>...(1000,0,1), I presume.


Yes thank you.


 ...z <- nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T);


>This makes no sense. Random numbers in a model specification???? 

And maybe that  model spec is nonsense, although it seems to me that it correctly incorporates the idea of a linear density function.     But any ideas on how you might correctly specify such a model?

Thanks,
GR


________________________________
 From: peter dalgaard <pdalgd@gmail.com>

Cc: "r-help@r-project.org" <r-help@r-project.org> 
Sent: Tuesday, January 3, 2012 3:11 AM
Subject: Re: [R] nls and rbinom function: step factor 0.000488281 reduced below 'minFactor' of 0.000976562


On Jan 3, 2012, at 05:25 , G Vishwanath wrote:

> I  am trying to learn nls using a simple simulation. I assumed that the binomial prob varies linearly as 0.2 + 0.3*x in  x {0,1},
> and the objective is to recover the known parameters a=0.2, b=0.3
> 
> ..data frame d has 1000 rows...
> 
> d$x<-runif(0,1)  

...(1000,0,1), I presume.

>            
> 
> d$y<-rbinom(1000,1,0.2+0.3*d$x) 
> 
> table(d$y,cut(d$x,breaks=5));
> 
>   (-0.000585,0.199] (0.199,0.399] (0.399,0.599] (0.599,0.799] (0.799,0.999]
>   0               154           149           130           122           114
>   1                34            48            71            76           102
> 
>  z <- nls(y ~ rbinom(1000,1,a+b*x),data=d,start= list(a =0.1,b=0.2),trace=T);
> 

This makes no sense. Random numbers in a model specification???? 

y~a+b*x might give a result, but you're fitting a model which assumes Gaussian errors with constant variance to data that are nothing of the sort.


-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes@cbs.dk  Priv: PDalgd@gmail.com
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