[R] Simulating points from GLM corresponding to new x-values

[Jacob Nabe-Nielsen]

Hi Cliff -- thanks for the suggestion.

I tried extracting the predicted mean and standard error using  
predict(). Afterwards I simulated the dependent variable using  
rnorm(), with mean and standard deviation taken from the predict()  
function (sd = sqrt(n)*se). The points obtained this way were  
scattered far too much (compared to points obtained with simulate())  
-- I am not quite sure why.

Unfortunately the documentation of the simulate() function does not  
provide much information about how it is implemented, which makes it  
difficult to judge which method is best (predict() or simulate(), and  
it is also unclear whether simulate() can be applied to glms (with  
family=gaussian or binomial).

Any suggestions for how to proceed?

Jacob


On 12 Aug 2009, at 13:11, Clifford Long wrote:

> Would the "predict" routine (using 'newdata') do what you need?
>
> Cliff Long
> Hollister Incorporated
>
>
>
> On Wed, Aug 12, 2009 at 4:33 AM, Jacob Nabe- 
> Nielsen<jacobnabe at me.com> wrote:
>> Dear List,
>>
>> Does anyone know how to simulate data from a GLM object correponding
>> to values of the independent (x) variable that do not occur in the
>> original dataset?
>>
>> I have tried using simulate(), but it generates a new value of the
>> dependent variable corresponding to each of the original x-values,
>> which is not what I need. Ideally I whould like to simulate new  
>> values
>> for GLM objects both with family="gaussian" and with  
>> family="binomial".
>>
>> Thanks in advance,
>> Jacob
>>
>> Jacob Nabe-Nielsen, PhD, MSc
>> Scientist
>>  --------------------------------------------------
>> Section for Climate Effects and System Modelling
>> Department of Arctic Environment
>> National Enviornmental Research Institute
>> Aarhus University
>> Frederiksborgvej 399, Postbox 358
>> 4000 Roskilde, Denmark
>>
>> email: nabe at dmu.dk
>> fax: +45 4630 1914
>> phone: +45 4630 1944
>>
>>
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