multistageoptimum.search: Function for optimizing three-stage selection in plant breeding with one Marker-Assisted Selection stage and two phenotypic stages

Description

This function is used to calculate the maximum of $\Delta G$ with changing correlation matrix by grid search algorithm. The changing correlation matrix of three-stage selection are the testcross progenies of DH lines in one Marker-Assisted Selection (MAS) stage and two Phenotypic Selection (PS) stages.

Usage

multistageoptimum.search (maseff=0.4, VGCAandE, 
  VSCA, CostProd, CostTest,  Nf, Budget, N2grid , 
  N3grid, L2grid, L3grid, T2grid, T3grid,R2,R3,alg,detail=FALSE,fig=FALSE)

Arguments

maseff

is the efficiency of MAS.

VGCAandE

is the vector of variance components for general combination ability and the plot error.

VSCA

is the vector of variance components for special combination ability.

CostProd

contains the initial costs of producing or identifying a candidate in each stage

CostTest

contains a vector with length n reflecting the cost of evaluating a candidate in the tests performed at stage i, i=1,...,n. The cost might vary in different stages.

is the number of final selected candidates.

Budget

contains the value of total budget.

N2grid

is the vector of lower and upper limit as well as the grid width of number of candidates in first field test stage.

N3grid

is the vector of lower and upper limit as well as the grid width of number of candidates in second field test stage.

L2grid

is the vector of lower and upper limits of number of location in the first field test stage.

L3grid

is the vector of lower and upper limits of number of location in the second field test stage.

T2grid

is the vector of lower and upper limits of number of tester in the first field test stage.

T3grid

is the vector of lower and upper limits of number of tester in the second field test stage.

is the number of replicates in the first field test stage. By default it is 1.

is the number of replicates in the second field test stage. By default it is 1.

alg

is used to switch between two algorithms. If alg = GenzBretz(), which is by default, the quasi-Monte Carlo algorithm from Genz (2009) will be used. If alg = Miwa(), the program will use the Miwa algorithm (Mi et. al., 2009), whic

detail

is the control parameter to decide if the result of all the grids will be given or only the maximum. The defalt

fig

is the control parameter to decide if a figure of contour plot will be saved in the default folder. The default value is FALSE.

Value

If $\texttt{detail}$ = FALSE, the output of this functions is a vector of the optimum allocation which achieves the maximum $\Delta G$. Otherwise, the result for all the grid points, which have been calculated, will be exported as a table.

References

A. Genz and F. Bretz. Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195, Springer-Verlag, Heidelberg, 2009. A. Genz, F. Bretz, T. Miwa, X. Mi, F. Leisch, F. Scheipl and T. Hothorn. mvtnorm: Multivariate normal and t distributions. R package version 0.9-9995, 2013. E.L. Heffner, A.J. Lorenz, J.L. Jannink, and M.E. Sorrells. Plant breeding with genomic selection: gain per unit time and cost. Crop Sci. 2007 50: 1681-1690. X. Mi, T. Miwa and T. Hothorn. Implement of Miwa's analytical algorithm of multi-normal distribution, R Journal, 1:37-39, 2009.

Examples

Run this code

CostProd =c(0.5,1,1)
CostTest = c(0.5,1,1)
Budget=1021
# Budget is very small here to save time in package checking
# for the example in Heffner's paper (2010), please change it to Budget=10021

VCGCAandError=c(0.4,0.2,0.2,0.4,2)
VCSCA=c(0.2,0.1,0.1,0.2)
Nf=10


multistageoptimum.search (maseff=0.4, VGCAandE=VCGCAandError, 
VSCA=VCSCA, CostProd = c(0.5,1,1), CostTest = c(0.5,1,1), 
Nf = 10, Budget = Budget, N2grid = c(11, 1211, 30), 
N3grid = c(11, 211, 5), L2grid=c(1,3,1), L3grid=c(6,8,1),
T2grid=c(1,2,1), T3grid=c(3,5,1),R2=1,R3=1,  alg = Miwa(),detail=TRUE,fig=TRUE)