Hi,
I was wandering if you'd like to have way to compute the calibration model (the parameters), and be able to use a 'predict()' function to predict, based upon the calibration done on a known dataset, the calibrated values for new values (that are not in the calibration set).
It's not very difficult with the current state to compute the calibrated values for new input LRs, but you have to understand how the calibration process is done and code yourself the calibration parameters and the apply-calibration functions.
I can do this for the pava (calibration.set() function) (and the logistic calibration if I add it later on).
Marco
EDIT:
The calibrated values are, in some circonsances a bit difficult to direclty relate to the input data, in particular if the input data is not sorted. The predict() function could help I think.
> lrss
[1] 316227.76602 562.34133 177.82794 56.23413 56234.13252
[6] 100000.00000 1778.27941 31622.77660 17782.79410 10000.00000
> lrds
[1] 1000.000000 56.234133 100.000000 3162.277660 5.623413 10.000000
[7] 316.227766 31.622777 3.162278 17.782794
> calibrate.set( lrss, lrds )
$LR.cal.ss
[1] 1 1 1 1 Inf Inf Inf Inf Inf Inf
$LR.cal.ds
[1] 0 0 0 0 0 0 1 1 1 1
Hi,
I was wandering if you'd like to have way to compute the calibration model (the parameters), and be able to use a 'predict()' function to predict, based upon the calibration done on a known dataset, the calibrated values for new values (that are not in the calibration set).
It's not very difficult with the current state to compute the calibrated values for new input LRs, but you have to understand how the calibration process is done and code yourself the calibration parameters and the apply-calibration functions.
I can do this for the pava (calibration.set() function) (and the logistic calibration if I add it later on).
Marco
EDIT:
The calibrated values are, in some circonsances a bit difficult to direclty relate to the input data, in particular if the input data is not sorted. The
predict()function could help I think.