multistagevariance: Expected variance after selection after k stages selection

In selectiongain: A Tool for Calculation and Optimization of the Expected Gain from Multi-Stage Selection

Expected variance after selection after k stages selection

Description

This function uses the algorithm described by Tallis (1961) to calculate the variance after multi-stage selection. The variance among candidates of y in the selected area \textbf{S}_{Q} is defined as the second central moment, ψ_n(y)=E(Y^2|\textbf{S}_{Q}) - ≤ft[E(Y|\textbf{S}_{Q})\right]^2, where E(Y^2|\textbf{S}_{Q}) = α^{-1} \int_{-∞} ^∞ \int_{q_{1}}^∞...\int_{q_{n}}^∞ y^2\, φ_{n+1}(\textbf{x}^{*}; \bm{Σ}^{*}) \, d \textbf{x}^*

Usage

multistagevariance(Q, corr, alg, Vg)

Arguments

Q	are the coordinates of the truncation points, which are the output of the function multistagetp that we are going to introduce.
corr	is the correlation matrix of y and X, which is introduced in the function multistagecorr. The correlation matrix must be symmetric and positive-definite. If the estimated correlation matrix is negative-definite, it must be adjusted before using this function. Before starting the calculations, it is recommended to check the correlation matrix.
alg	is used to switch between two algorithms. If alg = GenzBretz(), which is by default, the quasi-Monte Carlo algorithm from Genz et al. (2009, 2013), will be used. If alg = Miwa(), the program will use the Miwa algorithm (Mi et al., 2009), which is an analytical solution of the MVN integral. Miwa's algorithm has higher accuracy (7 digits) than quasi-Monte Carlo algorithm (5 digits). However, its computational speed is slower. We recommend to use the Miwa algorithm.
Vg	correspond to the genetic variance or variance of the GCA effects. The default value is 1

Value

The output is the value of ψ_n(y|\textbf{S}_{Q}).

Note

No further notes

Author(s)

Xuefei Mi

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.

G.M. Tallis. Moment generating function of truncated multi-normal distribution. J. Royal Stat. Soc., Ser. B, 23(1):223-229, 1961.

X. Mi, T. Miwa and T. Hothorn. Implement of Miwa's analytical algorithm of multi-normal distribution. R Journal, 1:37-39, 2009.

See Also

No link

Examples

# first example

Q =c(0.4308,0.9804,1.8603)

corr=matrix( c(1,       0.3508,0.3508,0.4979,
               0.3508,  1,     0.3016,0.5630,
               0.3508,  0.3016,1,     0.5630,
               0.4979,  0.5630,0.5630,1),
              nrow=4
)


multistagevariance(Q=Q,corr=corr,alg=Miwa)

# time comparsion

var.time.miwa=system.time (var.miwa<-multistagevariance(Q=Q,corr=corr,alg=Miwa))

var.time.bretz=system.time (var.bretz<-multistagevariance(Q=Q,corr=corr))



# second examples


Q= c(0.9674216, 1.6185430)
corr=matrix( c(1,         0.7071068, 0.9354143,
               0.7071068, 1,         0.7559289,
               0.9354143, 0.7559289, 1),
              nrow=3
)


multistagevariance(Q=Q,corr=corr,alg=Miwa)

var.time.miwa=system.time (var.miwa<-multistagevariance(Q=Q, corr=corr, alg=Miwa))

var.time.bretz=system.time (var.bretz<-multistagevariance(Q=Q, corr=corr))




# third examples

 alpha1<- 1/(24)^0.5
 alpha2<- 1/(24)^0.5
 Q=multistagetp(alpha=c(alpha1,alpha2),corr=corr)


corr=matrix( c(1,         0.7071068,0.9354143,
               0.7071068, 1,        0.7559289,
               0.9354143, 0.7559289,1),
              nrow=3
)

multistagevariance(Q=Q, corr=corr, alg=Miwa)

selectiongain documentation built on Sept. 17, 2022, 5:05 p.m.