cbind( zz <- 1:15, middle(zz), ends(zz) )
data( sbp )
bp <- subset( sbp, repl==1 & meth!="J" )
bp <- Meth( bp )
summary( bp )
plot( bp )
bw <- to.wide( bp )
with( bw, corr.measures( R, S ) )
# See how it gets better with less and less data:
summ.corr <-
rbind(
with( subset( bw, middle( R+S, 0.6 ) ), corr.measures( R, S ) ),
with( subset( bw, middle( R+S, 0.4 ) ), corr.measures( R, S ) ),
with( bw , corr.measures( R, S ) ),
with( subset( bw, ends( R+S, 0.3 ) ), corr.measures( R, S ) ),
with( subset( bw, ends( R+S, 0.4 ) ), corr.measures( R, S ) ),
with( subset( bw, ends( R+S, 0.6 ) ), corr.measures( R, S ) ),
with( subset( bw, ends( R+S, 0.8 ) ), corr.measures( R, S ) ) )
rownames( summ.corr ) <- c("middle 40%",
"middle 60%",
"total",
"outer 70%",
"outer 60%",
"outer 40%",
"outer 20%")
summ.corr
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