Description Usage Arguments Value Author(s) References See Also Examples
Function visualize the linearity of a gage by plotting the single and mean bias
in one plot and intercalate them with a straight line.
Furthermore the function deliver some characteristic values of linearity studies
according to MSA (Measurement System Analysis).
1 2 |
object |
an object of class |
conf.level |
an numeric value between ‘0’ and ‘1’, giving the confidence intervall
for the analysis. |
ylim |
a vector with two entries, giving the minimum and the maximum of the y-axis. |
col |
a vector with four numeric entries. The first gives the color of the single points,
the second gives the color of the points for the mean bias, the third gives the color
fo the straight interpolation line and the fourth gives the color for the lines
representing the confidence interval. If one of the values is missing or negative
the points or lines are not plotted. |
pch |
a vector with two numeric or single character entries giving the symbols for the single points (1st entry) and the mean bias (2nd entry). The default vector is “c(20,18)” |
lty |
a vector with two entries giving the line-style for the interpolating line and the
confidence interval lines. For detailed information to the entries please see
|
stats |
a logigal value. If ‘TRUE’ (default) the function returns all calculated information. |
plot |
a logigal value. If ‘TRUE’ (default) the function deliver a plot. |
The function returns an object of class MSALinearity
which can be used
with e.g. plot
or summary
.
Thomas Roth: thomas.roth@tu-berlin.de
Etienne Stockhausen: stocdarf@mailbox.tu-berlin.de
Cgrysler Group LCC & Ford Motor Company & General Motors Corporation,
Measurement System Analysis - MSA (2010), 4th ed.
Southfield,Michigan: Automotive Industry Action Group.
cg
gageRR
gageLinDesign
response
edit
http://www.r-qualitytools.org
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # Results of single runs
A=c(2.7,2.5,2.4,2.5,2.7,2.3,2.5,2.5,2.4,2.4,2.6,2.4)
B=c(5.1,3.9,4.2,5,3.8,3.9,3.9,3.9,3.9,4,4.1,3.8)
C=c(5.8,5.7,5.9,5.9,6,6.1,6,6.1,6.4,6.3,6,6.1)
D=c(7.6,7.7,7.8,7.7,7.8,7.8,7.8,7.7,7.8,7.5,7.6,7.7)
E=c(9.1,9.3,9.5,9.3,9.4,9.5,9.5,9.5,9.6,9.2,9.3,9.4)
# creates Desing
test=gageLinDesign(ref=c(2,4,6,8,10),n=12)
# creates data.frame with results
Messungen=data.frame(rbind(A,B,C,D,E))
# enter results in Desing
response(test)=Messungen
# no plot and no return
MSALin=gageLin(test,stats=FALSE,plot=FALSE)
# plot only
plot(MSALin)
# summary
summary(MSALin)
|
Loading required package: Rsolnp
Loading required package: MASS
Attaching package: 'qualityTools'
The following object is masked from 'package:stats':
sigma
----------------------
Part Ref X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12
A 1 2 2.7 2.5 2.4 2.5 2.7 2.3 2.5 2.5 2.4 2.4 2.6 2.4
B 2 4 5.1 3.9 4.2 5.0 3.8 3.9 3.9 3.9 3.9 4.0 4.1 3.8
C 3 6 5.8 5.7 5.9 5.9 6.0 6.1 6.0 6.1 6.4 6.3 6.0 6.1
D 4 8 7.6 7.7 7.8 7.7 7.8 7.8 7.8 7.7 7.8 7.5 7.6 7.7
E 5 10 9.1 9.3 9.5 9.3 9.4 9.5 9.5 9.5 9.6 9.2 9.3 9.4
----------------------
Call:
lm(formula = BIAS ~ ref)
Residuals:
Min 1Q Median 3Q Max
-0.41000 -0.12000 0.01667 0.11667 0.89000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.73667 0.07252 10.16 1.73e-14 ***
ref -0.13167 0.01093 -12.04 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2395 on 58 degrees of freedom
Multiple R-squared: 0.7143, Adjusted R-squared: 0.7094
F-statistic: 145 on 1 and 58 DF, p-value: < 2.2e-16
----------------------
Linearity:
13.16667
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