AOAC Gluten Quantitative Validation Guidance-Round 1(Nov 2023)

Data Set A2b

166

Lot

Analyst

TP

Well

Result

1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3

1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 2 2 2 2

1 1 2 2 1 1 2 2 1 1 2 2 1 1 2 2 1 1 2 2 1 1 2 2

1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2

90.25167 89.92019 95.44815 95.56066 84.36506 84.57392 84.08832 84.13355 106.9066 107.2665 109.8504 109.1556 98.01522 98.28006 105.577 104.6931 91.38499 94.22005 97.7466 99.12495 92.57129 90.96285 94.02378 94.9194

167

R-Code for Data Set A2b

168

169 170 171 172 173 174 175 176 177 178 179 180 181

library(VCA)

DataA2b<- read.csv("Test Data A2b.csv")

fit1<- fitVCA(form=Result~(Lot+Analyst)/TP, Data=DataA2b) # Analyst not nested within Lot

fit1

fit2<- fitVCA(form=Result~Lot/Analyst/TP, Data=DataA2b) # Analyst nested within Lot

fit2

varPlot(form=Result~Lot/Analyst/TP, Data=DataA2b, YLabel = list(text="Result", las=0, line=3, cex=1.5),

Title= list(main="GFA TEST DATA RESULTS PLOT SET A1b", cex.main= 1.75),

Points= list(pch=20, cex=2.50, col="blue"),

#MeanLine=list(var="int"),

MeanLine=list(var=c("Day", "int"), col="blue")

)

Data Output

182

Result ~ (Lot+Analyst)/TP

Analyst not nested within Lot, TP nested within (Lot+Analyst)