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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. 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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