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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