Thank you very much prof. Ravi,
That was very helpful. Is there a way to get the t and p value for the
coefficients?
Thanks
Alaa
> On Mar 2, 2016, at 10:05 AM, Ravi Varadhan <ravi.varad...@jhu.edu> wrote:
>
> There is nothing wrong with the optimization. It is a warning message.
> However, this is a good example to show that one should not simply dismiss a
> warning before understanding what it means. The MLE parameters are also
> large, indicating that there is something funky about the model or the data
> or both. In your case, there is one major problem with the data: for the
> highest dose (value of x), you have all subjects responding, i.e. y = n.
> Even for the next lower dose, there is almost complete response. Where do
> these data come from? Are they real or fake (simulated) data?
>
> Also, take a look at the eigenvalues of the hessian at the solution. You
> will see that there is some ill-conditioning, as the eigenvalues are widely
> separated.
>
> x <- c(1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839)
> y <- c( 6, 13, 18, 28, 52, 53, 61, 60)
> n <- c(59, 60, 62, 56, 63, 59, 62, 60)
>
> # note: there is no need to have the choose(n, y) term in the likelihood
> fn <- function(p)
> sum( - (y*(p[1]+p[2]*x) - n*log(1+exp(p[1]+p[2]*x))) )
>
> out <- nlm(fn, p = c(-50,20), hessian = TRUE)
>
> out
>
> eigen(out$hessian)
>
>
> Hope this is helpful,
> Ravi
>
>
>
> Ravi Varadhan, Ph.D. (Biostatistics), Ph.D. (Environmental Engg)
> Associate Professor, Department of Oncology
> Division of Biostatistics & Bionformatics
> Sidney Kimmel Comprehensive Cancer Center
> Johns Hopkins University
> 550 N. Broadway, Suite 1111-E
> Baltimore, MD 21205
> 410-502-2619
>
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