ss.rr: Gage R & R (Measurement System Assessment)

Description

Performs Gage R&R analysis for the assessment of the measurement system of a process. Related to the Measure phase of the DMAIC strategy of Six Sigma.

Usage

ss.rr(var, part, appr, lsl = NA, usl = NA, sigma = 6, data,
  main = "Six Sigma Gage R&R Study", sub = "", alphaLim = 0.05,
  errorTerm = "interaction", digits = 4)

Arguments

var

Measured variable

part

Factor for parts

appr

Factor for appraisers (operators, machines, ...)

lsl

Numeric value of lower specification limit used with USL to calculate Study Variation as %Tolerance

usl

Numeric value of upper specification limit used with LSL to calculate Study Variation as %Tolerance

sigma

Numeric value for number of std deviations to use in calculating Study Variation

data

Data frame containing the variables

main

Main title for the graphic output

sub

Subtitle for the graphic output (recommended the name of the project)

alphaLim

Limit to take into account interaction

errorTerm

Which term of the model should be used as error term (for the model with interation)

digits

Number of decimal digits for output

Value

Analysis of Variance Table/s. Variance composition and %Study Var. Graphics.

anovaTable

The ANOVA table of the model

anovaRed

The ANOVA table of the reduced model (without interaction, only if interaction not significant)

varComp

A matrix with the contribution of each component to the total variation

studyVar

A matrix with the contribution to the study variation

ncat

Number of distinct categories

Details

Performs an R&R study for the measured variable, taking into account part and appraiser factors. It outputs the sources of Variability, and six graphs: bar chart with the sources of Variability, plots by appraiser, part and interaction and x-bar and R control charts.

References

Automotive Industry Action Group. (2010). Measurement Systems Analysis (Fourth Edition). AIAG.

Cano, Emilio L., Moguerza, Javier M. and Redchuk, Andres. 2012. Six Sigma with R. Statistical Engineering for Process Improvement, Use R!, vol. 36. Springer, New York. http://www.springer.com/statistics/book/978-1-4614-3651-5.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (Sixth Edition ed.). New York: Wiley & Sons, Inc.

Examples

Run this code

# NOT RUN {
ss.rr(time1, prototype, operator, data = ss.data.rr, 
	sub = "Six Sigma Paper Helicopter Project", 
	alphaLim = 0.05,
	errorTerm = "interaction",
	lsl = 0.7,
	usl = 1.8)

# }

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