facDiGGer {DiGGer} | R Documentation |
Generate a DiGGer search for designs with factorial treatments.
facDiGGer(factorNames = NULL, rowsInDesign = NULL, columnsInDesign = NULL, rowsInReplicate = NULL, columnsInReplicate = NULL, mainPlotSizes = NULL, treatDataFrame = DF, treatRepColumn = "Repeats", blockSequence = NULL, objectiveWeight = c(0.8, 0.2), searchIntensity = 100, chequerboard = FALSE, spatial = TRUE, rowColumn = TRUE, designLayoutTemplate = NULL, maxInterchanges = 100000, rngSeeds = NULL, rngState = NULL)
factorNames |
Text vector of factor names. |
rowsInDesign |
Number of rows in the design. |
columnsInDesign |
Number of columns in the design. |
rowsInReplicate |
Number or rows in each replicate block. |
columnsInReplicate |
Number of columns in each replicate block. |
mainPlotSizes |
A list of dimension pairs indicating main plot
sizes. Randomised factorial designs have main plot sizes of
|
treatDataFrame |
Data frame with treatment factors and repeats per replicate. |
treatRepColumn |
Text name of the column in the
|
blockSequence |
List of dimension pairs of blocks to be optimised in sequence. |
objectiveWeight |
Relative weights for multiple objectives – limited use. |
searchIntensity |
Percentage of possible interchanges to consider for non-improving interchanges – limited use. |
chequerboard |
Used with 2 level factors. If |
spatial |
If |
rowColumn |
If |
designLayoutTemplate |
An optional matrix showing replicate numbers with missing plots coded as 0. |
maxInterchanges |
Maximum number of interchanges used in each search phase. |
rngSeeds |
Seeds c(s1,s2) to control the DiGGer search. s1 must be in the range [0,31328], s2 must be in the range [0,30081]. |
rngState |
Current state of the random number generator. |
facDiGGer
is a one factor at a time search which optimises the
first factor across the whole design before considering each
subsequent factor in order. As each factor is added to the design the
number of treatments in the design is increased and the levels of the
latest factor added are used as a group code. The last factor added
may have limited randomisation.
Split-plot designs are created by creating main plots and expanding them to the appropriate size before adding subsequent factors.
The createFactorialDF
function is provided to help create
factorial treatment data frames.
A DiGGer
object with the sequentially optimised design.
Neil Coombes
Coombes, N.E. (2002) The Reactive Tabu Search for Efficient Correlated Experimental Designs. PhD Thesis, Liverpool John Moores University.
# 3x3x3 factorial # blocked to give replicates in two directions DF27 <- createFactorialDF(c(3,3,3)) test27 <- facDiGGer(c("F1","F2","F3"), 27,3, 27,1, treatDataFrame = DF27, rngSeeds = c(2468, 1357), blockSequence = list(c(9,1)))