Hello,
Thanks for getting back to me. I was of the impression that once the res. var. is larger than the df then the data was overdispersed and as such the model was not a best fit. Is this true?
Here is an example of the output from R:
Call:
glm(formula = y ~ log(conc), family = binomial)
Deviance Residuals:
       1         2         3         4         5         6
 0.54568   1.08474   0.04561  -2.00959   0.05772   1.33891
Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.52815    0.85916  -6.434 1.24e-10 ***
log(conc)    0.40457    0.05938   6.813 9.56e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
    Null deviance: 78.811  on 5  degrees of freedom
Residual deviance:  7.311  on 4  degrees of freedom
AIC: 30.45
Number of Fisher Scoring iterations: 4
>
> xv<-seq(min(log(conc)-1),max(log(conc)+1),0.01)
> lines(xv,predict(model,list(conc=exp(xv)),type="response"))
>
> dose.p(model,p=c(0.10,0.25,0.5,0.75,0.90))
               Dose        SE
p = 0.10:  8.233179 0.9810446
p = 0.25: 10.948665 0.6580127
p = 0.50: 13.664152 0.4703530
p = 0.75: 16.379638 0.5720159
p = 0.90: 19.095125 0.8665399
> exp(13.664152)
[1] 859539.4
> exp(13.664152+(1.96*0.4703530))
[1] 2160918
> exp(13.664152-(1.96*0.04703530))
[1] 783842

BW

Adaku
________________________________________
From: peter dalgaard [pdalgd@gmail.com]
Sent: 09 July 2012 20:03
To: Lawrence, Adaku
Cc: r-help@r-project.org
Subject: Re: [R] Correcting for overdispersion

On Jul 9, 2012, at 20:23 , Lawrence, Adaku wrote:

> Hello,
>
> I am trying to determine LD50 and LD95 using dose.p in MASS however some of the Residual variance is larger than the degrees of freedom. Please can anyone help with any advice as to how i can correct for this?

Er, in what sense is that a problem? Your code is not reproducible, at least some output to look at might help.

-pd

>
> Here is the model as inputted into R
>
>
>
> y<-cbind(dead,n-dead)
>
> model<-glm(y~log(conc),binomial)
> summary(model)
>
> xv<-seq(min(log(conc)-1),max(log(conc)+1),0.01)
> lines(xv,predict(model,list(conc=exp(xv)),type="response"))
>
> dose.p(model,p=c(0.10,0.25,0.5,0.75,0.90,0.95))
>
>
>
> Thanks
>
> Adaku
>
>
>
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>
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--
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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