FrF2-package | R Documentation |

creates regular and non-regular Fractional Factorial 2-level designs. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

The package works together with packages DoE.base and DoE.wrapper.

The package is still subject to development; most key functionality is now included. Please contact me, if you have suggestions.

This package designs and analyses Fractional Factorial experiments with 2-level factors.
Regular (function `FrF2`

) and non-regular (function `pb`

) 2-level fractional factorial
designs can be generated. For regular fractional factorials, function `FrF2`

permits the specification of effects of interest, whose estimation is requested clear of aliasing
with other effects. The function can furthermore generate regular fractional factorials
as blocked or split-plot designs, and hard-to-change
factors can be specified in order to keep the number of level changes low.
Regular resolution V designs larger than those obtainable from function `FrF2`

can be created by function `FrF2Large`

(these are not guaranteed to be optimal).
Analysis facilities work for completely aliased designs only,
i.e. e.g. not for analysing Plackett-Burman designs with interactions.

Functions `fac.design`

, `fractionate`

or `oa.design`

from
Chambers and Hastie (1993) have been used as role models e.g. for
the option `factor.names`

or for outputting a data frame with attributes.
However, S compatibility has not been considered in devising this package. The original
above-mentioned functions are not available in `R`

; similar
functions have been implemented in package DoE.base
together with other general functionality for experimental designs.

In terms of analysis, package `FrF2`

works on linear models and enables convenient main effects and
interaction plots (functions `MEPlot`

and `IAPlot`

) similar to those
offered by Minitab software for all factors simultaneously, even though especially the
interactions are often aliased, i.e. the model is typically singular.
For the (less frequent) case of suspected three-factor-interactions, function
`cubePlot`

displays a cube with corners labeled with the (modeled)
means of three factors simultaneously.
Furthermore, the function `DanielPlot`

from package BsMD has been
modified to automatically label effects significant according to the
Lenth-criterion, to automatically distinguish between whole-plot and
split-plot effects for split-plot designs, and to provide more usage comfort
to the analyst.

Finally, the function `aliases`

determines the alias structure of a
Fractional Factorial 2-level design in a format more suitable for human readers
than the output from the built-in function `alias`

.

Ulrike Groemping

Maintainer: Ulrike Groemping <ulrike.groemping@bht-berlin.de>

Box G. E. P, Hunter, W. C. and Hunter, J. S. (2005)
*Statistics for Experimenters, 2nd edition*.
New York: Wiley.

Chambers, J.M. and Hastie, T.J. (1993). *Statistical Models in S*,
Chapman and Hall, London.

Chen, J., Sun, D.X. and Wu, C.F.J. (1993)
A catalogue of 2-level and 3-level orthogonal arrays.
*International Statistical Review* **61**, 131-145.

Daniel, C. (1959)
Use of Half Normal Plots in Interpreting Two Level Experiments.
*Technometrics*, **1**, 311-340.

Groemping, U. (2014). R Package FrF2 for Creating and Analyzing Fractional
Factorial 2-Level Designs. *Journal of Statistical Software*, **56**,
Issue 1, 1-56. https://www.jstatsoft.org/v56/i01/.

Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999)
*Orthogonal Arrays: Theory and Applications*, Springer, New York.

Lenth, R.V. (1989) Quick and easy analysis of unreplicated factorials.
*Technometrics*, **31**, 469-473.

Mee, R. (2009). *A Comprehensive Guide to Factorial Two-Level Experimentation*.
New York: Springer.

Montgomery, D.C. (2001). Design and Analysis of Experiments (5th ed.). Wiley, New York.

Plackett, R.L.; Burman, J.P. (1946) The design of optimum multifactorial
experiments. *Biometrika* **33**, 305-325.

Ryan, K.J. and Bulutoglu, D.A. (2010). Minimum Aberration Fractional Factorial Designs With Large N.
*Technometrics* **52**, 250-255.

Sanchez, S.M. and Sanchez, P.J. (2005). Very Large Fractional Factorial
and Central Composite Designs.
*ACM Transactions on Modeling and Computer Simulation*
**15**, 362-377.

The key design generating functions: `FrF2`

, `pb`

, `FrF2Large`

S3 class `design`

Related packages:
`DoE.base`

,
`DoE.wrapper`

,
`BsMD`

;

Graphical analysis functions: `MEPlot`

, `IAPlot`

, `cubePlot`

,
`DanielPlot`

Analysis of alias structure for linear models of `FrF2`

designs: `aliases`

```
### for examples on design generation, cf. functions pb and FrF2
### Injection Molding Experiment. Box et al. 1978.
data(BM93.e3.data, package="BsMD") #from BsMD
iMdat <- BM93.e3.data[1:16,2:10] #only original experiment
# make data more user-friendly
colnames(iMdat) <- c("MoldTemp","Moisture","HoldPress","CavityThick","BoostPress",
"CycleTime","GateSize","ScrewSpeed", "y")
# linear model with all main effects and 2-factor interactions
iM.lm <- lm(y ~ (.)^2, data = iMdat)
# determine aliases
aliases(iM.lm)
# coded version
aliases(iM.lm, code=TRUE)
# normal plot of effects, default is autolabel with alpha=0.05
DanielPlot(iM.lm)
DanielPlot(iM.lm,code=TRUE)
DanielPlot(iM.lm,code=TRUE,alpha=0.5)
# half normal plot of effects
DanielPlot(iM.lm,code=TRUE,alpha=0.5,half=TRUE)
# main effects plots
MEPlot(iM.lm, las=1)
# interaction plots
IAPlot(iM.lm, las=1)
# interaction plots with attention drawn to aliases
aus <- IAPlot(iM.lm, show.alias=TRUE)
# alias groups corresponding to interaction plots
aliases(iM.lm)$aliases[9:15]
# returned object
aus
# cube plot of three factors
# (not very useful for this model, for demonstration only)
## per default, modeled means are shown
## this does not make a difference here, since the main effect of
## ScrewSpeed is confounded with the MoldTemp:HoldPress:BoostPress
## interaction, so that the three-factor-interaction is indirectly included
## in the modeled means
cubePlot(iM.lm, "MoldTemp", "HoldPress", "BoostPress")
## modeled means without a three-factor interaction
cubePlot(lm(y ~ (MoldTemp+HoldPress+BoostPress)^2, data = iMdat),
"MoldTemp", "HoldPress", "BoostPress")
## modeled=FALSE reverts to showing the apparent three-factor interaction
cubePlot(lm(y ~ (MoldTemp+HoldPress+BoostPress)^2, data = iMdat),
"MoldTemp", "HoldPress", "BoostPress", modeled=FALSE)
## cubePlot also works on raw data
cubePlot(iMdat$y, iMdat$MoldTemp, iMdat$HoldPress, iMdat$BoostPress)
## plotting functions also work directly on designs,
## if these have been generated from functions FrF2 or pb:
plan <- FrF2(16, 7)
plan <- add.response(plan, rnorm(16))
MEPlot(plan)
IAPlot(plan)
DanielPlot(plan)
```

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