R help - Jan 2012 - nls and rbinom function: step factor 0.000488281 reduced below 'minFactor' of 0.000976562

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)              

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);

374 :  0.1 0.2 
361 :  0.1 0.2 
350 :  0.1 0.2 
Error in nls(y ~ rbinom(1000, 1, a + b * x), data = d, start = list(a = 0.1,  : 
  step factor 0.000488281 reduced below 'minFactor' of 0.000976562

I have tried plinear, got the same....

Additionaly- why does the parameters not change with the iteration ?
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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.


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Peter Dalgaard, Professor,
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