A simple library for calculating Gauge RnR
Project description
Gauge R&R
Install
pip install GaugeRnR
Example
The package can be used in the following way:
from gaugeRnR import GaugeRnR
import numpy as np
# The input should be structeted in a 3d
# numpy array n[i,j,k] where
# i = operator, j = part, k = measurement
# Example:
# m1 m2 m3
data = np.array( #
[[[3.29, 3.41, 3.64], # p1 | o1
[2.44, 2.32, 2.42], # p2
[4.34, 4.17, 4.27], # p3
[3.47, 3.5, 3.64], # p4
[2.2, 2.08, 2.16]], # p5
[[3.08, 3.25, 3.07], # p1 | o2
[2.53, 1.78, 2.32], # p2
[4.19, 3.94, 4.34], # p3
[3.01, 4.03, 3.2], # p4
[2.44, 1.8, 1.72]], # p5
[[3.04, 2.89, 2.85], # p1 | o3
[1.62, 1.87, 2.04], # p2
[3.88, 4.09, 3.67], # p3
[3.14, 3.2, 3.11], # p4
[1.54, 1.93, 1.55]]]) # p5
g = GaugeRnR(data)
g.calculate()
print(g.toTabulare())
This will result in the following table:
Sources of Variance | DF | SS | MS | Var (σ²) | Std (σ) | F-value | P-value |
---|---|---|---|---|---|---|---|
Operator | 2 | 1.63 | 0.815 | 0.054 | 0.232 | 100.322 | 0.000 |
Part | 4 | 28.909 | 7.227 | 0.802 | 0.896 | 889.458 | 0.000 |
Operator by Part | 8 | 0.065 | 0.008 | 0 | 0 | 0.142 | 0.996 |
Measurment | 30 | 1.712 | 0.057 | 0.057 | 0.239 | ||
Total | 44 | 32.317 | 0.734 | 0.913 | 0.956 |
To access the result from the Gauge RnR data directly:
from gaugeRnR import GaugeRnR, Component, Result
.
.
.
g = GaugeRnR(data)
result = g.calculate()
F = result[Result.F]
>>> print(F[Component.OPERATOR])
100.322
For more examples of how to use this library take a look at the unit tests!
Documentations
This GaugeRnR package was built and tested using the resources bellow. If you want to learn more about Gauge RnR and ANOVA they are a great place to start!
Project details
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GaugeRnR-0.2.0.tar.gz
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