We would like to confound the three factor interaction abc with the blocks.
Block 1: 1,ab,ac,bc
Block 2: a,b,c,abc
We would like to confound the two factor interactions ab, ac and bc with the blocks.
Block 1: 1, abc
Block 2: a, bc
Block 3: b ac
Block 4: c ,ab
a<- c(-1, 1, -1, 1,-1, 1, -1, 1)
b<- c(-1, -1, 1,1,-1, -1, 1,1)
c<- c(-1,-1,-1,-1, 1, 1, 1, 1)
ab<-c(1,-1,-1,1,1,-1,-1,1)
ac<-c(1,-1,1,-1,-1,1,-1,1)
bc<-c(1,1,-1,-1,-1,-1,1,1)
abc<-c(-1,1,1,-1,1,-1,-1,1)
tool_life<-c(22,
32,
35,
55,
44,
40,
60,
39)
dat<-data.frame(a,b,c,ab,ac,bc,abc,tool_life)
dat
## a b c ab ac bc abc tool_life
## 1 -1 -1 -1 1 1 1 -1 22
## 2 1 -1 -1 -1 -1 1 1 32
## 3 -1 1 -1 -1 1 -1 1 35
## 4 1 1 -1 1 -1 -1 -1 55
## 5 -1 -1 1 1 -1 -1 1 44
## 6 1 -1 1 -1 1 -1 -1 40
## 7 -1 1 1 -1 -1 1 -1 60
## 8 1 1 1 1 1 1 1 39
library(DoE.base)
## Loading required package: grid
## Loading required package: conf.design
## Registered S3 method overwritten by 'DoE.base':
## method from
## factorize.factor conf.design
##
## Attaching package: 'DoE.base'
## The following objects are masked from 'package:stats':
##
## aov, lm
## The following object is masked from 'package:graphics':
##
## plot.design
## The following object is masked from 'package:base':
##
## lengths
model<-lm(tool_life~a*b*c, data=dat)
coef(model)
## (Intercept) a b c a:b a:c
## 40.875 0.625 6.375 4.875 -0.875 -6.875
## b:c a:b:c
## -2.625 -3.375
halfnormal(model)
## no significant effects
From the halfnormal plot, we can see that there no significant effects.
Since we can see from the halfnormal plot that none of the effects are significant, our blocks also don’t seem to be significant.