R help - Mar 2003 - Goodness of fit tests

I have a dataset which I want to model using a Poisson distribution, with a
given parameter. I would like to know what is the proper way to do a
''goodness of fit'' test using R.
   I know the steps I''d take if I were to do it
''manually'': grouping the numbers into classes, calculating the
expected frequencies using ''ppois'', then calculating Chi_2_obs
= Sum (e_i - o_i)^2/e_i) (where e_i represents the expected frequencies and o_i
the observeds ones) and then finally calculating the p-value (using pchisq).
   I''ve read a lot of documentation, also tried googling for
''goodness of fit R'' but it was helpless, most of it is only
about ''regression analysis''. Does anyone know if there is a
simpler way to do this?


Thank you, 

 
<Fernando Henrique Ferraz Pereira da Rosa - mentus at gmx.de>

Try `goodfit'' in package `vcd''.

g.,
-d

        Mag. David Meyer            Wiedner Hauptstrasse 8-10
Vienna University of Technology     A-1040 Vienna/AUSTRIA
         Department of              Tel.: (+431) 58801/10772
Statistics and Probability Theory   Fax.: (+431) 58801/10798






On Sat, 29 Mar 2003, Fernando Henrique Ferraz wrote:
>   
>    I have a dataset which I want to model using a Poisson distribution,
with a given parameter. I would like to know what is the proper way to do a
''goodness of fit'' test using R.
>    I know the steps I''d take if I were to do it
''manually'': grouping the numbers into classes, calculating the
expected frequencies using ''ppois'', then calculating Chi_2_obs
= Sum (e_i - o_i)^2/e_i) (where e_i represents the expected frequencies and o_i
the observeds ones) and then finally calculating the p-value (using pchisq).
>    I''ve read a lot of documentation, also tried googling for
''goodness of fit R'' but it was helpless, most of it is only
about ''regression analysis''. Does anyone know if there is a
simpler way to do this?
> 
> 
> Thank you, 
> 
>  
> <Fernando Henrique Ferraz Pereira da Rosa - mentus at gmx.de>
> 
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>