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.
ss.rr(
var,
part,
appr,
lsl = NA,
usl = NA,
sigma = 6,
tolerance = usl - lsl,
data,
main = "Six Sigma Gage R&R Study",
sub = "",
alphaLim = 0.05,
errorTerm = "interaction",
digits = 4,
method = "crossed",
print_plot = TRUE,
signifstars = FALSE
)
Analysis of Variance Table/s. Variance composition and %Study Var. Graphics.
The ANOVA table of the model
The ANOVA table of the reduced model (without interaction, only if interaction not significant)
A matrix with the contribution of each component to the total variation
A matrix with the contribution to the study variation
Number of distinct categories
Measured variable
Factor for parts
Factor for appraisers (operators, machines, ...)
Numeric value of lower specification limit used with USL to calculate Study Variation as %Tolerance
Numeric value of upper specification limit used with LSL to calculate Study Variation as %Tolerance
Numeric value for number of std deviations to use in calculating Study Variation
Numeric value for the tolerance
Data frame containing the variables
Main title for the graphic output
Subtitle for the graphic output (recommended the name of the project)
Limit to take into account interaction
Which term of the model should be used as error term (for the model with interation)
Number of decimal digits for output
Character to specify the type of analysis to perform, "crossed"
(default) or "nested"
if TRUE (default) the plots are printed. Change to FALSE to avoid printing plots.
if FALSE (default) the significance stars are ommitted. Change to TRUE to allow printing stars.
EL Cano with contributions by Kevin C Limburg
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.
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. https://link.springer.com/book/10.1007/978-1-4614-3652-2.
Montgomery, D. C. (2009). Introduction to Statistical Quality Control (Sixth Edition ed.). New York: Wiley & Sons, Inc.
ss.data.rr
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,
method = "crossed")
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