MANASI VYDYANATH wrote:> Hello -
>
> I apologize if this question is simple/obvious, but I couldn't find a
> satisfactory answer online, and I am not very accustomed to working
> with R (Matlab is my poison. :-)). Any help would be greatly
> appreciated.
>
> I have a model with a three-level factor and a continuous covariate.
> The call I use is:
>
> mymodel <- glm(Response ~ Factor_covariate + continuous_covariate -
> 1, family = binomial(link = "logit"))
>
> I would like to generate predicted values for a given level of the
> covariate, and a given level of the factor. For instance, I want it
> to give me a fitted value for the response at factor level 1 and
> continuous covariate value 10. How would I go about expressing this?
> I tried to look at the package Design, and specifically, at the
> command "predict.lrt". But I was unable to quite understand how I
> ought to enter my x-values. Again, any help would be much appreciated.
>
> Thank you for taking the time to read this!
>
> Cheers,
>
> Manasi
With Design you do predict(mymodel, data.frame(age=30, sex='male'),
type='fitted')
For ordinal responses there are several options for prediction different
things. If you want to leave some covariates unspecified (default to
reference values - medians or modes) you can use predict(mymodel,
gendata(mymodel, list of covariates you care to specify))
Frank Harrell
>
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--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University