selectiongain (version 2.0.6)

multistagegain.each: Function for calculating the selection gain in each stage

Description

This function calculates $\Delta G(y)$ stepwise for each stage.

Usage

multistagegain.each(Q, corr, alg, lim.y)

Arguments

Q

Vector with length n. It refers to the coordinates of the truncation points Q, which is the output of the function multistagetp.

corr

(n+1-dimensional matrix). It is the correlation matrix $\bm{\Sigma}^{*}$ of true value y and selection indices X. More detail see multistagegain.each.

alg

An object used to switch between two algorithms. More detail see multistagegain.

lim.y

The lower limit of y as double, and set to -200 as default, which is on the safe side.

Value

• The output is given as $(\Delta G_1(y), \Delta G_2(y)-\Delta G_1(y), \Delta G_3(y)-\Delta G_2(y), ...)$ where $\Delta G_i(y)$ refers to the total selection gain after the first i stages of selection.

Details

In some situations, the user wants to know the increase of $\Delta G(y)$ in each stage so that it is possible to determine the stage which contributes most to $\Delta G(y)$.

See Also

multistagegain

Examples

k=c(-200,0.4308,0.9804,1.8603)

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  
)

multistagegain.each(Q=c(0.4308,0.9804,1.8603),corr=corr)

# further examples 3 for the JSS paper

 alpha1<- 1/24
 alpha2<- 1
 Q=multistagetp(alpha=c(alpha1,alpha2),corx=corr[2:3,2:3])

k=c(-200,Q)

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

alphaofx=pmvnorm(lower=k,corr=corr)

multistagegain(Q=Q,corr=corr,)

multistagegain(Q=Q,corr=corr,stages=TRUE)

multistagegain.each(Q=Q,corr=corr)