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Data X:
'Economic well being' 0.0. 0.234543935587 0.2 0.819543935587 0.3 0.30102999566398 0.65321251377534 1.6232492903979 3 1 3 'Human capital' 3.40602894496362 3.66304097489397 2.1 0.25527250510331 1.83884909073726 2.79518458968242 3 5 4 'Income to needs ratio' 1.02325245963371 2.25406445291434 9.1 -0.15490195998574 1.43136376415899 2.25527250510331 4 4 4 'Community poverty' -1.69897000433602 -0.52287874528034 15.8 0.5910646070265 1.27875360095283 1.54406804435028 1 1 1 'Age' 2.20411998265592 2.22788670461367 5.2 0 1.48287358360875 2.59328606702046 4 5 4 'Gender' 0.51851393987789 1.40823996531185 10.9 0.55630250076729 1.44715803134222 1.79934054945358 1 2 1 'Ethnicity' 1.71733758272386 2.64345267648619 8.3 0.14612803567824 1.69897000433602 2.36172783601759 1 1 1 'Single-parent status' -0.36653154442041 0.80617997398389 11 0.17609125905568 0.84509804001426 2.04921802267018 5 4 4 'Confounding conditions' 2.66745295288995 2.62634036737504 3.2 -0.15490195998574 1.47712125471966 2.44870631990508 5 5 5 'General health status' -1.09691001300806 0.079181246047625 6.3 0.32221929473392 0.54406804435028 1.6232492903979 1 1 1 'Specific health problems' -0.10237290870956 0.54406804435028 6.6 0.61278385671974 0.77815125038364 1.6232492903979 2 2 2
Names of X columns:
label logWb logWbr SWS logPS logL logtg P S D
Number of Factors
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R Code
par1 <- '2' 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] print(y) fit <- principal(y, nfactors=par1, rotate='varimax') fit fs <- factor.scores(y,fit) fs bitmap(file='test2.png') plot(fs$scores,pch=20) text(fs$scores,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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