ff0708)
which has 7 experimental factors each at two levels. The design
consists of 8 runs and is a 1/16 fraction of the full factorial. The
noise design is
ff0304. The control and noise designs are combined to
form a robust design that has 32 runs.
Engel, J. (1992), Modeling variation in industrial experiments, Applied Statistics 41, 579-593.
Used by permission of Carfax Publishing, Inc.
# This design is already available in S-PLUS under
# the name mold.df. The following commands were used
# to create the data frame:
cont.des <- oa.design(rep(2,7),min.resid.df=0)
nois.des<-fac.design(rep(2,3),c('M','N','O'),
fraction=1/2)
mold.des <- robust.design(cont.des,nois.des)
mold.shrink <- c(2.2,0.3,0.5,2.0,3.0,2.1,4.0,
2.0,2.3,0.3,2.8,2.0,3.0,3.1,2.2,1.8,2.3,
2.7,0.4,1.8,3.0,1.0,4.6,1.9,2.1,2.5,3.1,
1.9,3.1,4.2,1.9,1.9)
mold.df <- cbind(mold.des,shrink=mold.shrink)
# Sample analysis
summary(mold.df)
plot(mold.df)
mold.sn <- robust.sn(mold.df)
mold.sn
plot(mold.sn,"shrink.mean",data.pts=F)
plot(mold.sn,"shrink.target")
mold.fac <- fac.aov(mold.sn)
mold.fac
summary(mold.fac)
moldsd.fac <- fac.aov(response=shrink.sd,mold.sn)
moldsn.fac <- fac.aov(response=shrink.target,mold.sn)
moldsn2.fac <- fac.aov(response=shrink.meanl,mold.sn)
pareto(mold.fac)
pareto(moldsn.fac)
qqnorm(mold.fac,label=3)
qqnorm(moldsn.fac)
acplot(mold.fac)
acplot(moldsn.fac)
mold.facs <- fac.aov(shrink.mean~A+C+H,mold.sn)
pareto(mold.facs, "mse")
summary(mold.facs)
newdata <- data.frame(A = seq(-1,1,by=.1),
C = rep(-1,21), H = rep(-1,21))
print(predict(mold.facs))
mold.pred <- predict(mold.facs, newdata,
numeric.levels = list(A = c(-1, 1),
C = c(-1, 1), H = c(-1, 1)))
mold.pred