tmpar <- par( mar=c(3.1,3.1,0,0.5), mfrow=c(1,2) )
on.exit( par( tmpar ) )

mplot( jitter( predict( cloning.fit ) ), resid( cloning.fit ),
   group = cloning$code, xlab = "", ylab = "" )
mtext( "(a) all 13 predicteds", 1, 2 )
mtext( "residuals", 2, 2 )
abline( h = 0, lty = 2 )
abline( h = c(-1,1) * std.dev( cloning.fit ), lty = 2 )

tmp1 <- is.na(pmatch(as.character(cloning$clone),c("b","c","g","j"),dup=T))
#cloning$clone==3 | cloning$clone==7
#cloning$clone==2 | cloning$clone==10

cloning.fit1 <- aov( titer ~ clone, cloning, subset = tmp1 )
mplot( jitter( predict( cloning.fit1 ) ), resid( cloning.fit1 ),
   group = cloning$code[ tmp1 ], ylim = range( resid( cloning.fit ) ),
   xlab = "", ylab = "" )
mtext( "(b) only 9 predicteds", 1, 2 )
axis(1,1000)
abline( h = 0, lty=2 )
abline( h = c(-1,1) * std.dev( cloning.fit1 ), lty = 2 )

#rm( tmp1, tmp2, tmp3 )