gageRR: gageRR: Gage R&R - Gage Repeatability and Reproducibility

In r6qualitytools: R6-Based Statistical Methods for Quality Science

gageRR: Gage R&R - Gage Repeatability and Reproducibility

Description

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

Usage

gageRR(
  gdo,
  method = "crossed",
  sigma = 6,
  alpha = 0.25,
  tolerance = NULL,
  dig = 3,
  print = TRUE
)

Arguments

gdo	Needs to be an object of class gageRR.c.
method	Character string specifying the Gage R&R 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'.
tolerance	Mumeric 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'.
print	Print the summary of the perform of the Gage.

Value

The function gageRR returns an object of class gageRR.c 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.

See Also

gageRR.c, gageRRDesign, gageLin, cg.

Examples

# Create de gageRR Design
design <- gageRRDesign(Operators = 3, Parts = 10, Measurements = 3,
                       method = "crossed", sigma = 6, randomize = TRUE)
design$response(rnorm(nrow(design$X), mean = 10, sd = 2))

# Results of de Design
result <- gageRR(gdo = design, method = "crossed", sigma = 6, alpha = 0.25)
class(result)
result$plot()

r6qualitytools documentation built on Oct. 4, 2024, 1:09 a.m.