gageRR: Gage R&R - Gage Repeatability and Reproducibility
In qualityTools: Statistical Methods for Quality Science

Description Usage Arguments Value Note Author(s) See Also Examples

Performs a Gage R&R analysis for an object of class gageRR.

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gageRR(gdo, method = "crossed", sigma = 6, alpha = 0.25, DM = NULL,
       HM = NULL, tolerance = NULL, dig = 3, ...)

`gdo`	needs to be an object of class `gageRR`.
`method`	“crossed” which is the typical design for performing a Measurement Systems Analysis using Gage Repeatability and Reproducibility or “nested” which is used for destructive testing (i.e. the same part cannot be measured twice). Operators measure each a different sample of parts under the premise that the parts of each batch are alike. By default `method` is set to “crossed”.
`sigma`	numeric value giving the number of sigmas. For sigma=6 this relates to 99.73 percent representing the full spread of a normal distribution function (i.e. pnorm(3) - pnorm(-3)). Another popular setting sigma=5.15 relates to 99 percent (i.e. `pnorm(2.575) - pnorm(-2.575)`). By default `sigma` is set to ‘6’.
`alpha`	alpha value for discarding the interaction Operator:Part and fitting a non-interaction model. By default `alpha` is set to ‘0.25’.
`DM`	By default `DM` is set to ‘NULL’.
`HM`	By default `HM` is set to ‘NULL’.
`tolerance`	numeric value giving the tolerance for the measured parts. This is required to calculate the Process to Tolerance Ratio. By default `tolerance` is set to ‘NULL’.
`dig`	numeric value giving the number of significant digits for `format`. By default `dig` is set to ‘3’.
`...`	further graphical parameters(see `par`

gageRR() returns an object of class gageRR and shows typical Gage Repeatability and Reproducibility Output including Process to Tolerance Ratios and the number of distinctive categories (i.e. ndc) the measurement system is able to discriminate with the tested setting.

For an example in context which shows the usage of the function gageRR() please read the vignette for the package qualityTools at http://www.r-qualitytools.org/html/Measure.html.

Thomas Roth thomas.roth@tu-berlin.de

gageLin
cg
gageRRDesign
response
cg
http://www.r-qualitytools.org/html/Measure.html

#create a crossed Gage R&R Design
gdo = gageRRDesign(3,10, 2, randomize = FALSE)

#set the response i.e. Measurements
y = c(23,22,22,22,22,25,23,22,23,22,20,22,22,22,24,25,27,28,23,24,23,24,24,22,
      22,22,24,23,22,24,20,20,25,24,22,24,21,20,21,22,21,22,21,21,24,27,25,27,
      23,22,25,23,23,22,22,23,25,21,24,23)
response(gdo) = y

#perform a Gage R&R
gdo = gageRR(gdo, tolerance = 5)

#summary
summary(gdo)

#standard graphics for Gage R&R
plot(gdo)


##create a crossed Gage R&R Design - 
##Vardeman, VanValkenburg 1999 - Two-Way Random-Effects Analyses and Gauge
#gdo = gageRRDesign(Operators = 5, Parts = 2, Measurements = 3, randomize = FALSE)
#
##Measurements
#weight = c(3.481, 3.448, 3.485, 3.475, 3.472,
#           3.258, 3.254, 3.256, 3.249, 3.241,
#           3.477, 3.472, 3.464, 3.472, 3.470,
#           3.254, 3.247, 3.257, 3.238, 3.250,
#           3.470, 3.470, 3.477, 3.473, 3.474,
#           3.258, 3.239, 3.245, 3.240, 3.254)
#
##set the response i.e. Measurements
#response(gdo) = weight
#
##perform a Gage R&R
#gdo = gageRR(gdo)
#
##summary
#summary(gdo)
#
##standard graphics for Gage R&R
#plot(gdo)
#