On Nov 19, 2013, at 10:59 AM, Calum wrote:
> Hi there,
> I hope someone can help me.
>
> I have a dataset of Concentration against Mortality, and I am trying to
> compare the use of Logit and Probit models using this data.
>
> The issue I am having is trying to back transform the data from the probit
> model, to plot it in normal space instead of log space.
> I know this can be done with a logit model using the code below, where
> ilogit is a function for the inverse logit:
>
> NEWCONC <- seq(0,0.6, length=25)
> NEWMORT <- predict(LOGIT, Conc=NEWCONC, se=TRUE)
>
> plot(data=DATA, Prop~Conc)
> lines(NEWCONC, ilogit(NEWMORT$fit))
>
> However, I can't seem to find a function equivalent to ilogit for a
probit
> model, that I could use in this code:
>
> NEWCONC <- seq(0,0.6, length=25)
> NEWMORT <- predict(PROBIT, Conc=NEWCONC, se=TRUE)
You should be looking at ?predict and paying particular attention to the
'type' argument. I think you want: type='response'
>
> plot(data=DATA, Prop~Conc)
> lines(NEWCONC,###INVERSE PROBIT### (NEWMORT$fit))
>
>
> Any advice on this issue would be appreciated,
> Thanks,
> Calum
>
>
>
> --
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David Winsemius
Alameda, CA, USA