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Data X:
99.2 96.7 101.0 99.0 98.1 100.1 100.0 100.0 100.0 111.6 104.9 90.6 122.2 104.9 86.5 117.6 109.5 89.7 121.1 110.8 90.6 136.0 112.3 82.8 154.2 109.3 70.1 153.6 105.3 65.4 158.5 101.7 61.3 140.6 95.4 62.5 136.2 96.4 63.6 168.0 97.6 52.6 154.3 102.4 59.7 149.0 101.6 59.5 165.5 103.8 61.3
Names of X columns:
A B C
Number of Factors
Chart options
R Code
library(psych) par1 <- as.numeric(par1) x <- t(x) nrows <- length(x[,1]) ncols <- length(x[1,]) y <- array(as.double(x[1:nrows,2:ncols]),dim=c(nrows,ncols-1)) colnames(y) <- colnames(x)[2:ncols] rownames(y) <- x[,1] y fit <- principal(y, nfactors=par1, rotate='varimax') fit fs <- factor.scores(y,fit) fs bitmap(file='test1.png') fa.diagram(fit) dev.off() bitmap(file='test2.png') plot(fs,pch=20) text(fs,labels=rownames(y),pos=3) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Rotated Factor Loadings',par1+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variables',1,TRUE) for (i in 1:par1) { a<-table.element(a,paste('Factor',i,sep=''),1,TRUE) } a<-table.row.end(a) for (j in 1:length(fit$loadings[,1])) { a<-table.row.start(a) a<-table.element(a,rownames(fit$loadings)[j],header=TRUE) for (i in 1:par1) { a<-table.element(a,round(fit$loadings[j,i],3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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1 seconds
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Big Analytics Cloud Computing Center
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