snPlot(object, type="nominal", factors, fun = mean, response = NULL, single = FALSE, points = FALSE, classic = FALSE, axes = TRUE, lty, xlab, ylab, main, ylim, ...)
taguchiDesign
.
By default fun
is set to nominal
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.par(mfrow = c(2,2))
.
By default single
is set to FALSE.
fun
.
By default points
is set to FALSE.
classic
is set to FALSE.
title
.
title
.
title
.
par
).
snPlot
uses effectPlot
and creates an effect plot for the signal-to-noise ratios as target values.
Depending on the used type
the target values for the single replications of the taguchi design will be calculated in the following way:
Signal-to-Noise ratio plots are an additional tool to estimate the effects of the single factors. Beside the effect plot, which is used to identify the factor with the most effect to a process or something like that, the signal-to-noise ratio plot can be used to judge the variance an d therefore the validity of the results of an effect plot.
interactionPlot
paretoPlot
facDesign
response
normalPlot
http://www.r-qualitytools.org/html/Improve.html
tdo = taguchiDesign("L9_3",replicates=3)
response(tdo) = rnorm(27)
snPlot(tdo, points = TRUE, col = 2, pch = 16, lty = 3)
Run the code above in your browser using DataLab