a binomial mixed model to predict whether subjects make a
certain type of response in trials in a behavioural task. I've been fitting
a binomial mixed model using lme4, and have been using the ID of my subjects
as a random intercept, and using the predict.merMod method to predict the
outcome of new trial data, which has been working. 

However, having recently read Barr et al 2013
(http://idiom.ucsd.edu/~rlevy/papers/barr-etal-2013-jml.pdf) I've been
trying to make my models maximal by adding random slopes for my
within-subjects factors. When I add random slopes to my models and use
predict.merMod I now get an error:

"Error: sum(nb) == q is not TRUE"

Here is the code, and a link to the data I am working with, which is
hopefully fully self-contained and working:

library(lme4)

latencyData <-
read.csv("https://dl.dropboxusercontent.com/s/uhn86f5nae662i6
/latencyDataTest.dat",header = TRUE)

#latencyData$prem is the outcome of each behavioural trial (premature or
#not premature) - the dependent variable
#latencyData$group is a between subjects factor 
#latencyData$latency is a reaction time that is measured as part of each
#trial
#latencyData$PO is the outcome of the previous trial
#latencyData$subjectID is a code for the subject which made each trial

#take the full dataset apart from one trial, which I will use for
#prediction later
x <- 1
ix <- 1:dim(latencyData)[1] != x

dataTrain <- latencyData[ix,]
dataTest  <- latencyData[x ,]


#fit a logistic regression model with the subject ID as random intercept
m1 <- glmer(prem ~ group*latency*PO + (1|subjectID),data = dataTrain, 
family = binomial)

#fit a logistic regression model that attempts to be "maximal" after Barr
#et al 2013, including random slopes for the subject's latency, the
#previous trial outcome, and their interaction(the highest order
within-#subjects factors)

m2 <- glmer(prem ~ group*latency*PO + (latency:PO|subjectID),
data = dataTrain, family = binomial)

#Try a model with just 1 random slope
m3 <- glmer(prem ~ group*latency*PO + (PO|subjectID),
data = dataTrain,family = binomial)


#try and predict the left out trial with the random intercept only model-
#gives a result
predict(m1,dataTest,REform = NULL,type = "response")

#try and predict the left out trial with the random intercepts and slopes 
#model
predict(m2,dataTest,REform = NULL,type = "response")

#this gives an error:"Error: sum(nb) == q is not TRUE"

#try and predict the left out trial with the random intercepts and single
#random slope model
predict(m3,dataTest,REform = NULL,type = "response")

#this gives the same error:"Error: sum(nb) == q is not TRUE"

#If I remove the RE from the prediction, I can get a result rather than an
#error
predict(m3,dataTest,REform = NA,type = "response")

However, I would like to be able to make predictions using the maximal model
and the random effects structure. Is there anything anyone can suggest I can
do to get this working, or is it not yet an available feature in lme4 (I
know the predict.merMod method is new)?