using this code:
par(mar=c(5, 4, 4, 6) + 0.1)
plot(Xodd_pcr3,"validation",estimate="CV",
col="blue",ylim=c(0.5,2.5),
xlim=c(1,10),lwd=2,xlab="",ylab="",main="")
par(new=TRUE)
plot(terms,Test_rms,type="l",col="red",ylim=c(0.5,2.5),
xlim=c(1,10),lwd=2,xlab="Nº Terms",
ylab="RMSEP",
main=("CV vs Ext Validation & Corr"))
## Plot the second plot and put axis scale on right
par(new=TRUE)
plot(terms, Test_corr, pch=15, xlab="", ylab="",
ylim=c(0,1),axes=FALSE, type="b", col="green")
## a little farther out (line=4) to make room for labels
mtext("Correlation",side=4,col="green",line=3)
axis(4, ylim=c(0,1), col="green",
col.axis="green",las=1)
legend("topright",legend=c("CV", "Ext Val","Corr"),
col=c(1:3),lwd=2)
## In the plot 5 seems to be the best option
for the number of terms
The statistics for this external validation set with 5 terms are (Monitor function):
> monitor10c24xyplot(pred_vs_ref_test_5t)
Validation Samples = 25
Reference Mean = 45.6236
Predicted Mean = 46.38495
RMSEP : 1.046828
Bias : -0.761354
SEP : 0.7332779
Corr : 0.9131366
RSQ : 0.8338185
Slope : 0.7805282
Intercept: 9.418837
RER : 8.115895 Poor
RPD : 2.428305 Fair
Residual Std Dev is : 0.633808
***Slope/Intercept adjustment is recommended***
BCL(+/-): 0.3020428
***Bias adjustment in not necessary***
Without any adjustment and using SEP as std dev
the residual distibution is:
Residuals into 68% prob (+/- 1SEP) = 15 % = 60
Residuals into 95% prob (+/- 2SEP) = 21 % = 84
Residuals into 99.5% prob (+/- 3SEP) = 23 % = 92
Residuals outside 99.5% prob (+/- 3SEP) = 2 % = 8
With S/I correction and using Sres as standard deviation,
the Residual Distribution would be:
Residuals into 68% prob (+/- 1Sres) = 20 % = 80
Residuals into 95% prob (+/- 2Sres) = 24 % = 96
Residuals into 99.5% prob (+/- 3Sres) = 25 % = 100
Residuals outside 99.5% prob (> 3Sres) = 0 % = 0
No hay comentarios:
Publicar un comentario