Usage
designScore( genotype, array.allocation, condition.allocation, nEnvFactors, nLevels, Level, nConditions, weight=1, optimality="A", bTwoColorArray, envFactorNames)
Arguments
genotype
genotype data: a nMarker-by-nRILs matrix with two allels being 0 and 1 (or A and B)
or three allels being 0, 0.5 and 1 (or, A, H, and B), where 0.5 (or H) represents
heterozygous allele.
array.allocation
matrix with nArray rows and nRIL columns.
Elements of 1/0 indicate this RIL (or strains) is/not selected
for this array.
condition.allocation
matrix with nCondition rows and nRIL columns.
Elements of 1/0 indicate this RIL (or strains) is/not selected
for this condition.
nEnvFactors
number of environmental factors, an integer bewteen 1 and 3.
When nEnvFactors is 1 and the number of levels for the enviromental factor
(nLevels)is 1, there is one condition in the experiment (i.e. no
enviromental perturbation) and thus only genetic factor will be considered in the algorithm.
When nEnvFactors is 1 and nLevels is larger than 1 or nEnvFactors is
larger than 1, all main factor(s) and interacting facotr(s) will be included.
Examples:
If there is a temperature perturbation, then nEnvFactors is 1;
If there is both temperature and drug treatment perturbation,
then nEnvFactors is 2.
nLevels
number of levels for each factor, a vector with each
component being an integer. The length of it should equal
nEnvFactors.
Level
a list which specifies the levels for each factor in the
experiment. There are in total nEnvFactors elements in the list and each
element correpsond to certain envrironmental factor. The emlemet is a
vector describing all levels of the environmental factor.
default setting for the level of each factor is 1, 2, ... nLevels[i]. (Here
nLevels[i] is the ith element of nLevels, which gives the total number of
levels for i environmental factor).
nConditions
number of all possible combination of all environmental factors.
weight
a vector with length of variableNumber which is calculated
from function variableNumber. Default = 1 (which means the parameters to be
estimated are equally important during optimization.)
optimality
type of optimality, i.e. "A" (A-optimality) or "D"
(D-optimality). A-optimality minimizes $Trace((X'X)^-1)$,
which corresponds to minimum average variance of the
parameter estimates. D-optimality minimizes $det(X'X)^-1$,
which corresponds to minimum generalized variance of the
parameter estimates.
bTwoColorArray
binary variable indicating experiment type:
bTwoColorArray <- TRUE \#for dual channel experiment
bTwoColorArray <- FALSE \#for single channel experiment
envFactorNames
a vector with names for all environmental factor(s). For example, for the
experiment with two environmental factors of temperature and drug treatment:
envFactorNames <- c( "Temperature", "Dosage" )
Default = NULL, then the output will use "F1" and "F2" to indicate the
environmental factors.