Creates a Signal-to-Noise Ratio plot for designs of type taguchiDesign.c with at least two replicates.
snPlot(object, type = "nominal", factors, fun = mean, response = NULL,
points = FALSE, classic = FALSE, lty, xlab, ylab,
main, ylim, l.col, p.col, ld.col, pch)An invisible data.frame containing all the single Signal-to-Noise Ratios.
An object of class taguchiDesign.c.
A character string specifying the type of the Signal-to-Noise Ratio plot. Possible values are:
`nominal`: Nominal-the-best plot to equalize observed values to a nominal value.
`smaller`: Smaller-the-better plot to minimize observed values.
`larger`: Larger-the-better plot to maximize observed values.
Default is `nominal`.
The factors for which the effect plot is to be created.
A function for constructing the effect plot such as mean, median, etc. Default is mean.
A character string specifying the response variable. If object contains multiple responses, this parameter selects one column to plot. Default is NULL.
A logical value. If TRUE, points are shown in addition to values derived from fun. Default is FALSE.
A logical value. If TRUE, creates an effect plot as depicted in most textbooks. Default is FALSE.
A numeric value specifying the line type to be used.
A title for the x-axis.
A title for the y-axis.
An overall title for the plot.
A numeric vector of length 2 specifying the limits of the y-axis.
A color for the lines.
A color for the points.
A color for the dashed line.
The symbol for plotting points.
The Signal-to-Noise Ratio (SNR) is calculated based on the type specified:
`nominal`: $$SN = 10 \cdot log(mean(y) / var(y))$$
`smaller`: $$SN = -10 \cdot log((1 / n) \cdot sum(y^2))$$
`larger`: $$SN = -10 \cdot log((1 / n) \cdot sum(1 / y^2))$$
Signal-to-Noise Ratio plots are used to estimate the effects of individual factors and to judge the variance and validity of results from an effect plot.
tdo <- taguchiDesign("L9_3", replicates = 3)
tdo$.response(rnorm(27))
snPlot(tdo, points = TRUE, l.col = 2, p.col = 2, ld.col = 2, pch = 16, lty = 3)
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