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qualityTools (version 1.55)

paretoPlot: paretoPlot

Description

Display standardized effects and interactions of a 'facDesign' object in a pareto plot.

Usage

paretoPlot(fdo, threeWay = FALSE, abs = TRUE, decreasing = TRUE, na.last = NA, alpha = 0.05, response = NULL, xlim, ylim, xlab, ylab, main, single = TRUE, ...)

Arguments

fdo
an object of class facDesign
threeWay
logical. If TRUE, threeway-interactions are displayed as well.
abs
logical. If TRUE, absolute effects and interactions are displayed.
alpha
the significance level used to calculate the critical value
response
response variable. If the response data frame of fdo consists of more then one responses, this variable can be used to choose just one column of the response data frame. response needs to be an object of class character with length of ‘1’. It needs to be the same character as the name of the response in the response data frame that should be plotted. By default response is set to ‘NULL’.
decreasing
logical. If TRUE, effects and interactions are sorted decreasing.
na.last
na.last
xlab
graphical parameter
ylab
graphical parameter
xlim
graphical parameter
ylim
graphical parameter
main
graphical parameter
single
a logical value.If ‘TRUE’ a new graphic device will be opened for each column of the respond dataframe of fdo (response(fdo)). If set to ‘FALSE’ par(mfrow) will be set internally. By default single is set to ‘TRUE’.
...
graphical parameters

Value

Details

paretoPlot displays a pareto plot of effects and interactions for an object of class facDesign (i.e. 2^k full or 2^k-p fractional factorial design). For a given significance level alpha, a critical value is calculated and added to the plot. Standardization is achieved by dividing estimates with their standard error. For unreplicated fractional factorial designs a Lenth Plot is generated.

References

Design and Analysis of experiments - Volume2 - Advanced Experimental Design - Hinkelmann/Kempthorne

See Also

factors, fracDesign, facDesign

Examples

Run this code
#factorial design with replications
#NA in response column and 2 replicates per factor combination
vp = fracDesign(k = 3, replicates = 2)  
#generate some data
y1 = 4*vp[,1] -7*vp[,2] + 2*vp[,2]*vp[,1] + 0.2*vp[,3] + rnorm(16)
y2 = 9*vp[,1] -2*vp[,2] + 5*vp[,2]*vp[,1] + 0.5*vp[,3] + rnorm(16)               
response(vp) = data.frame(y1,y2)
#show effects and interactions (nothing significant expected)
paretoPlot(vp)                         

#fractional factorial design --> Lenth Plot
vp = fracDesign(k = 4, gen = "D = ABC")
#generate some data
y = rnorm(8)                
response(vp) = y
#show effects and interactions (nothing significant expected)
paretoPlot(vp)                        


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