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Data X:
14 10 1 19 13 2 17 14 2 20 13 2 15 14 1 19 14 1 20 12 1 18 11 1 15 12 1 14 14 2 16 13 2 19 13 2 18 12 1 17 12 1 19 13 1 17 13 1 19 12 2 20 13 1 19 10 2 16 14 1 16 10 2 18 10 1 16 14 1 17 14 2 20 13 2 19 12 2 7 12 2 16 12 2 16 10 2 18 14 2 17 8 2 19 11 1 16 10 2 13 14 1 16 12 2 12 14 2 17 13 2 17 13 2 17 13 2 16 12 1 16 10 2 14 14 1 16 11 2 13 10 1 16 13 2 14 12 2 19 13 1 18 11 2 14 10 1 18 14 2 15 7 1 17 13 1 19 13 2 18 14 2 15 13 1 15 11 2 20 14 1 19 14 1 18 12 2 15 13 2 20 14 1 17 13 2 19 12 1 20 10 2 18 12 1 17 9 1 18 12 2 17 13 1 20 13 2 16 11 1 14 12 1 15 11 1 20 12 2 17 12 2 17 13 2 18 8 2 20 13 1 16 8 1 18 13 1 15 12 2 18 15 1 20 14 1 14 11 1 15 10 1 17 14 2 18 10 2 20 15 2 17 11 2 16 12 1 11 13 1 15 12 2 18 9 2 16 14 2 18 14 1 15 12 2 17 15 1 19 11 1 16 12 2 14 11 1
Names of X columns:
TDVCSUM ITHSUM geslacht
R Code
library(corpcor) x <- t(y) (r1 <- pcor.shrink(x)) (r0 <- cor(x)) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Pearson Correlation Matrix',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('r0'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable0.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Partial Pearson Correlation Matrix (Shrinkage Method)',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,paste('<pre>',RC.texteval('r1'),'</pre>',sep='')) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab')
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