Calculate common language effect size for 2 independent samples. See Using_CLES.ipynb for a notebook of given examples.
Syntax
thetaHat = CL.CLES_2independent(data1, data2)
Description
A
thetaHat = CL.CLES_2independent(data1, data2) returns common language effect size for 2 independent samples (data1 and data2). example
Examples
Example 1
Generate some random data and find common language effect size.
group1 = np.random.normal(0, 1, (100,))
group2 = np.random.normal(5,3, (100,))
CL.CLES_2independent(group1, group2)
thetaHat = 96.08
data1
Data vector 1.
Vector of data from group 1.
Data Types: (vector, numeric)
data2
Data vector 2.
Vector of data from group 2.
Data Types: (vector, numeric)
Output
thetaHat
Effect size.
Common language effect size. This value will be between 50 andn 100. This value can be interpreted as the percentage or chance a value from one group is larger than a value from the other group. A thetHat of 50 would me groups are essentially from same distribution. A thetaHat of 100 would mean that 100% of the data in one group is larger than the data in the other group.
Data Types: (scalar, float, numeric)
More About
Lecture
I refrained from outputting size of effect (e.g., ‘small’, ‘medium’, ‘large’) because these are arbitrary and should be thought of as such.
Tips
Issues and Discussion
Issues and Discussion.
If you don’t know how to use github (or don’t want to), just send me an email.