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R/R_getZ.general.r
In CGEN: An R package for analysis of case-control studies in genetic epidemiology
Defines functions
getZ.general
########## March 8 2011: indep=T for 2x3 ###############################################################
########## Jan 10 2010: this expands to dominant model ####
getZ.general=function(xx,G,nX1,nX2) {# , mycolnames,g.model,interCols){ # G=0,1,2 (genotype score of first locus) N is number of obserbations
########## xx is columns of main effects of x1 and x2
########## G takes 0,1,or 2
ans=NULL
################# [1] Preliminary ############################################
mainCols.x2 = mycolnames = cols = NULL
if(nX1==2 & nX2==2) { mainCols.x1=1; mycolnames =c("xx11","xx21","d11") }
if(nX1==2 & nX2==3) { mainCols.x1=1; mycolnames =c("xx11","xx21","xx22","d11","d12") }
if(nX1==3 & nX2==2) { mainCols.x1=1:2; mycolnames =c("xx11","xx12","xx21","d11","d21") }
if(nX1==3 & nX2==3) { mainCols.x1=1:2; mycolnames =c("xx11","xx12","xx21","xx22","d11","d21","d12","d22") }
if(nX1==2 & nX2==2) cols=c("1 2") # x1 x2
if(nX1==2 & nX2==3) cols=c("1 2","1 3") # x1 x2_1 x2_2
if(nX1==3 & nX2==2) cols=c("1 3","2 3") # x1.1 x1.2 x2.1
if(nX1==3 & nX2==3) cols=c("1 3","2 3","1 4", "2 4") # # x1_1 x1_2 x2_1 x2_2 ---> g11 g21 g21 g22
#> xx[1:10,] # main effect terms
#> xx[1:5,]
# xx1 xx2
#[1,] 0 1
#[2,] 1 1
#[3,] 0 1
#[4,] 1 1
#[5,] 0 1
#> xx[1:10,] # main effect terms
# xx11 xx12 xx21 xx22
# [1,] 1 0 0 0
# [2,] 1 0 0 0
# [3,] 1 0 0 0
# [4,] 1 0 1 0
# [5,] 0 0 1 0
# [6,] 0 1 0 0
# [7,] 0 1 0 1
# [8,] 1 0 1 0
# [9,] 1 0 1 0
#[10,] 0 1 0 0
#> mycolnames
#[[1] "xx1" "xx2" "d12"
################ (2) Make Main effect matrix according to specified Z ############
xx2=NULL
xx2=xx
if(G==0){ # then assign genotype score 0 for all individual
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
xx2[,mainCols.x1] = 0 # for both dummy variables x11 and x12 assign 0
}#end of if(G==0){
if(G==1){ # then assign genotype score 1 for all individual : for dominant,
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
xx2[,mainCols.x1[1]] = 1 # for dominnt model, the single xx1 assigned 1 &
# for general model, only first dummy assigend 1 and second dummy 0
if(nX1==3) xx2[,mainCols.x1[2]] = 0 # for general model, the second dummy is zero
}#end of if(G==0){
if(G==2){ # then assign genotype score 2 for all individual for general model, but NOT for dominant,
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
if(nX1==3) { #if general model
xx2[,mainCols.x1[1]] = 0 # first dummy is zero and second dummy is 1
xx2[,mainCols.x1[2]] = 1
}#end of if(nX1==3)
###### this doesn't do anything to dominant model
}#end of if(G==0){
table(xx2[,1])
G
#> xx2[1:5,]
# xx11 xx21 xx22
#[1,] 1 0 1
#[2,] 1 0 0
#[3,] 1 1 0
#[4,] 1 0 1
#[5,] 1 1 0
#> table(xx2[,1])
#
# 1
#1364
#> G
#[1] 1
################ (2) Interaction design matrix ################
#> interCols
#[1] "1 2"
dat.inter=myInteractMatrix2(mat=xx2,cols)
#dat.inter[1:10,]
#> xx2[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> dat.inter[1:5,]
# [,1] [,2] [,3] [,4]
#[1,] 0 0 0 0
#[2,] 0 0 0 0
#[3,] 0 0 0 0
#[4,] 0 1 0 0
#[5,] 0 1 0 0
ans=cbind(xx2,dat.inter)
colnames(ans)=mycolnames
ans
#> ans[1:5,]
# xx11 xx21 xx22 d11 d12
#[1,] 1 0 1 0 1
#[2,] 1 0 0 0 0
#[3,] 1 1 0 1 0
#[4,] 1 0 1 0 1
#[5,] 1 1 0 1 0
}# end of getZ
#> getZ(xx,G=0,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 0 0 0 0 0 0 0 0
# [2,] 0 0 0 0 0 0 0 0
# [3,] 0 0 0 0 0 0 0 0
# [4,] 0 0 1 0 0 0 0 0
# [5,] 0 0 1 0 0 0 0 0
# [6,] 0 0 0 0 0 0 0 0
# [7,] 0 0 0 1 0 0 0 0
# [8,] 0 0 1 0 0 0 0 0
# [9,] 0 0 1 0 0 0 0 0
#[10,] 0 0 0 0 0 0 0 0
#> getZ(xx,G=1,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 1 0 0 0 0 0 0 0
# [2,] 1 0 0 0 0 0 0 0
# [3,] 1 0 0 0 0 0 0 0
# [4,] 1 0 1 0 1 0 0 0
# [5,] 1 0 1 0 1 0 0 0
# [6,] 1 0 0 0 0 0 0 0
# [7,] 1 0 0 1 0 0 1 0
# [8,] 1 0 1 0 1 0 0 0
# [9,] 1 0 1 0 1 0 0 0
#[10,] 1 0 0 0 0 0 0 0
#> getZ(xx,G=2,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 0 1 0 0 0 0 0 0
# [2,] 0 1 0 0 0 0 0 0
# [3,] 0 1 0 0 0 0 0 0
# [4,] 0 1 1 0 0 1 0 0
# [5,] 0 1 1 0 0 1 0 0
# [6,] 0 1 0 0 0 0 0 0
# [7,] 0 1 0 1 0 0 0 1
# [8,] 0 1 1 0 0 1 0 0
# [9,] 0 1 1 0 0 1 0 0
#[10,] 0 1 0 0 0 0 0 0
#>
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Defines functions getZ.general
########## March 8 2011: indep=T for 2x3 ###############################################################
########## Jan 10 2010: this expands to dominant model ####
getZ.general=function(xx,G,nX1,nX2) {# , mycolnames,g.model,interCols){ # G=0,1,2 (genotype score of first locus) N is number of obserbations
########## xx is columns of main effects of x1 and x2
########## G takes 0,1,or 2
ans=NULL
################# [1] Preliminary ############################################
mainCols.x2 = mycolnames = cols = NULL
if(nX1==2 & nX2==2) { mainCols.x1=1; mycolnames =c("xx11","xx21","d11") }
if(nX1==2 & nX2==3) { mainCols.x1=1; mycolnames =c("xx11","xx21","xx22","d11","d12") }
if(nX1==3 & nX2==2) { mainCols.x1=1:2; mycolnames =c("xx11","xx12","xx21","d11","d21") }
if(nX1==3 & nX2==3) { mainCols.x1=1:2; mycolnames =c("xx11","xx12","xx21","xx22","d11","d21","d12","d22") }
if(nX1==2 & nX2==2) cols=c("1 2") # x1 x2
if(nX1==2 & nX2==3) cols=c("1 2","1 3") # x1 x2_1 x2_2
if(nX1==3 & nX2==2) cols=c("1 3","2 3") # x1.1 x1.2 x2.1
if(nX1==3 & nX2==3) cols=c("1 3","2 3","1 4", "2 4") # # x1_1 x1_2 x2_1 x2_2 ---> g11 g21 g21 g22
#> xx[1:10,] # main effect terms
#> xx[1:5,]
# xx1 xx2
#[1,] 0 1
#[2,] 1 1
#[3,] 0 1
#[4,] 1 1
#[5,] 0 1
#> xx[1:10,] # main effect terms
# xx11 xx12 xx21 xx22
# [1,] 1 0 0 0
# [2,] 1 0 0 0
# [3,] 1 0 0 0
# [4,] 1 0 1 0
# [5,] 0 0 1 0
# [6,] 0 1 0 0
# [7,] 0 1 0 1
# [8,] 1 0 1 0
# [9,] 1 0 1 0
#[10,] 0 1 0 0
#> mycolnames
#[[1] "xx1" "xx2" "d12"
################ (2) Make Main effect matrix according to specified Z ############
xx2=NULL
xx2=xx
if(G==0){ # then assign genotype score 0 for all individual
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
xx2[,mainCols.x1] = 0 # for both dummy variables x11 and x12 assign 0
}#end of if(G==0){
if(G==1){ # then assign genotype score 1 for all individual : for dominant,
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
xx2[,mainCols.x1[1]] = 1 # for dominnt model, the single xx1 assigned 1 &
# for general model, only first dummy assigend 1 and second dummy 0
if(nX1==3) xx2[,mainCols.x1[2]] = 0 # for general model, the second dummy is zero
}#end of if(G==0){
if(G==2){ # then assign genotype score 2 for all individual for general model, but NOT for dominant,
#> xx[1:5,]
# xx11 xx21 xx22
#[1,] 0 0 1
#[2,] 0 0 0
#[3,] 0 1 0
#[4,] 1 0 1
#[5,] 0 1 0
#> mainCols.x1
#[1] 1
#> xx[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> mainCols.x1
#[1] 1 2
if(nX1==3) { #if general model
xx2[,mainCols.x1[1]] = 0 # first dummy is zero and second dummy is 1
xx2[,mainCols.x1[2]] = 1
}#end of if(nX1==3)
###### this doesn't do anything to dominant model
}#end of if(G==0){
table(xx2[,1])
G
#> xx2[1:5,]
# xx11 xx21 xx22
#[1,] 1 0 1
#[2,] 1 0 0
#[3,] 1 1 0
#[4,] 1 0 1
#[5,] 1 1 0
#> table(xx2[,1])
#
# 1
#1364
#> G
#[1] 1
################ (2) Interaction design matrix ################
#> interCols
#[1] "1 2"
dat.inter=myInteractMatrix2(mat=xx2,cols)
#dat.inter[1:10,]
#> xx2[1:5,]
# xx11 xx12 xx21 xx22
#[1,] 0 1 0 0
#[2,] 0 1 0 0
#[3,] 0 1 0 0
#[4,] 0 1 1 0
#[5,] 0 1 1 0
#> dat.inter[1:5,]
# [,1] [,2] [,3] [,4]
#[1,] 0 0 0 0
#[2,] 0 0 0 0
#[3,] 0 0 0 0
#[4,] 0 1 0 0
#[5,] 0 1 0 0
ans=cbind(xx2,dat.inter)
colnames(ans)=mycolnames
ans
#> ans[1:5,]
# xx11 xx21 xx22 d11 d12
#[1,] 1 0 1 0 1
#[2,] 1 0 0 0 0
#[3,] 1 1 0 1 0
#[4,] 1 0 1 0 1
#[5,] 1 1 0 1 0
}# end of getZ
#> getZ(xx,G=0,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 0 0 0 0 0 0 0 0
# [2,] 0 0 0 0 0 0 0 0
# [3,] 0 0 0 0 0 0 0 0
# [4,] 0 0 1 0 0 0 0 0
# [5,] 0 0 1 0 0 0 0 0
# [6,] 0 0 0 0 0 0 0 0
# [7,] 0 0 0 1 0 0 0 0
# [8,] 0 0 1 0 0 0 0 0
# [9,] 0 0 1 0 0 0 0 0
#[10,] 0 0 0 0 0 0 0 0
#> getZ(xx,G=1,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 1 0 0 0 0 0 0 0
# [2,] 1 0 0 0 0 0 0 0
# [3,] 1 0 0 0 0 0 0 0
# [4,] 1 0 1 0 1 0 0 0
# [5,] 1 0 1 0 1 0 0 0
# [6,] 1 0 0 0 0 0 0 0
# [7,] 1 0 0 1 0 0 1 0
# [8,] 1 0 1 0 1 0 0 0
# [9,] 1 0 1 0 1 0 0 0
#[10,] 1 0 0 0 0 0 0 0
#> getZ(xx,G=2,mycolnames)[1:10,]
# xx11 xx12 xx21 xx22 d11 d21 d12 d22
# [1,] 0 1 0 0 0 0 0 0
# [2,] 0 1 0 0 0 0 0 0
# [3,] 0 1 0 0 0 0 0 0
# [4,] 0 1 1 0 0 1 0 0
# [5,] 0 1 1 0 0 1 0 0
# [6,] 0 1 0 0 0 0 0 0
# [7,] 0 1 0 1 0 0 0 1
# [8,] 0 1 1 0 0 1 0 0
# [9,] 0 1 1 0 0 1 0 0
#[10,] 0 1 0 0 0 0 0 0
#>
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