gageRR.c {r6qualitytools} | R Documentation |
gageRR-class: Class 'gageRR'
Description
R6 Class for Gage R&R (Repeatability and Reproducibility) Analysis
Public fields
X
Data frame containing the measurement data.
ANOVA
List containing the results of the Analysis of Variance (ANOVA) for the gage study.
RedANOVA
List containing the results of the reduced ANOVA.
method
Character string specifying the method used for the analysis (e.g.,
`crossed`
,`nested`
).Estimates
List of estimates including variance components, repeatability, and reproducibility.
Varcomp
List of variance components.
Sigma
Numeric value representing the standard deviation of the measurement system.
GageName
Character string representing the name of the gage.
GageTolerance
Numeric value indicating the tolerance of the gage.
DateOfStudy
Character string representing the date of the gage R&R study.
PersonResponsible
Character string indicating the person responsible for the study.
Comments
Character string for additional comments or notes about the study.
b
Factor levels for operator.
a
Factor levels for part.
y
Numeric vector or matrix containing the measurement responses.
facNames
Character vector specifying the names of the factors (e.g.,
`Operator`
,`Part`
).numO
Integer representing the number of operators.
numP
Integer representing the number of parts.
numM
Integer representing the number of measurements per part-operator combination.
Methods
Public methods
Method new()
Initialize the fiels of the gageRR
object
Usage
gageRR.c$new( X, ANOVA = NULL, RedANOVA = NULL, method = NULL, Estimates = NULL, Varcomp = NULL, Sigma = NULL, GageName = NULL, GageTolerance = NULL, DateOfStudy = NULL, PersonResponsible = NULL, Comments = NULL, b = NULL, a = NULL, y = NULL, facNames = NULL, numO = NULL, numP = NULL, numM = NULL )
Arguments
X
Data frame containing the measurement data.
ANOVA
List containing the results of the Analysis of Variance (ANOVA) for the gage study.
RedANOVA
List containing the results of the reduced ANOVA.
method
Character string specifying the method used for the analysis (e.g., "crossed", "nested").
Estimates
List of estimates including variance components, repeatability, and reproducibility.
Varcomp
List of variance components.
Sigma
Numeric value representing the standard deviation of the measurement system.
GageName
Character string representing the name of the gage.
GageTolerance
Numeric value indicating the tolerance of the gage.
DateOfStudy
Character string representing the date of the gage R&R study.
PersonResponsible
Character string indicating the person responsible for the study.
Comments
Character string for additional comments or notes about the study.
b
Factor levels for operator.
a
Factor levels for part.
y
Numeric vector or matrix containing the measurement responses.
facNames
Character vector specifying the names of the factors (e.g., "Operator", "Part").
numO
Integer representing the number of operators.
numP
Integer representing the number of parts.
numM
Integer representing the number of measurements per part-operator combination.
Method print()
Return the data frame containing the measurement data (X
)
Usage
gageRR.c$print()
Method subset()
Return a subset of the data frame that containing the measurement data (X
)
Usage
gageRR.c$subset(i, j)
Arguments
i
The i-position of the row of
X
.j
The j-position of the column of
X
.
Method summary()
Summarize the information of the fields of the gageRR
object.
Usage
gageRR.c$summary()
Method get.response()
Get or get the response for a gageRRDesign
object.
Usage
gageRR.c$get.response()
Method response()
Set the response for a gageRRDesign
object.
Usage
gageRR.c$response(value)
Arguments
value
New response vector.
Method names()
Methods for function names
in Package base
.
Usage
gageRR.c$names()
Method as.data.frame()
Methods for function as.data.frame
in Package base
.
Usage
gageRR.c$as.data.frame()
Method get.tolerance()
Get the tolerance
for an object of class gageRR
.
Usage
gageRR.c$get.tolerance()
Method set.tolerance()
Set the tolerance
for an object of class gageRR
.
Usage
gageRR.c$set.tolerance(value)
Arguments
value
A data.frame or vector for the new value of tolerance.
Method get.sigma()
Get the sigma
for an object of class gageRR
.
Usage
gageRR.c$get.sigma()
Method set.sigma()
Set the sigma
for an object of class gageRR
.
Usage
gageRR.c$set.sigma(value)
Arguments
value
Valor of
sigma
Method plot()
This function creates a customized plot using the data from the gageRR.c
object.
Usage
gageRR.c$plot(main = NULL, xlab = NULL, ylab = NULL, col, lwd, fun = mean)
Arguments
main
Character string specifying the title of the plot.
xlab
A character string for the x-axis label.
ylab
A character string for the y-axis label.
col
A character string or vector specifying the color(s) to be used for the plot elements.
lwd
A numeric value specifying the line width of plot elements
fun
Function to use for the calculation of the interactions (e.g.,
mean
,median
). Default ismean
.
Examples
# Create gageRR-object gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE) # Vector of responses y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80, -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26, 1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94, 1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01, -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58, -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06, -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16) # Appropriate responses gdo$response(y) # Perform and gageRR gdo <- gageRR(gdo) gdo$plot()
Method errorPlot()
The data from an object of class gageRR
can be analyzed by running 'Error Charts' of the individual deviations from the accepted rference values. These 'Error Charts' are provided by the function errorPlot
.
Usage
gageRR.c$errorPlot(main, xlab, ylab, col, pch, ylim, legend = TRUE)
Arguments
main
a main title for the plot.
xlab
A character string for the x-axis label.
ylab
A character string for the y-axis label.
col
Plotting color.
pch
An integer specifying a symbol or a single character to be used as the default in plotting points.
ylim
The y limits of the plot.
legend
A logical value specifying whether a legend is plotted automatically. By default legend is set to 'TRUE'.
Examples
# Create gageRR-object gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE) # Vector of responses y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80, -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26, 1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94, 1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01, -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58, -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06, -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16) # Appropriate responses gdo$response(y) # Perform and gageRR gdo <- gageRR(gdo) gdo$errorPlot()
Method whiskersPlot()
In a Whiskers Chart, the high and low data values and the average (median) by part-by-operator are plotted to provide insight into the consistency between operators, to indicate outliers and to discover part-operator interactions. The Whiskers Chart reminds of boxplots for every part and every operator.
Usage
gageRR.c$whiskersPlot(main, xlab, ylab, col, ylim, legend = TRUE)
Arguments
main
a main title for the plot.
xlab
A character string for the x-axis label.
ylab
A character string for the y-axis label.
col
Plotting color.
ylim
The y limits of the plot.
legend
A logical value specifying whether a legend is plotted automatically. By default legend is set to 'TRUE'.
Examples
# Create gageRR-object gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE) # Vector of responses y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80, -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26, 1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94, 1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01, -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58, -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06, -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16) # Appropriate responses gdo$response(y) # Perform and gageRR gdo <- gageRR(gdo) gdo$whiskersPlot()
Method averagePlot()
averagePlot
creates all x-y plots of averages by size out of an object of class gageRR
. Therfore the averages of the multiple readings by each operator on each part are plotted with the reference value or overall part averages as the index.
Usage
gageRR.c$averagePlot(main, xlab, ylab, col, single = FALSE)
Arguments
main
a main title for the plot.
xlab
A character string for the x-axis label.
ylab
A character string for the y-axis label.
col
Plotting color.
single
A logical value.If 'TRUE' a new graphic device will be opened for each plot. By default
single
is set to 'FALSE'.
Examples
# Create gageRR-object gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE) # Vector of responses y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80, -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26, 1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94, 1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01, -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58, -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06, -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16) # Appropriate responses gdo$response(y) # Perform and gageRR gdo <- gageRR(gdo) gdo$averagePlot()
Method compPlot()
compPlot
creates comparison x-y plots of an object of class gageRR
. The averages of the multiple readings by each operator on each part are plotted against each other with the operators as indices. This plot compares the values obtained by one operator to those of another.
Usage
gageRR.c$compPlot(main, xlab, ylab, col, cex.lab, fun = NULL)
Arguments
main
a main title for the plot.
xlab
A character string for the x-axis label.
ylab
A character string for the y-axis label.
col
Plotting color.
cex.lab
The magnification to be used for x and y labels relative to the current setting of cex.
fun
Optional function that will be applied to the multiple readings of each part. fun should be an object of class
function
likemean
,median
,sum
, etc. By default,fun
is set to 'NULL' and all readings will be plotted.
Examples
# Create gageRR-object gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE) # Vector of responses y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80, -0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26, 1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94, 1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01, -0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58, -0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06, -0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16) # Appropriate responses gdo$response(y) # Perform and gageRR gdo <- gageRR(gdo) gdo$compPlot()
Method clone()
The objects of this class are cloneable with this method.
Usage
gageRR.c$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
#create gageRR-object
gdo <- gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
#vector of responses
y <- c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
#appropriate responses
gdo$response(y)
# perform and gageRR
gdo <- gageRR(gdo)
# Using the plots
gdo$plot()
## ------------------------------------------------
## Method `gageRR.c$plot`
## ------------------------------------------------
# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)
gdo$plot()
## ------------------------------------------------
## Method `gageRR.c$errorPlot`
## ------------------------------------------------
# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)
gdo$errorPlot()
## ------------------------------------------------
## Method `gageRR.c$whiskersPlot`
## ------------------------------------------------
# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)
gdo$whiskersPlot()
## ------------------------------------------------
## Method `gageRR.c$averagePlot`
## ------------------------------------------------
# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)
gdo$averagePlot()
## ------------------------------------------------
## Method `gageRR.c$compPlot`
## ------------------------------------------------
# Create gageRR-object
gdo = gageRRDesign(Operators = 3, Parts = 10, Measurements = 3, randomize = FALSE)
# Vector of responses
y = c(0.29,0.08, 0.04,-0.56,-0.47,-1.38,1.34,1.19,0.88,0.47,0.01,0.14,-0.80,
-0.56,-1.46, 0.02,-0.20,-0.29,0.59,0.47,0.02,-0.31,-0.63,-0.46,2.26,
1.80,1.77,-1.36,-1.68,-1.49,0.41,0.25,-0.11,-0.68,-1.22,-1.13,1.17,0.94,
1.09,0.50,1.03,0.20,-0.92,-1.20,-1.07,-0.11, 0.22,-0.67,0.75,0.55,0.01,
-0.20, 0.08,-0.56,1.99,2.12,1.45,-1.25,-1.62,-1.77,0.64,0.07,-0.15,-0.58,
-0.68,-0.96,1.27,1.34,0.67,0.64,0.20,0.11,-0.84,-1.28,-1.45,-0.21,0.06,
-0.49,0.66,0.83,0.21,-0.17,-0.34,-0.49,2.01,2.19,1.87,-1.31,-1.50,-2.16)
# Appropriate responses
gdo$response(y)
# Perform and gageRR
gdo <- gageRR(gdo)
gdo$compPlot()