On Tue, 9 Sep 2003 Christoph Lehmann <christoph.lehmann at gmx.ch>
wrote:> I have the following data
> V1 V2
> 1 -5.8000000 0
> 2 -4.8000000 0
> 3 -2.8666667 0
> 4 -0.8666667 0
> 5 -0.7333333 0
> 6 -1.6666667 0
> 7 -0.1333333 1
> 8 1.2000000 1
> 9 1.3333333 1
>
> and I want to know, whether V1 can predict V2: of course it can, since
> there is a perfect separation between cases 1..6 and 7..9
>
> How can I test, whether this conclusion (being able to assign an
> observation i to class j, only knowing its value on Variable V1) holds
> also for the population, our data were drawn from?
The brlr package does this:
summary(brlr(V2 ~ V1))
brlr(formula = V2 ~ V1)
Coefficients:
Value Std. Error t value
(Intercept) 0.2620 1.0624 0.2466
V1 1.4014 1.0077 1.3908
Deviance: 3.5078
Penalized deviance: 3.528
Residual df: 7
--
| David Duffy (MBBS PhD) ,-_|\
| email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / *
| Epidemiology Unit, Queensland Institute of Medical Research \_,-._/
| 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v