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
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