Package 'daewr' reference manual

Title:	Design and Analysis of Experiments with R
Description:	Contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.
Authors:	John Lawson [aut, cre], Gerhard Krennrich [aut], Ruben Amoros [ctr]
Maintainer:	John Lawson <[email protected]>
License:	GPL-2
Version:	1.2-11
Built:	2025-02-19 03:13:11 UTC
Source:	https://github.com/cran/daewr

Data frames and functions for Design and Analysis of Experiments with R

Description

This package contains the data sets and functions from the book Design and Analysis of Experiments with R published by CRC in 2013.

Author(s)

John Lawson

Maintainer: John Lawson <[email protected]>

References

J. Lawson, Design and Analysis of Experiments with R, CRC 2013.

Alternate 16 run screening designs

Description

Recalls Jones and Montgomery's 16 run screening designs from data frames

Usage

Altscreen(nfac, randomize=FALSE)
Altscreen(nfac, randomize=FALSE)

Arguments

`nfac`	input- an integer
`randomize`	input - logical

Value

a data frame containing the alternate screening design

Author(s)

John Lawson

References

Jones, B. and Montgomery, D. C. (2010) "Alternatives to resolution IV screening designs in 16 runs", Int. J. Experimental Design and Process Optimization, Vol 1, No. 4, 2010.

Two-period crossover study of antifungal agent

Description

Data from the Two-period crossover study of an antifungal agent in chapter 9 of Design and Analysis of Experiments with R

Usage

data(antifungal)data(antifungal)

Format

A data frame with 34 observations on the following 5 variables.

Group: a factor with levels 1 2
Subject: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18
Period: a factor with levels 1 2
Treat: a factor with levels A B
pl: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(antifungal)
data(antifungal)

apolipoprotein survey varaince component study

Description

Data from the apolipoprotein survey variance component study of Chapter 5 in Design and Analysis of Experiments with R

Usage

data(Apo)data(Apo)

Format

A data frame with 30 observations on the following 2 variables.

lab: a factor with levels A B C D
conc: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Apo)
data(Apo)

Confounded apple slice browning experiment

Description

Data from the confounded apple slice browning experiment in chapter 7 of Design and Analysis of Experiments with R

Usage

data(apple)data(apple)

Format

A data frame with 24 observations on the following 4 variables.

Block: a factor with levels 1 2 3 4
A: a factor with levels 0 1 2 3
B: a factor with levels 0 1 2
rating: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(apple)
data(apple)

$2^{(7-3)}$ arsenic removal experiment

Description

Data from the $2^{(7-3)}$ arsenic removal experiment in chapter 6 of Design and Analysis of Experiments with R

Usage

data(arso)data(arso)

Format

A data frame with 8 observations on the following 8 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
E: a factor with levels -1 1
F: a factor with levels -1 1
G: a factor with levels -1 1
y1: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(arso)
data(arso)

$2^{(7-3)}$ arsenic removal experiment augmented with mirror image

Description

Data from the $2^{(7-3)}$ arsenic removal experiment augmented with mirror image in chapter 6 of Design and Analysis of Experiments with R

Usage

data(augm)data(augm)

Format

A data frame with 8 observations on the following 8 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
fold: a factor with levels original mirror
D: a factor with levels -1 1
E: a factor with levels -1 1
F: a factor with levels -1 1
G: a factor with levels -1 1
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(augm)
data(augm)

Confounded Block Dishwashing Experiment

Description

Data from the Confounded Block Dishwashing Experiment in chapter 7 of Design and Analysis of Experiments with R

Usage

data(Bdish)data(Bdish)

Format

A data frame with 16 observations on the following 5 variables.

Blocks: a factor with levels 1 2 3 4
A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
y: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Bdish)
data(Bdish)

Confounded block fractional mouse growth experiment

Description

Data from the Confounded block fractional factorial mouse growth experiment in chapter 7 of Design and Analysis of Experiments with R

Usage

data(Bff)data(Bff)

Format

A data frame with 16 observations on the following 5 variables.

Blocks: a factor with levels 1 2 3 4 5 6 7 8
A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
E: a factor with levels -1 1
F: a factor with levels -1 1
G: a factor with levels -1 1
H: a factor with levels -1 1
weight: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Bff)
data(Bff)

mouse liver enzyme experiment

Description

Data from the mouse liver enzyme experiment in chapter 4 of Design and Analysis of Experiments with R

Usage

data(bha)data(bha)

Format

A data frame with 16 observations on the following 4 variables.

block: a factor with levels 1 2
strain: a factor with levels A/J 129O1a NIH BALB/c
treat: a factor with levels treated control
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(bha)
data(bha)

Balanced incomplete blocksize

Description

This function computes the number of blocks, treatment frequency and lambda for a potential BIB design

Usage

BIBsize(t,k)
BIBsize(t,k)

Arguments

`t`	input - number of levels of the treatment factor
`k`	input - blocksize or number of experimental units per block

Value

a list containing the b=number of blocks, r=number of treatment replicates and lambda for a potential BIB design with t levels of treatment factor and blocksize k.

Author(s)

John Lawson

Extra-period crossover bioequivalence study

Description

Data from the extra-period crossover bioequivalence study in chapter 9 of Design and Analysis of Experiments with R

Usage

data(bioequiv)data(bioequiv)

Format

A data frame with 108 observations on the following 5 variables.

Group: a factor with levels 1 2
Subject: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 21 23 24 25 26 27 28 30 31 32 33 34 35 36 120 122 129
Period: a factor with levels 1 2 3
Treat: a factor with levels A B
Carry: a factor with levels none A B
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(bioequiv)
data(bioequiv)

Latin Square bioequivalence experiment

Description

Data from the Latin Square bioequivalence experiment in chapter 4 of Design and Analysis of Experiments with R

Usage

data(bioeqv)data(bioeqv)

Format

A data frame with 9 observations on the following 4 variables.

Period: a factor with levels 1 2 3
Subject: a factor with levels 1 2 3
Treat: a factor with levels A B C
AUC: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(bioeqv)
data(bioeqv)

Variance component study of calcium in blood serum

Description

Data from the Variance component study of calcium in blood serum in chapter 5 of Design and Analysis of Experiments with R

Usage

data(blood)data(blood)

Format

A data frame with 27 observations on the following 3 variables.

sol: a factor with levels 1 2 3 4
lab: a factor with levels A B C
calcium: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(blood)
data(blood)

Box and Meyer's unreplicated $2^4$ from Chapter 3

Description

Data from Box and Meyer's unreplicated $2^4$ in chapter 3 of Design and Analysis of Experiments with R

Usage

data(BoxM)data(BoxM)

Format

A data frame with 16 observations on the following 4 variables.

A: a numeric vector containing the coded (-1,1) levels of factor A
B: a numeric vector containing the coded (-1,1) levels of factor B
C: a numeric vector containing the coded (-1,1) levels of factor C
D: a numeric vector containing the coded (-1,1) levels of factor D
y: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

References

Box, G. E. P. "George's Column", Quality Engineering, Vol. 3, pp. 405-410.

Examples

data(BoxM)
data(BoxM)

blood pressure monitor experiment

Description

Data from the blood pressure monitor experiment experiment in Chapter 7 of Design and Analysis of Experiments with R

Usage

data(BPmonitor)data(BPmonitor)

Format

A data frame with 12 observations on the following 3 variables.

Block: a factor with levels 1 2 3 4 5 6
Treatment: a factor with levels "P" "A" "B" "C"
pressure: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(BPmonitor)
data(BPmonitor)

Bread rise experiment data from Chapter 2

Description

Data from the bread rise experiment in chapter 2 of Design and Analysis of Experiments with R

Usage

data(bread)data(bread)

Format

A data frame with 12 observations on the following 3 variables.

loaf: a numeric vector
time: a numeric vector
height: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(bread)
data(bread)

Split-Plot response surface for cake baking experiment

Description

Data from the Split-Plot response surface for cake baking experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(cakeb)data(cakeb)

Format

A data frame with 11 observations on the following 6 variables.

Ovenrun: a factor with levels 1 2 3 4
x1: a numeric vector
x2: a numeric vector
y: a numeric vector
x1sq: a numeric vector
x2sq: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(cakeb)
data(cakeb)

CCD design for cement workability experiment

Description

Data from the CCD design for cement workability experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(cement)data(cement)

Format

A data frame with 20 observations on the following 4 variables.

Block: a factor with levels 1 2
x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(cement)
data(cement)

Chemical process experiment data from Chapter 3

Description

Data from the Chemical process experiment in chapter 3 of Design and Analysis of Experiments with R

Usage

data(chem)data(chem)

Format

A data frame with 16 observations on the following 4 variables.

A: a numeric vector containing the coded (-1,1) levels of factor A
B: a numeric vector containing the coded (-1,1) levels of factor B
C: a numeric vector containing the coded (-1,1) levels of factor C
D: a numeric vector containing the coded (-1,1) levels of factor D
y: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(chem)
data(chem)

Williams' crossover design for sprinting experiment

Description

Data from the Williams' crossover design for sprinting experiment in chapter 9 of Design and Analysis of Experiments with R

Usage

data(chipman)data(chipman)

Format

A data frame with 36 observations on the following 5 variables.

Square: a factor with levels 1 2
Group: a factor with levels 1 2 3
Subject: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12
Period: a factor with levels 1 2 3
Treat: a factor with levels 1 2 3
Carry: a factor with levels 0 1 2 3
Time: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(chipman)
data(chipman)

CO emmisions experiment data from Chapter 3

Description

Data from the CO emissions experiment in chapter 3 of Design and Analysis of Experiments with R

Usage

data(COdata)data(COdata)

Format

A data frame with 18 observations on the following 3 variables.

Eth: a factor with levels 0.1 0.2 0.3
Ratio: a factor with levels 14 15 16
CO: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(COdata)
data(COdata)

This function makes a colormap of correlations in a design matrix

Description

This function makes a colormap of the correlations of a design matrix stored in the data frame design

Usage

colormap(design, mod)
colormap(design, mod)

Arguments

`design`	input - a data frame containing columns of the numeric factor levels
`mod`	input - a number indicationg the model for the colormap 1 = linear model containing only the terms in the dataframe 2 = linear model plus two factor interactions 3 = linear model plus 2 and 3 factor interactions 4 = linear model plus 2, 3, and 4 factor interactions

Author(s)

John Lawson

Examples

## The function is currently defined as
function(design,mod) {
##################### Inputs ###########################################
# design - a data frame containing columns of the numeric factor levels
# mod - the model for the color plot of correlations
#    1 = Linear model containing only the terms in the data frame
#    2 = Linear model plus two factor interactions
#    3 = Linear model plus 2 and 3 factor interactions
#    4 = Linear model plus 2, 3 and 4 factor interactions
########################################################################
y<-runif(nrow(design),0,1)
if(mod==1) {test <- model.matrix(lm(y~(.),data=design))}
if(mod==2) {test <- model.matrix(lm(y~(.)^2,data=design))}
if(mod==3) {test <- model.matrix(lm(y~(.)^3,data=design))}
if(mod==4) {test <- model.matrix(lm(y~(.)^4,data=design))}
names<-colnames(test)
names<-gsub(':','',names)
names<-gsub('1','',names)
colnames(test)<-names
cmas<-abs(cor(test[,ncol(test):2]))
cmas<-cmas[c((ncol(cmas)):1), ]
rgb.palette <- colorRampPalette(c("white", "black"), space = "rgb")
levelplot(cmas, main="Map of absolute correlations", xlab="", ylab="", col.regions=rgb.palette(120),
            cuts=100, at=seq(0,1,0.01),scales=list(x=list(rot=90))) }
## The function is currently defined as
function(design,mod) {
##################### Inputs ###########################################
# design - a data frame containing columns of the numeric factor levels
# mod - the model for the color plot of correlations
#    1 = Linear model containing only the terms in the data frame
#    2 = Linear model plus two factor interactions
#    3 = Linear model plus 2 and 3 factor interactions
#    4 = Linear model plus 2, 3 and 4 factor interactions
########################################################################
y<-runif(nrow(design),0,1)
if(mod==1) {test <- model.matrix(lm(y~(.),data=design))}
if(mod==2) {test <- model.matrix(lm(y~(.)^2,data=design))}
if(mod==3) {test <- model.matrix(lm(y~(.)^3,data=design))}
if(mod==4) {test <- model.matrix(lm(y~(.)^4,data=design))}
names<-colnames(test)
names<-gsub(':','',names)
names<-gsub('1','',names)
colnames(test)<-names
cmas<-abs(cor(test[,ncol(test):2]))
cmas<-cmas[c((ncol(cmas)):1), ]
rgb.palette <- colorRampPalette(c("white", "black"), space = "rgb")
levelplot(cmas, main="Map of absolute correlations", xlab="", ylab="", col.regions=rgb.palette(120),
            cuts=100, at=seq(0,1,0.01),scales=list(x=list(rot=90))) }

Table 12.21 Experiment with Elastometric Connector

Description

Data from the Single Array Experiment with an Elastometric Connector in Chapter 12 of Design and Analysis of Experiments with R. The control and noise factors are in coded levels.

Usage

data(connector)data(connector)

Format

A data frame with 32 observations on the following 8 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

  data(connector)
data(connector)

Control factor array and summary statistics for controller circuit design experiment

Description

Data from the control factor array and summary statistics for controller circuit design experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(cont)data(cont)

Format

A data frame with 18 observations on the following 6 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
F: a numeric vector
lns2: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(cont)
data(cont)

Split-plot response surface for ceramic pipe experiment

Description

Data from the Split-plot response surface for ceramic pipe experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(cpipe)data(cpipe)

Format

A data frame with 48 observations on the following 6 variables.

WP: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12
A: a numeric vector
B: a numeric vector
P: a numeric vector
Q: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(cpipe)
data(cpipe)

paecilomyces variotii culture experiment

Description

Data from the paecilomyces variotii culture experiment experiment in chapter 6 of Design and Analysis of Experiments with R

Usage

data(culture)data(culture)

Format

A data frame with 16 observations on the following 9 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
E: a factor with levels -1 1
F: a factor with levels -1 1
G: a factor with levels -1 1
H: a factor with levels -1 1
y1: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(culture)
data(culture)

Repeated measures study with dairy cow diets

Description

Data from the Repeated measures study with dairy cow diets in chapter 9 of Design and Analysis of Experiments with R (compact format)

Usage

data(dairy)data(dairy)

Format

A data frame with 120 observations on the following 5 variables.

Diet: a factor with levels "Barley" "Mixed" "Lupins"
pr1: a numeric vector
pr2: a numeric vector
pr3: a numeric vector
pr4: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(dairy)
data(dairy)

Definitive Screening Designs

Description

Recalls Jones and Nachtsheim's Definitive screening designs for 3-level factors and 3-level factors with added 2-level categorical factors.

Usage

DefScreen(m, c=0, center=0, randomize=FALSE)
DefScreen(m, c=0, center=0, randomize=FALSE)

Arguments

`m`	input- an integer, the m=number of 3-level factors
`c`	input- an integer, the m=number of 2-level categorical factors, default is zero if not supplied
`center`	input- an integer, the number of extra center points. This must be zero when c>0
`randomize`	input - logical

Value

a data frame containing the definitive screening design with 3-level factors first followed by 2-level factors.

Author(s)

John Lawson

References

Jones, B. and Nachtsheim, C. J. (2011) "A Class of Three Level Designs for Definitive Screening in the Presence of Second-Order Effects", Journal of Quality Technology, Vol 43, No. 1, 2011, pp 1-15. Jones, B. and Nachtsheim, C. J. (2013) "Definitive Screening Designs with Added Two-Level Categorical Factors", Journal of Quality Technology, Vol 44, No. 2, 2013, pp. 121-129.

Data from rat behavior experiment in Chapter 4

Description

Data from rat behavior experiment in Chapter 4 of Design and Analysis of Experiments with R

Usage

data(drug)data(drug)

Format

A data frame with 50 observations on the following 3 variables.

rat: a factor with levels 1 2 3 4 5 6 7 8 9 10
dose: a factor with levels 0.0 0.5 1.0 1.5 2.0
rate: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(drug)
data(drug)

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 1 whole plot factor and 1 sub-plot factor from a catalog

Usage

EEw1s1(des, randomize=FALSE)
EEw1s1(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 1 whole plot factor and 2 sub-plot factors from a catalog

Usage

EEw1s2(des, randomize=FALSE)
EEw1s2(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 1 whole plot factor and 3 sub-plot factors from a catalog

Usage

EEw1s3(des, randomize=FALSE)
EEw1s3(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 2 whole plot factors and 1 sub-plot factor from a catalog

Usage

EEw2s1(des, randomize=FALSE)
EEw2s1(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 2 whole plot factors and 1 sub-plot factor from a catalog

Usage

EEw2s2(des, randomize=FALSE)
EEw2s2(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 2 whole plot factors and 1 sub-plot factor from a catalog

Usage

EEw2s3(des, randomize=FALSE)
EEw2s3(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

D-efficient Estimation Equivalent Response Surface Designs

Description

Recalls Jones and Goos JQT Estimation Equivalent Response Surface Designs for 3 whole plot factors and 1-2 sub-plot factors from a catalog

Usage

EEw3(des, randomize=FALSE)
EEw3(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Jones, B. and Goos, P.(2012) "An Algorithm for Finding D-Efficient Equivalent-Estimation Second-Order Split Plot Designs", Journal of Quality Technology, Vol 44, No. 4, pp281-303, 2012.

Single array and raw response for silicon layer growth experiment

Description

Data from the single array and raw response for silicon layer growth experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(eptaxr)data(eptaxr)

Format

A data frame with 64 observations on the following 9 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
H: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(eptaxr)
data(eptaxr)

Control array and variance of response for silicon layer growth experiment

Description

Data from the control array and variance of response for silicon layer growth experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(eptaxs2)data(eptaxs2)

Format

A data frame with 16 observations on the following 9 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
H: a numeric vector
s2: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(eptaxs2)
data(eptaxs2)

Control array and mean response for silicon layer growth experiment

Description

Data from the control array and mean response for silicon layer growth experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(eptaxyb)data(eptaxyb)

Format

A data frame with 16 observations on the following 9 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
H: a numeric vector
ybar: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(eptaxyb)
data(eptaxyb)

F-Distribution critical values

Description

Gets F-distribution critical values

Usage

Fcrit(alpha, nu1, nu2)
Fcrit(alpha, nu1, nu2)

Arguments

`alpha`	input- right tail area beyond critical value
`nu1`	input - numerator degrees of freedom for F-distribution
`nu2`	input - denominator degrees of freedom for F-distribution

Value

returned critical value

Author(s)

John Lawson

Subsequent steps in a forward stepwise regression that preserves model hierarchy

Description

This function performs a single step of a hierarchical forward stepwise regression by entering additional term(s) to a model already created by ihstep or fhstep. If an interaction or quadratic term is entered first, the parent main effects are also entered into the model. This function is called by HierAFS.R

Usage

fhstep(y,des,m,c,prvm)
fhstep(y,des,m,c,prvm)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1 i.e., letters of the alphabet. The m three-level factors always preceed the c two-level factors in the design.
`m`	input - this is an integer equal to the number of three-level factors in the design
`c`	input - this is an integer equal to the number of two-level factors in the design. Note m+c must be equal to the number of columns of des.
`prvm`	input - this is a vector of text names of the terms in the model. This is created as the value resulting from running ihstep or fhstep.

Value

returned vector of terms entered in the model at this step.

Author(s)

John Lawson

Forward Stepwise modeling taking into account special structure of Definitive Screening Design

Description

This function performs a single step of a forward stepwise regression by entering an additional 2nd order term to a model already created by FitDefSc.R or fhstepDS.R This function is called by FitDefSc.R

Usage

fhstepDS(y,des,m,c,prvm)
fhstepDS(y,des,m,c,prvm)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1 i.e., letters of the alphabet. The m three-level factors always preceed the c two-level factors in the design.
`m`	input - this is an integer equal to the number of three-level factors in the design
`c`	input - this is an integer equal to the number of two-level factors in the design. Note m+c must be equal to the number of columns of des.
`prvm`	input - this is a vector of text names of the terms in the model. This is created as the value resulting from running ihstep or fhstep.

Value

returned vector of terms entered in the model at this step.

Author(s)

John Lawson

Find first term to enter forward stepwise regression that preserves model hierarchy

Description

This function finds the first term to enter a hierarchical forward stepwise regression. If the term is an interaction or quadratic term, the parent main effects are also included. This function is called by ihstep.R

Usage

firstm(y,des)
firstm(y,des)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1. The m three-level factors always preceed the c two-level factors in the design.

Value

returned vector of terms to be entered in the model at the first step.

Author(s)

John Lawson

An Effective Design Based Model Fitting Method for Definitive Screening Designs

Description

This function performs fits a model to a Definitive Screeing Design by first restricting main effects to the smallest main effects and those significant at at least the .20 level in a main effects model. Next forward stepwise selection is used to enter 2 factor interactions and quadratic effects.

Usage

FitDefSc(y,design,alpha=.05)
FitDefSc(y,design,alpha=.05)

Arguments

`y`	input - this is a vector containing a single numeric column of response data.
`design`	input - this is a data frame containing the numeric columns of the candidate independent variables created by the DefScreen function with only numerical factors i.e. c=0. The factor names or colnames(design) should always be of length 1 (for example letters of the alphabet "A", "B", etc.)
`alpha`	input - alpha to enter in the forward stepwise regression with second order candidates should be between 0.05 and 0.20

Author(s)

John Lawson

Find first term to enter forward stepwise regression that preserves model hierarchy

Description

Usage

fnextrm(y,des,prvm)
fnextrm(y,des,prvm)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1. The m three-level factors always preceed the c two-level factors in the design.
`prvm`	input - this is a vector of text names of the terms in the model. This is created as the value resulting from running ihstep or fhstep.

Value

returned vector of terms to be entered in the model at the next step.

Author(s)

John Lawson

Find first term to enter forward stepwise regression that preserves model hierarchy

Description

Usage

fntrmDS(y,des,prvm)
fntrmDS(y,des,prvm)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1. The m three-level factors always preceed the c two-level factors in the design.
`prvm`	input - this is a vector of text names of the terms in the model. This is created as the value resulting from running ihstep or fhstep.

Value

returned vector of terms to be entered in the model at the next step.

Author(s)

John Lawson

F-Distribution Power Calculation

Description

Calculates the power for the non-central F-distribution

Usage

Fpower(alpha, nu1, nu2, nc)
Fpower(alpha, nu1, nu2, nc)

Arguments

`alpha`	input - critical value alpha
`nu1`	input - degrees of freedom for numerator
`nu2`	input - degrees of freedom for denominator
`nc`	input - noncentrality parameter

Value

probability of exceeding fcrit(alpha, nu1,nu2) with the non-central F-distribution with nu1 and nu2 degrees of freedom and noncentrality parameter nc

Author(s)

John Lawson

F-Distribution Power Calculation

Description

Calculates the power for one-way ANOVA

Usage

Fpower1(alpha,nlev,nreps,Delta,sigma)
Fpower1(alpha,nlev,nreps,Delta,sigma)

Arguments

`alpha`	input - significance level of the F-test.
`nlev`	input - the number of levels of the factor
`nreps`	input - the number of replicates in each level of the factor.
`Delta`	input - the size of a practical difference in two cell means.
`sigma`	input - the standard deviation of the experimental error.

Value

probability of exceeding fcrit(alpha, nu1,nu2) with the non-central F-distribution with nu1 and nu2 degrees of freedom and noncentrality parameter nc

Author(s)

John Lawson

F-Distribution Power Calculation

Description

Calculates the power for a two-way ANOVA

Usage

Fpower2(alpha,nlev,nreps,Delta,sigma)
Fpower2(alpha,nlev,nreps,Delta,sigma)

Arguments

`alpha`	input - significance level of the F-test.
`nlev`	input - vector of length two containing the number of levels of the factors.
`nreps`	input - the the number of replicates in each combination of factor levels.
`Delta`	input - the size of a practical difference in two marginal factor level means.
`sigma`	input - the standard deviation of the experimental error.

Value

probability of exceeding fcrit(alpha, nu1,nu2) with the non-central F-distribution with nu1 and nu2 degrees of freedom and noncentrality parameter nc

Author(s)

John Lawson

This function makes a full normal plot of the elements of the vector called effects

Description

This function makes a full normal plot of the elements of the vector called effects

Usage

fullnormal(effects, labs, alpha = 0.05, refline = "TRUE")
fullnormal(effects, labs, alpha = 0.05, refline = "TRUE")

Arguments

`effects`	input - vector of effects to be plotted
`labs`	input - vector of labels of the effects to be plotted
`alpha`	input - alpha level for labeling of significant effects using Lenth statistic
`refline`	input - logical variable that indicates whether a reference line is added to the plot (default is "TRUE")

Author(s)

John Lawson

Gauge R&R Study

Description

Data from the Gauge R&R Study in chapter 5 of Design and Analysis of Experiments with R

Usage

data(gagerr)data(gagerr)

Format

A data frame with 60 observations on the following 3 variables.

part: a factor with levels 1 2 3 4 5 6 7 8 9 10
oper: a factor with levels 1 2 3
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(gagerr)
data(gagerr)

This function computes the gap statistic which is used to test for an outlier using Daniels method

Description

This function computes the gap statistic which is used to test for an outlier using Daniels method

Usage

gapstat(beta, pse)
gapstat(beta, pse)

Arguments

`beta`	input - vector of coefficients from saturated model fit to the data
`pse`	input - Lenth's PSE statistic calculated from the elements of beta

Value

returned gap statistic

Author(s)

John Lawson

This function uses Daniel's Method to find an outlier in an unreplicated $2^{(k-p)}$ design.

Description

This function uses Daniel's Method to find an outlier in an unreplicated $2^{(k-p)}$ design.

Usage

Gaptest(DesY)
Gaptest(DesY)

Arguments

DesY

input this is a data frame containing an unreplicated $2^{(k-p)}$ design. The last variable in the data frame should be the numeric response.

Author(s)

John Lawson

References

Box, G.E.P. (1991) "George's column: Finding bad values in factorial designs", Quality Engineering, 3, 249-254.

Unreplicated split-plot fractional-factorial experiment on geometric distortion of drive gears

Description

Data from the unreplicated split-plot fractional-factorial experiment on geometric distortion of drive gears in chapter 8 of Design and Analysis of Experiments with R

Usage

data(gear)data(gear)

Format

A data frame with 16 observations on the following 6 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
P: a factor with levels -1 1
Q: a factor with levels -1 1
y: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(gear)
data(gear)

This function makes a half normal plot of the elements of the vector called effects

Description

This function makes a half normal plot of the elements of the vector called effects

Usage

halfnorm(effects, labs, alpha = 0.05, refline = "TRUE")
halfnorm(effects, labs, alpha = 0.05, refline = "TRUE")

Arguments

`effects`	input - vector of effects to be plotted
`labs`	input - vector of labels of the effects to be plotted
`alpha`	input - alpha level for labeling of significant effects using Lenth statistic
`refline`	input - logical variable that indicates whether a reference line is added to the plot (default is "TRUE")

Author(s)

John Lawson

low grade hardwood conjoint study

Description

Data from the low grade hardwood conjoint study in chapter 6 of Design and Analysis of Experiments with R

Usage

data(hardwood)data(hardwood)

Format

A data frame with 12 observations on the following 5 variables.

Design: a factor with levels "RC" "AC" "OCI" "OCII"
Price: a numeric variable
Density: a factor with levels "Clear" "Heavy" "Medium"
Guarantee: a factor with levels "1y" "Un"
Rating: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(hardwood)
data(hardwood)

RSM forward regression keeping model hierarchy

Description

This function performs a hierarchical forward stepwise regression. If an interaction or quadratic term is entered in the model, the parent main effects are also entered into the model.

Usage

HierAFS(y,x,m,c,step) 
HierAFS(y,x,m,c,step)

Arguments

`y`	input - this is a vector containing a single numeric column of response data.
`x`	input - this is a data frame containing the numeric columns of the candidate independent variables. The m three-level factors always preceed the c two-level factors in the design. The factor names or colnames(x) should always be of length (for example letters of the alphabet "A", "B", etc.)
`m`	input - this is an integer equal to the number of three-level factors in the design
`c`	input - this is an integer equal to the number of two-level factors in the design. Note m+c must be equal to the number of columns of des.
`step`	input - this is a single numeric value containing the n umber of steps requested.

Value

returned data frame the first column is a factor variable containing the formula for the model fit at each step, the second numeric column is the R-square statistic for the model fit with each formula.

Author(s)

Gerhard Krennrich, and modified by John Lawson

First step in a forward stepwise regression that preserves model hierarchy

Description

This function performs the first step of a hierarchical forward stepwise regression. If an interaction or quadratic term is entered first, the parent main effects are also entered into the model. This function is called by HierAFS.R

Usage

ihstep(y,des,m,c)
ihstep(y,des,m,c)

Arguments

`y`	input - this is a data frame containing a single numeric column of response data.
`des`	input - this is a data frame containing the numeric columns of the candidate independent variables. The column names of des are of length 1 i.e., letters of the alphabet. The m three-level factors always preceed the c two-level factors in the design.
`m`	input - this is an integer equal to the number of three level factors in the design
`c`	input - this is an integer equal to the number of two level factors in the design. Note m+c must be equal to the number of columns of des.

Value

returned vector of terms entered in the model at this step.

Author(s)

John Lawson

Single array for injection molding experiment

Description

Data from the single array for injection molding experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(inject)data(inject)

Format

A data frame with 20 observations on the following 8 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
shrinkage: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(inject)
data(inject)

Interleave vectors

Description

interleaves two vectors

Usage

interleave(v1,v2)
interleave(v1,v2)

Arguments

`v1`	input - first vector
`v2`	input - second vector

Value

vector

Lenth's Plot of Effects

Description

Plot of the factor effects with significance levels based on robust estimation of contrast standard errors.

Usage

LenthPlot(obj, alpha = 0.05, plt = TRUE, limits = TRUE,
    xlab = "factors", ylab = "effects", faclab = NULL, cex.fac = graphics::par("cex.lab"),
    cex.axis=graphics::par("cex.axis"), adj = 1, ...)
LenthPlot(obj, alpha = 0.05, plt = TRUE, limits = TRUE,
    xlab = "factors", ylab = "effects", faclab = NULL, cex.fac = graphics::par("cex.lab"),
    cex.axis=graphics::par("cex.axis"), adj = 1, ...)

Arguments

`obj`	object of class `lm` or vector with the factor effects.
`alpha`	numeric. Significance level used for the margin of error (ME) and simultaneous margin of error (SME). See Lenth(1989).
`plt`	logical. If `TRUE`, a spikes plot with the factor effects is displayed. Otherwise, no plot is produced.
`limits`	logical. If `TRUE` ME and SME limits are displayed and labeled.
`xlab`	character string. Used to label the x-axis. "factors" as default.
`ylab`	character string. Used to label the y-axis. "effects" as default.
`faclab`	list with components `idx` (numeric vector) and `lab` (character vector). The `idx` entries of effects vector (taken from `obj`) are labelled as `lab`. The rest of the effect names are blanked. If `NULL` all factors are labelled using the coefficients' name.
`cex.fac`	numeric. Character size used for the factor labels.
`cex.axis`	numeric. Character size used for the axis.
`adj`	numeric between 0 and 1. Determines where to place the "ME" (margin of error) and the "SME" (simultaneous margin of error) labels (character size of 0.9*`cex.axis`). 0 for extreme left hand side, 1 for extreme right hand side.
`...`	extra parameters passed to `plot`.

Details

If obj is of class lm, 2*coef(obj) is used as factor effect with the intercept term removed. Otherwise, obj should be a vector with the factor effects. Robust estimate of the contrasts standard error is used to calculate marginal (ME) and simultaneous margin of error (SME) for the provided significance (1 - alpha) level. See Lenth(1989). Spikes are used to display the factor effects. If faclab is NULL, factors are labelled with the effects or coefficient names. Otherwise, those faclab\$idx factors are labelled as faclab\$lab. The rest of the factors are blanked.

Value

The function is called mainly for its side effect. It returns a vector with the value of alpha used, the estimated PSE, ME and SME.

Author(s)

Ernesto Barrios. Extension provided by Kjetil Kjernsmo (2013).

References

Lenth, R. V. (1989). "Quick and Easy Analysis of Unreplicated Factorials". Technometrics Vol. 31, No. 4. pp. 469–473.

This function uses the LGB Method to detect significant effects in unreplicated fractional factorials.

Description

This function uses the LGB Method to detect significant effects in unreplicated fractional factorials.

Usage

LGB(Beta, alpha = 0.05, rpt = TRUE, plt = TRUE, pltl = TRUE)
LGB(Beta, alpha = 0.05, rpt = TRUE, plt = TRUE, pltl = TRUE)

Arguments

`Beta`	input - this is the numeric vector of effects or coefficients to be tested
`alpha`	input - This is the significance level of the test
`rpt`	input - this is a logical variable that controls whether the report is written (default is TRUE)
`plt`	input - this is a logical variable that controls whether a half-normal plot is made (default is TRUE)
`pltl`	input - this is a logical variable that controls whether the significance limit line is drawn on the half-normal plot (default is TRUE)

Author(s)

John Lawson

References

Lawson, J., Grimshaw, S., Burt, J. (1998) "A quantitative method for identifying active contrasts in unreplicated factorial experiments based on the half-normal plot", Computational Statistics and Data Analysis, 26, 425-436.

This function does the calculations for the LGB Method to detect significant effects in unreplicated fractional factorials.

Description

This function uses the LGB Method to detect significant effects in unreplicated fractional factorials.

Usage

LGBc(Beta, alpha = 0.05, rpt = TRUE, plt = TRUE, pltl = TRUE)
LGBc(Beta, alpha = 0.05, rpt = TRUE, plt = TRUE, pltl = TRUE)

Arguments

`Beta`	input - this is the numeric vector of effects or coefficients to be tested
`alpha`	input - This is the significance level of the test
`rpt`	input - this is a logical variable that controls whether the report is written (default is TRUE)
`plt`	input - this is a logical variable that controls whether a half-normal plot is made (default is TRUE)
`pltl`	input - this is a logical variable that controls whether the significance limit line is drawn on the half-normal plot (default is TRUE)

Author(s)

John Lawson

References

Mod function

Description

Gets mod of a to base b

Usage

mod(a,b)
mod(a,b)

Arguments

`a`	input- an integer
`b`	input - an integer

Value

remainder of a/b or mod(a,b)

Author(s)

John Lawson

Model Robust Factorial Designs

Description

Recalls Li and Nachtsheim's model robust factorial designs from a catalog of data frames

Usage

ModelRobust(des, randomize=FALSE)
ModelRobust(des, randomize=FALSE)

Arguments

`des`	input- a character variable containing the name of a design in the catalog. If left blank, the function prints a table showing all the design names in the catalog
`randomize`	input- a logical

Value

design

Author(s)

John Lawson

References

Li, W. and Nachtsheim, C. J. (2000) "Model Robust factorial Designs", Technometrics, Vol 42, No. 4, pp345-352, 2000.

mixture process variable experiment with mayonnaise

Description

Data from the mixture process variable experiment with mayonnaise in chapter 11 of Design and Analysis of Experiments with R

Usage

data(MPV)data(MPV)

Format

A data frame with 35 observations on the following 4 variables.

x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
z1: a numeric vector
z2: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(MPV)
data(MPV)

Yields of naphthalene black

Description

Data from the Yields of naphthalene black of Chapter 5 in Design and Analysis of Experiments with R

Usage

data(Naph)data(Naph)

Format

A data frame with 30 observations on the following 2 variables.

sample: a factor with levels 1 2 3 4 5 6
yield: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Naph)
data(Naph)

Optimum Plackett-Burman Designs

Description

Selects the columns from a Plackett-Burman Design Produced by FrF2 that will minimize model dependence for main effects and two factor interactions and returns the design in a data frame

Usage

OptPB(nruns, nfactors, randomize=FALSE)
OptPB(nruns, nfactors, randomize=FALSE)

Arguments

`nruns`	input- an integer representing the number of runs in the design
`nfactors`	input - in integer representing the number of factors in the design
`randomize`	input - logical

Value

design

Author(s)

John Lawson

References

Fairchild, K. (2011) "Screening Designs that Minimize Model Dependence", MS Project Department of Statistics Brigham Young University, Dec. 2011.

Blocked response surface design for pastry dough experiment

Description

Data from the Blocked response surface design for pastry dough experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(pastry)data(pastry)

Format

A data frame with 28 observations on the following 5 variables.

Block: a factor with levels 1 2 3 4 5 6 7
x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(pastry)
data(pastry)

Plackett-Burman Designs

Description

Creates a 12, 20, or 24 run Plackett-Burman design in a data frame with numeric factor levels by cyclically rotating the factor leves in the first row

Usage

PBDes(nruns, nfactors, randomize=FALSE)
PBDes(nruns, nfactors, randomize=FALSE)

Arguments

`nruns`	input- an integer representing the number of runs in the design
`nfactors`	input - in integer representing the number of factors in the design
`randomize`	input - logical

Value

design

Author(s)

John Lawson

References

Lawson, J. (2015) "Design and Analysis of Experiments with R page 229",CRC Press, Boca Raton, 2015.

Pesticide formulation experiment

Description

Data from the Pesticide formulation experiment in chapter 11 of Design and Analysis of Experiments with R

Usage

data(pest)data(pest)

Format

A data frame with 13 observations on the following 4 variables.

x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(pest)
data(pest)

pesticide application experiment

Description

Data from the pesticide application experiment in chapter 5 of Design and Analysis of Experiments with R

Usage

data(pesticide)data(pesticide)

Format

A data frame with 16 observations on the following 4 variables.

form: a factor with levels A B
tech: a factor with levels 1 2
plot: a factor with levels 1 2
residue: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(pesticide)
data(pesticide)

Unreplicated split-plot $2^5$ experiment on plasma treatment of paper

Description

Data from the unreplicated split-plot $2^5$ experiment on plasma treatment of paper in chapter 8 of Design and Analysis of Experiments with R

Usage

data(plasma)data(plasma)

Format

A data frame with 32 observations on the following 6 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
E: a factor with levels -1 1
y: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(plasma)
data(plasma)

Polvoron mixture experiment

Description

Data from the Polvoron mixture experiment in chapter 11 of Design and Analysis of Experiments with R

Usage

data(polvdat)data(polvdat)

Format

A data frame with 12 observations on the following 4 variables.

x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(polvdat)
data(polvdat)

polymerization strength variability study

Description

Data from the polymerization strength variability study in chapter 5 of Design and Analysis of Experiments with R

Usage

data(polymer)data(polymer)

Format

A data frame with 120 observations on the following 5 variables.

lot: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
box: a factor with levels 1 2
prep: a factor with levels 1 2
test: a factor with levels 1 2
strength: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(polymer)
data(polymer)

Complete control factor array and noise factor array for connector experiment

Description

Data from the complete control factor array and noise factor array for connector experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(prodstd)data(prodstd)

Format

A data frame with 16 observations on the following 16 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
Pof: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(prodstd)
data(prodstd)

Library of substituted hydroxyphenylurea compounds

Description

Data from the Library of substituted hydroxyphenylurea compounds in chapter 10 of Design and Analysis of Experiments with R (compact format)

Usage

data(qsar)data(qsar)

Format

A data frame with 36 observations on the following 4 variables.

Compound: a numeric vector
HE: a numeric vector
DMz: a numeric vector
S0K: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(qsar)
data(qsar)

Cattle rations design experiment data from Table 10.16

Description

Data from the cattle rations design experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(Rations)data(Rations)

Format

A data frame with 45 observations on the following 4 variables.

Block: a factor with levels 1 2 3 4 5 6 7 8
x1: a numeric vector
x2: a numeric vector
ADG: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Rations)
data(Rations)

generalized RCB golf driving experiment

Description

Data from the generalized RCB golf driving experiment in chapter 4 of Design and Analysis of Experiments with R

Usage

data(rcb)data(rcb)

Format

A data frame with 135 observations on the following 3 variables.

id: a factor with levels 1 2 3 4 5 6 7 8 9
teehgt: a factor with levels 1 2 3
cdistance: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(rcb)
data(rcb)

Herbicide degradation experiment

Description

Data from the Herbicide degradation experiment in chapter 9 of Design and Analysis of Experiments with R

Usage

data(residue)data(residue)

Format

A data frame with 16 observations on the following 3 variables.

soil: a factor with levels "C" "P"
moisture: a factor with levels "L" "H"
temp: a factor with levels 10 30
X1: a numeric vector
X2: a numeric vector
X3: a numeric vector
X4: a numeric vector
X5: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(residue)
data(residue)

Rubber Elasticity data

Description

Data from the Rubber Elasticity Study in chapter 5 of Design and Analysis of Experiments with R

Usage

data(rubber)data(rubber)

Format

A data frame with 96 observations on the following 4 variables.

supplier: a factor with levels A B C D
batch: a factor with levels I II III IV
sample: a factor with levels 1 2
elasticity: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(rubber)
data(rubber)

Split-plot experiment on sausage casing with RCB in whole plot

Description

Data from the Split-plot experiment on sausage casing with RCB in whole plot in chapter 7 of Design and Analysis of Experiments with R

Usage

data(sausage)data(sausage)

Format

A data frame with 32 observations on the following 5 variables.

Block: a factor with levels 1 2
Gbatch: a factor with levels 1 2 3 4
A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
ys: a numeric vector containing the response

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(sausage)
data(sausage)

Single array for starting motor experiment

Description

Data from the single array for starting motor experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(Smotor)data(Smotor)

Format

A data frame with 18 observations on the following 6 variables.

A: a factor with levels 1 2
B: a factor with levels 1 2 3
C: a factor with levels 1 2 3
D: a factor with levels 1 2 3
E: a factor with levels 1 2
torque: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Smotor)
data(Smotor)

dry mix soup experiment

Description

Data from the dry mix soup experiment in chapter 6 of Design and Analysis of Experiments with R

Usage

data(soup)data(soup)

Format

A data frame with 16 observations on the following 6 variables.

A: a factor with levels -1 1
B: a factor with levels -1 1
C: a factor with levels -1 1
D: a factor with levels -1 1
E: a factor with levels -1 1
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(soup)
data(soup)

dry soup mix variance component study

Description

Data from the dry soup mix variance component study of Chapter 5 in Design and Analysis of Experiments with R

Usage

data(soupmx)data(soupmx)

Format

A data frame with 12 observations on the following 2 variables.

batch: a factor with levels 1 2 3 4
weight: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(soupmx)
data(soupmx)

Split-plot cookie baking experiment

Description

Data from the Split-plot cookie baking experiment in chapter 8 of Design and Analysis of Experiments with R

Usage

data(splitPdes)data(splitPdes)

Format

A data frame with 24 observations on the following 5 variables.

short: a factor with levels 100 80
trayT: a factor with levels RoomT Hot
bakeT: a factor with levels low mid high
batch: a factor with levels 1 2
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(splitPdes)
data(splitPdes)

Split-plot mixture process variable experiment with vinyl

Description

Data from the Split-plot mixture process variable experiment with vinyl in chapter 10 of Design and Analysis of Experiments with R

Usage

data(SPMPV)data(SPMPV)

Format

A data frame with 28 observations on the following 7 variables.

wp: a factor with levels 1 2 3 4 5 6 7
z1: a numeric vector
z2: a numeric vector
x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(SPMPV)
data(SPMPV)

Standard Order

Description

Makes standard order

Usage

stdord(m)
stdord(m)

Arguments

`m`	input - vector length

Value

vector in standard order

Repeated measures study with dairy cow diets

Description

Data from the Repeated measures study with dairy cow diets in chapter 9 of Design and Analysis of Experiments with R (strung out format)

Usage

data(strung)data(strung)

Format

A data frame with 120 observations on the following 5 variables.

Diet: a factor with levels "Barley" "Mixed" "Lupins"
Cow: a factor with levels 1 2 3 4 5 6 7 8 9 10
week: a factor with levels 1 2 3 4
protein: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(strung)
data(strung)

Strung out control factor array and raw response data for Ina tile experiment

Description

Data from the strung out control factor array and raw response data for Ina tile experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(strungtile)data(strungtile)

Format

A data frame with 16 observations on the following 16 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
H: a numeric vector
AH: a numeric vector
BH: a numeric vector
CH: a numeric vector
DH: a numeric vector
EH: a numeric vector
FH: a numeric vector
GH: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(strungtile)
data(strungtile)

Sugarbeet data from Chapter 2

Description

Sugarbeet data from chapter 2 of Design and Analysis of Experiments with R

Usage

data(sugarbeet)data(sugarbeet)

Format

A data frame with 18 observations on the following 2 variables.

treat: a factor with levels A B C D
yield: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(sugarbeet)
data(sugarbeet)

taste test panel experiment

Description

Data from the taste test panel experiment in Chapter 7 of Design and Analysis of Experiments with R

Usage

data(taste)data(taste)

Format

A data frame with 24 observations on the following 3 variables.

panelist: a factor with levels 1 2 3 4 5 6 7 8 9 10 11 12
recipe: a factor with levels "A" "B" "C" "D"
score: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(taste)
data(taste)

Teaching experiment data from Chapter 2

Description

Data from the teaching experiment in chapter 2 of Design and Analysis of Experiments with R

Usage

data(teach)data(teach)

Format

A data frame with 30 observations on the following 4 variables.

class: a numeric vector
method: a factor with levels 1 2 3
score: a factor with levels 1 2 3 4 5
count: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(teach)
data(teach)

Tetracycline concentration in plasma

Description

Data from the Tetracycline concentration in plasma study in chapter 10 of Design and Analysis of Experiments with R (compact format)

Usage

data(Tet)data(Tet)

Format

A data frame with 9 observations on the following 2 variables.

Time: a numeric vector
Conc: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Tet)
data(Tet)

Control factor array and summary statistics for Ina tile experiment

Description

Data from the control factor array and summary statistics for Ina tile experiment in chapter 12 of Design and Analysis of Experiments with R

Usage

data(tile)data(tile)

Format

A data frame with 8 observations on the following 11 variables.

A: a numeric vector
B: a numeric vector
C: a numeric vector
D: a numeric vector
E: a numeric vector
F: a numeric vector
G: a numeric vector
y1: a numeric vector
y2: a numeric vector
ybar: a numeric vector
lns2: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(tile)
data(tile)

Box-Behnken design for trebuchet experiment

Description

Data from the Box-Behnken design for trebuchet experiment in chapter 10 of Design and Analysis of Experiments with R

Usage

data(Treb)data(Treb)

Format

A data frame with 15 observations on the following 4 variables.

x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(Treb)
data(Treb)

This function performs Tukey's single degree of freedom test for interaction in an unreplicated two-factor design

Description

This function performs Tukey's single degree of freedom test for interaction in an unreplicated two-factor design

Usage

Tukey1df(data)
Tukey1df(data)

Arguments

data

input - this is a data frame with three variables, the first variable is a numeric response and next two variables are factors. There should be $ab$ lines in the data frame where $a$ is the number of levels of the first factor, and $b$ is the number of levels of the second factor.

Author(s)

John Lawson

confidence limits for method of moments estimators of variance components

Description

function for getting confidence intervals on variance components estimated by the method of moments

Usage

vci(confl,c1,ms1,nu1,c2,ms2,nu2)
vci(confl,c1,ms1,nu1,c2,ms2,nu2)

Arguments

`confl`	input- confidence level
`c1`	input - linear combination coefficient of ms1 in the estimated variance component
`ms1`	input - Anova mean square 1
`nu1`	input - Anova degrees of freedom for mean square 1
`c2`	input - linear combination coefficient of ms2 in the estimated variance component
`ms2`	input - Anova mean square 2
`nu2`	input - Anova degrees of freedom for mean square 2

Value

returned delta, Lower and Upper limits

Author(s)

John Lawson

Vinysl plasticizer formulations experiment data

Description

Data from vinyl plasticiser formulation experiment in chapter 11 of Design and Analysis of Experiments with R

Usage

data(vinyl)data(vinyl)

Format

A data frame with 40 observations on the following 7 variables.

WP: a numeric vector
x1: a numeric vector
x2: a numeric vector
x3: a numeric vector
z1: a numeric vector
z2: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

  data(vinyl)
data(vinyl)

Assay of Viral Contamination experiment data from Chapter 3

Description

Data from the Assay of Viral Contamination experiment in chapter 3 of Design and Analysis of Experiments with R

Usage

data(virus)data(virus)

Format

A data frame with 18 observations on the following 3 variables.

y: a numeric vector
Sample: a factor with levels 1 2 3 4 5 6
Dilution: a factor with levels 3 4 5

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(virus)
data(virus)

Volt meter experiment data from Chapter 3

Description

Data from the Volt meter experiment in chapter 3 of Design and Analysis of Experiments with R

Usage

data(volt)data(volt)

Format

A data frame with 16 observations on the following 3 variables.

y: a numeric vector
A: a factor containing the levels (22, 32) of factor A
B: a factor containing the levels (0.5, 5.0) of factor B
C: a factor containing the levels (0.5, 5.0) of factor C

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(volt)
data(volt)

Web page design experiment data from Chapter 3

Description

Data from the web page design experiment in chapter 3 of Design and Analysis of Experiments with R

Usage

data(web)data(web)

Format

A data frame with 36 observations on the following 6 variables.

A: a factor with levels 1 2
B: a factor with levels 1 2
C: a factor with levels 1 2
D: a factor with levels 1 2
visitors: a numeric vector
signup: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

data(web)
data(web)

Table 12.24 Experiment with Weld Tensile Strength

Description

Data from the Single Array Experiment in Exercise 5 of Chapter 12 in Design and Analysis of Experiments with R. The factors are in coded levels.

Usage

data(WeldS)data(WeldS)

Format

A data frame with 16 observations on the following 16 variables.

D: a numeric vector
H: a numeric vector
G: a numeric vector
A: a numeric vector
F: a numeric vector
GH: a numeric vector
C: a numeric vector
B: a numeric vector
J: a numeric vector
E: a numeric vector
AC: a numeric vector
AH: a numeric vector
AG: a numeric vector
e1: a numeric vector
e2: a numeric vector
y: a numeric vector

Source

Design and Analysis of Experiments with R, by John Lawson, CRC/Chapman Hall

Examples

  data(WeldS)
data(WeldS)

Package 'daewr'

Help Index

Data frames and functions for Design and Analysis of Experiments with R

Description

Author(s)

References

Alternate 16 run screening designs

Description

Usage

Arguments

Value

Author(s)

References

Two-period crossover study of antifungal agent

Description

Usage

Format

Source

Examples

apolipoprotein survey varaince component study

Description

Usage

Format

Source

Examples

Confounded apple slice browning experiment

Description

Usage

Format

Source

Examples

2(7−3)2^{(7-3)}2(7−3) arsenic removal experiment

Description

Usage

Format

Source

Examples

2(7−3)2^{(7-3)}2(7−3) arsenic removal experiment augmented with mirror image

Description

Usage

Format

Source

Examples

Confounded Block Dishwashing Experiment

Description

Usage

Format

Source

Examples

Confounded block fractional mouse growth experiment

Description

Usage

Format

Source

Examples

mouse liver enzyme experiment

Description

Usage

Format

Source

Examples

Balanced incomplete blocksize

Description

Usage

Arguments

Value

Author(s)

Extra-period crossover bioequivalence study

Description

Usage

Format

Source

Examples

Latin Square bioequivalence experiment

Description

Usage

Format

Source

Examples

Variance component study of calcium in blood serum

$2^{(7-3)}$ arsenic removal experiment

$2^{(7-3)}$ arsenic removal experiment augmented with mirror image

Box and Meyer's unreplicated $2^4$ from Chapter 3