I am interested Andrea is whether you ever established why your R2 was 1.
I have had a similar situation previously.
My main issue though, which I'd be v grateful for advice on, is why I am
obtaining such negative values -0.3 for Somers Dxy using validate.cph from
the Design package given my value of Nagelkerke R2 is not so low 13.2%.
I have this output when fitting 6 variables all with p-values<0.01
I am wondering what the interpretation should be.
I know my Nagelkerke R2 isn't very good but I compare my results with the
example from ?validate.cph and although I have a better R2 (13% v 9%) the Somers
dxy from the example data set is much better, 38%, so certainly not negative !
So my main question is : Why such a difference between explained variation, R2,
and predictive ability: somers dxy ??
Obs Events Model L.R. d.f. P Score Score P R2
471 228 66.36 6 0 73.41 0
0.132
....
> validate(f, B=150,dxy=T) # normally B=150
index.orig training test optimism index.corrected n
Dxy -0.3022537331 -0.3135032097 -0.292492573 -0.021010636 -0.2812430968 150
R2 0.1319445685 0.1431179294 0.122599605 0.020518324 0.1114262446 150
Slope 1.0000000000 1.0000000000 0.923340558 0.076659442 0.9233405576 150
D 0.0250864459 0.0276820092 0.023163167 0.004518842 0.0205676038 150
U -0.0007676033 -0.0007725071 0.000610456 -0.001382963 0.0006153598 150
Q 0.0258540493 0.0284545164 0.022552711 0.005901805 0.0199522440 150
I also calculated the Schemper and Henderson V measure and obtained v=10.5%
I was using the surev package of Lusa Lara; Miceli Rosalba; Mariani
LuigiEstimation of predictive accuracy in survival analysis using R and
S-PLUS.<http://www.biomedexperts.com/Abstract.bme/17601627/Estimation_of_predictive_accuracy_in_survival_analysis_using_R_and_S-PLUS>
Computer methods and programs in biomedicine 2007;87(2):132-7.
And my code was
library(surev)
pred.accuracy<-f.surev(f)
pred.accuracy
sorry if my question isn't clear - should I have included my sessionInfo for
a methodological question ? (I'm a newbie)
many thanks for any advice
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