library(VGAM)
library(MTAR)
Z = rbinom(1000,1,0.5)
G = rbinom(1000,2,0.25)
##
X = rnorm(1000)
e = rnorm(1000)
Y1 = Z+X + 0.15*G + rnorm(1000)+e
Y2 = Z+X + 0.1*G + rnorm(1000)+e
Y = cbind(Y1,Y2)
obj = MLM.null(Y,cbind(Z,X))
MQTAc(obj, G)
##
X1 = rnorm(1000)
X2 = rnorm(1000)
Y1 = Z+X1 + 0.15*G + rnorm(1000)+e
Y2 = Z+X2 + 0.1*G + rnorm(1000)+e
Y = cbind(Y1,Y2)
YX = list(Y, cbind(X1,Z), cbind(X2,Z))
objd = MDM.null(YX,pux=2)
MQTAd(objd, G)
library(MTAR)
n=1e3; K=4; m=3; maf=0.2
Xs = matrix(rnorm(n*K),n,K)
E = matrix(rnorm(n*m), n,m)*sqrt(0.75) + rnorm(n)*sqrt(0.25)
G = rbinom(n,2,maf)
Ys = rowMeans(Xs) + E
Y1 = Ys
Y1[,1] = Y1[,1] + 0.15*G
Y1[,2] = Y1[,2] - 0.15*G
MTA.ACL(Y1,Xs,G)
Y2 = Ys
Y2[,1] = Y2[,1] + 0.15*G
Y2[,2] = Y2[,2] + 0.15*G
MTA.ACL(Y2,Xs,G)
Y3 = Ys + 0.15*G
MTA.ACL(Y3,Xs,G)
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