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I'm doing an experimental study of 5 factors with a different amount of levels in each. More specifically: 9, 13, 12, 6 & 15.

Using the AlgDesign library in R, i ran the following syntax to generate a D-efficient design:

levels.design <- c(9,13,12,6,15) #5 factors for the data

#Full factorial:
f.design <- gen.factorial(levels.design)

#Fractional factorial design:
start <- Sys.time()
fract.design <- optFederov(data=f.design,
                           nTrials=sum(levels.design),
                           approximate=TRUE, 
                           criterion="D",
                           nRepeats=100,
                           eval=TRUE)

This gave me the following measures:

enter image description here

And a design of 30 rows, looking like this: enter image description here

My question is: How do I implement the table/design above? And how do I interpret the result on the D-measure?

Every level is a specific setting/alternative in a an algorithm. But how do I interpret which of the levels to be run, based on the table?

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