Nothing
vector.alpha <-
function(x, set, type="cor", CI=.95, CItype="xci", minval=-1.0) {
comb.data <- data.frame(cbind(x, set))
comp.data <- subset(comb.data, complete.cases(comb.data))
N <- nrow(comp.data)
# Alpha for vector of correlations
if(type=="cor") {
std.data <- data.frame(scale2(comp.data))
Zcross.prod <- std.data$x * std.data[2:length(std.data)]
tZcross.prod <- data.frame(t(Zcross.prod))
Zcov.mat <- cov(tZcross.prod)
Zcor.mat <- cor(tZcross.prod)
Zavg.r <- mean(Zcor.mat[upper.tri(Zcor.mat)])
Zalpha <- alpha.cov(Zcov.mat)
Zalpha <- ifelse(Zalpha >= minval, Zalpha, minval)
if(CItype=="xci") {
ZCIs <- alpha.xci(Zalpha, k=N, n=ncol(set), CI=CI)
}
if(CItype=="aci") {
ZCIs <- alpha.aci(Zalpha, k=N, n=ncol(set), CI=CI)
}
out <- rbind(N, Zavg.r, Zalpha, ZCIs[1], ZCIs[2])
}
# Alpha for vector of covariances
if(type=="cov") {
cnt.data <- data.frame(scale2(comp.data, scale=F))
Ccross.prod <- cnt.data$x * cnt.data[2:length(cnt.data)]
tCcross.prod <- data.frame(t(Ccross.prod))
Ccov.mat <- cov(tCcross.prod)
Ccor.mat <- cor(tCcross.prod)
Cavg.r <- mean(Ccor.mat[upper.tri(Ccor.mat)])
Calpha <- alpha.cov(Ccov.mat)
Calpha <- ifelse(Calpha >= minval, Calpha, minval)
if(CItype=="xci") {
CCIs <- alpha.xci(Calpha, k=N, n=ncol(set), CI=CI)
}
if(CItype=="aci") {
CCIs <- alpha.aci(Calpha, k=N, n=ncol(set), CI=CI)
}
out <- rbind(N, Cavg.r, Calpha, CCIs[1], CCIs[2])
}
# Alpha for X-Y Beta Coefficients (X predicts each Y; same as covariances)
if(type=="XY") {
cnt.data <- data.frame(scale2(comp.data, scale=F))
Ccross.prod <- cnt.data$x * cnt.data[2:length(cnt.data)]
XYelems <- Ccross.prod / (var(comp.data$x)*(N-1)/N)
tXYelems <- data.frame(t(XYelems))
XYcov.mat <- cov(tXYelems)
XYcor.mat <- cor(tXYelems)
XYavg.r <- mean(XYcor.mat[upper.tri(XYcor.mat)])
XYalpha <- alpha.cov(XYcov.mat)
XYalpha <- ifelse(XYalpha >= minval, XYalpha, minval)
if(CItype=="xci") {
XYCIs <- alpha.xci(XYalpha, k=N, n=ncol(set), CI=CI)
}
if(CItype=="aci") {
XYCIs <- alpha.aci(XYalpha, k=N, n=ncol(set), CI=CI)
}
out <- rbind(N, XYavg.r, XYalpha, XYCIs[1], XYCIs[2])
}
# Alpha for Y-X Beta Coefficients (Each Y predicts X)
if(type=="YX") {
cnt.data <- data.frame(scale2(comp.data, scale=F))
y.vars <- diag(var(cnt.data[2:length(cnt.data)]))*(N-1)/N
y.mat <- matrix(rep(y.vars, N), nrow=N, ncol=length(y.vars), byrow=T)
YXelems <- cnt.data$x * cnt.data[2:length(cnt.data)] / y.mat
tYXelems <- data.frame(t(YXelems))
YXcov.mat <- cov(tYXelems)
YXcor.mat <- cor(tYXelems)
YXavg.r <- mean(YXcor.mat[upper.tri(YXcor.mat)])
YXalpha <- alpha.cov(YXcov.mat)
YXalpha <- ifelse(YXalpha >= minval, YXalpha, minval)
if(CItype=="xci") {
YXCIs <- alpha.xci(YXalpha, k=N, n=ncol(set), CI=CI)
}
if(CItype=="aci") {
YXCIs <- alpha.aci(YXalpha, k=N, n=ncol(set), CI=CI)
}
out <- rbind(N, YXavg.r, YXalpha, YXCIs[1], YXCIs[2])
}
colnames(out) <- c("Results")
rownames(out) <- c("N", "Average r", "Alpha", "Lower Limit", "Upper Limit")
return(out)
}
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