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These are samples analyzed by a reference method (column: Protein) and by an analytical method with a certain model (column: IFTpro). The idea is to create a Monitor Report for some basic statistics (RMSEP, Bias, SEP, R,RSQ) to see how well the model performs.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Sample Protein IFTpro
3 12.85 12.95
4 12.68 12.59
5 11.94 12.12
6 12.07 12.25
7 12.53 12.35
8 11.82 12.20
9 12.58 12.18
10 12.35 12.27
11 12.38 12.32
12 12.15 12.31
13 12.75 12.28
14 12.51 12.07
15 11.92 12.20
16 12.14 12.24
17 12.33 12.27
18 12.15 12.10
20 11.82 11.94
21 11.82 12.05
22 12.36 12.05
23 12.06 11.91
24 11.87 11.98
25 11.81 11.80
26 11.53 11.64
27 11.75 11.84
I take this as a practice with R to write some script.
This is the script:
monitor2<-function(x,y){
n<-length(y)
res<-y-x
par(mfrow=c(2,2))
hist(res,col=”blue”)
plot(x~y,xlab=”predicted”,ylab=”reference”,lty=1)
abline(0,1,col=”blue”)
l<-seq(1:n)
plot(res~l)
abline(0,0,col=”blue”)
{rmsep<-sqrt(sum((y-x)^2)/n)
cat(“RMSEP:”,rmsep,”\n”)}
{(bias<-mean(res))
cat(“Bias :”,bias,”\n”)}
{sep<-sd(res)
cat(“SEP :”,sep,”\n”)}
{r<-cor(x,y)
cat(“Corr :”,r,”\n”)}
{rsq<-(r^2)
cat(“RSQ :”,rsq,”\n”)}
}The statistics for this case are:
> monitor2(semola1$Protein,semola1$IFTpro)
RMSEP: 0.2219797
Bias : -0.01083333
SEP : 0.2264838
Corr : 0.772607
RSQ : 0.5969215
And the plots:
I realized that there are a lot of things to improve. to make this script more robust. So I will continue reading tutorials, R help pages, and posts from R blogger,…looking at videos, webinars, reading books,…. to continue improving. Anyway, feel free to take this scrip and add to me feedback.
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