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snPlot: Signal-to-Noise-Ratio Plots

Source: R/5.3_Taguchi_designs_Functions.R
snPlot.Rd

Creates a Signal-to-Noise Ratio plot for designs of type taguchiDesign.c with at least two replicates.

Usage

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)

Arguments

object

An object of class taguchiDesign.c.

type

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`.

factors

The factors for which the effect plot is to be created.

fun

A function for constructing the effect plot such as mean, median, etc. Default is mean.

response

A character string specifying the response variable. If object contains multiple responses, this parameter selects one column to plot. Default is NULL.

points

A logical value. If TRUE, points are shown in addition to values derived from fun. Default is FALSE.

classic

A logical value. If TRUE, creates an effect plot as depicted in most textbooks. Default is FALSE.

lty

A numeric value specifying the line type to be used.

xlab

A title for the x-axis.

ylab

A title for the y-axis.

main

An overall title for the plot.

ylim

A numeric vector of length 2 specifying the limits of the y-axis.

l.col

A color for the lines.

p.col

A color for the points.

ld.col

A color for the dashed line.

pch

The symbol for plotting points.

Value

An invisible data.frame containing all the single Signal-to-Noise Ratios.

Details

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.

Examples

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)