errorPlot: Function to create error Charts
In qualityTools: Statistical Methods for Quality Science

Description Usage Arguments Details Note Author(s) References See Also Examples

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.

1	errorPlot(x, main, xlab, ylab, col, pch, type, ylim, legend=TRUE, ...)

`x`	needs to be an object of class `gageRR`.
`main`	a main title for the plot.
`xlab`	a label for the x axis.
`ylab`	a label for the y axis.
`col`	plotting color.
`pch`	an integer specifying a symbol or a single character to be used as the default in plotting points.
`type`	graphical parameter (see `plot`).
`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’. If the argument `legend` is set to ‘FALSE’ an individual legend can be added by using the function `legend` afterwards.
`...`	arguments to be passed to methods, such as graphical parameters (see `par`).

The plotted values are can be calculated in two ways:

Error = Observed Value - Reference Value (not yet implemented)
Error = Observed Value - Average Measurement of Part

The first way is not yet implemented, because it is not yet possible to give a refrence value to the object in x.
This will be implemented later! Therefore errorPlot uses the second way above to calculate the plotted error.

Graphical parameters such as col or pch can be given as single characters or as
vectors containing characters or number for the parameters of the individual operators.

Please do read the vignette for the package qualityTools at http://www.r-qualitytools.org.

Thomas Roth: thomas.roth@tu-berlin.de
Etienne Stockhausen: stocdarf@mailbox.tu-berlin.de

The idea of the plot and the example given by example(errorPlot) are out of:

CHRYSLER Group LLC; FORD Motor Company; GENERAL MOTORS Corporation: Measurement System Analysis (MSA),
p.112, 4rd ed. Southfield: AIAG, 2010.

gageRR
par
http://www.r-qualitytools.org

#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      
response(gdo)=y                                                                
# perform and gageRR    
gdo=gageRR(gdo)                                                                    
errorPlot(gdo)

Loading required package: Rsolnp
Loading required package: MASS

Attaching package: 'qualityTools'

The following object is masked from 'package:stats':

    sigma


AnOVa Table -  crossed Design
              Df Sum Sq Mean Sq F value   Pr(>F)    
Operator       2   3.17   1.584  34.440 1.09e-10 ***
Part           9  88.36   9.818 213.517  < 2e-16 ***
Operator:Part 18   0.36   0.020   0.434    0.974    
Residuals     60   2.76   0.046                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

----------
AnOVa Table Without Interaction -  crossed Design
            Df Sum Sq Mean Sq F value   Pr(>F)    
Operator     2   3.17   1.584   39.62 1.34e-12 ***
Part         9  88.36   9.818  245.61  < 2e-16 ***
Residuals   78   3.12   0.040                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

----------

Gage R&R
                 VarComp VarCompContrib Stdev StudyVar StudyVarContrib
totalRR           0.0914         0.0776 0.302     1.81           0.279
 repeatability    0.0400         0.0339 0.200     1.20           0.184
 reproducibility  0.0515         0.0437 0.227     1.36           0.209
   Operator       0.0515         0.0437 0.227     1.36           0.209
   Operator:Part  0.0000         0.0000 0.000     0.00           0.000
Part to Part      1.0864         0.9224 1.042     6.25           0.960
totalVar          1.1779         1.0000 1.085     6.51           1.000

---
 * Contrib equals Contribution in %
 **Number of Distinct Categories (truncated signal-to-noise-ratio) = 4 

NULL