R help - Jan 2006 - predict.lme / nlmmPQL: "non-conformable arguments"

I'm trying to use "predict" with a linear mixed-effects logistic  
regression model fitted with nlmmPQL from the MASS library.
Unfortunately, I'm getting an error "non-conformable arguments" in
predict.lme, and I would like to understand why.

I have used the same call to "predict" with "glm" models
without
problems. I assume I'm doing something wrong, but I have no idea what  
it is. If someone could help me (even by telling me how to trace this  
properly - is there an interactive tracer/debugger I can use?),  
that'd be fantastic.

Here's what I'm doing:

 > summary(model)
...
Random effects:
Formula: ~log(distance) | target.utt
...
Fixed effects: primed ~ log(distance) * role * source - log 
(distance):source
...

 >  x=10:500*0.1
 >  new <- data.frame(distance=x, role="r", source="m"
)

 > yp = predict(model,  newdata=new, type="response",  level=0)
Error in X %*% fixef(object) : non-conformable arguments


 > traceback()
4: predict.lme(object, newdata, level = level, na.action = na.action)
3: predict(object, newdata, level = level, na.action = na.action)
2: predict.glmmPQL(model, newdata = new, type = "response", level = 0)
1: predict(model, newdata = new, type = "response", level = 0)

On 30 Jan 2006, at 22:01, David Reitter wrote:
> I'm trying to use "predict" with a linear mixed-effects
logistic
> regression model fitted with nlmmPQL from the MASS library.
> Unfortunately, I'm getting an error "non-conformable
arguments" in
> predict.lme, and I would like to understand why.
I'd like to add a bit of information. (Correction: I am talking about  
glmmPQL from the MASS library.)

Again, the error I'm getting is:
> > yp = predict(model,  newdata=new, type="response",  level=0)
> Error in X %*% fixef(object) : non-conformable arguments
I have ensured that I input a data frame in newdata with the fixed  
factors/predictors filled in (as factors with the correct level sets  
where appropriate).

Debugging this, I had a look at lme.R from the nlme library.
Specifically, line 1909:

if (maxQ == 0) {
     ## only population predictions
     val <- c(X %*% fixef(object))
     attr(val, "label") <- "Predicted values"
     return(val)
   }

the 'fixef' structure in my model looks like this (7 elements)

fixef(model)
                       (Intercept)                     log(distance)
                       -2.14560407                       -0.13207341
                             roler                     sourcemaptask
                       -0.58692474                       -0.93108113
               log(distance):roler log(distance):rolei:sourcemaptask
                        0.16449238                        0.06877369
log(distance):roler:sourcemaptask
                       -0.12278367


But the predict.lme function produces the following 6x5 matrix

   (Intercept) log(distance) roler sourcemaptask log 
(distance):roler    log(distance):roler:sourcemaptask
           1     0.0000000     1             0           0.0000000  0
          1     0.6931472     1             0           0.6931472  0
(...)

We're missing the coefficient for the 3-way interaction "log 
(distance):rolei:sourcemaptask", which is why we can't come up with  
the inner product of X and the fixed effects coefficients.

Is this an issue with predict.lme, and/or can I do something about it?

Thanks
D