RyanJoiner#

class normtest.ryan_joiner.RyanJoiner(alpha=0.05, safe=True, cte_alpha='3/8', weighted=False, **kwargs)[source]#

This class instantiates an object to perform the Ryan-Joiner Normality test [1].

References

[1]

RYAN, T. A., JOINER, B. L. Normal Probability Plots and Tests for Normality, Technical Report, Statistics Department, The Pennsylvania State University, 1976. Available at www.additive-net.de. Access on: 22 Jul. 2023.

Examples

>>> from normtest import RyanJoiner
>>> import numpy as np
>>> x = np.array([6, 1, -4, 8, -2, 5, 0])
>>> test = RyanJoiner()
>>> test.fit(x)
>>> print(test.normality)
RyanJoiner(statistic=0.9844829186140105, critical=0.8977794003662074, p_value='p > 0.100', conclusion='Fail to reject H₀')

Attributes:

statisticfloat (positive): The test statistic;
criticalfloat: The critical value of the test;
p_valuefloat or str: The probability of the test;
conclusionstr: The test conclusion (e.g, Normal/Not Normal).
alphafloat, optional: The level of significance (\(\alpha\)). Default is 0.05;
normalitynamed tuple: A tuple with the main test results summarized
normality_hypothesisstr: Description of the Normality test
safebool, optional: Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);

Methods

fit(x_data)	Applies the Ryan-Joiner Normality test;
dist_plot(axes, alphas=[0.10, 0.05, 0.01]):	Generates axis with critical data from the Ryan-Joiner Normality test;
correlation_plot(axes)	Generates an axis with the Ryan-Joiner test correlation graph;
line_up(seed=None, correct=False)	Generates a Figure with the correlation graphs for the line up method;
citation(export=False)	Returns the Ryan-Joiner’s test reference;

citation(export=False)[source]#

This method returns the reference from Ryan-Joiner’s test, with the option to export the reference in .bib format.

Parameters:

exportbool: Whether to export the reference as RyanJoiner1976.bib file (True) or not (False, default);

Returns:

referencestr: The Ryan-Joiner Test reference;

correlation_plot(axes)[source]#

This method generates an axis with the correlation plotfor the Ryan-Joiner Normality test.

Parameters:

axesmatplotlib.axes.SubplotBase: The axis of the graph;

Returns:

axesmatplotlib.axes.SubplotBase: The axis of the graph;

See also

correlation_plot

dist_plot(axes, critical_range=(4, 50))[source]#

This method generates an axis with the critical data from the Ryan-Joiner Normality test.

Parameters:

axesmatplotlib.axes.SubplotBase

The axis of the graph;

critical_rangetuple (optional), with two elements:

x_minint, optional: The lower range of the number of observations for the critical values (default is 4).
x_maxint, optional: The upper range of the number of observations for the critical values (default is 50).

Returns:

axesmatplotlib.axes.SubplotBase: The axis of the graph;

See also

dist_plot

fit(x_data)[source]#

This method applies the Ryan-Joiner test.

Parameters:

x_datanumpy array: One dimension numpy array with at least 4 observations.

Returns:

x_datanumpy array: One dimension numpy array with at least 4 observations.
statisticfloat (positive): The test statistic;
criticalfloat: The critical value of the test;
p_valuefloat or str: The probability of the test;
conclusionstr: The test conclusion (e.g, Normal/Not Normal).
normalitynamed tuple: A tuple with the main test results summarized

See also

rj_test

get_alpha()#

Returns the current alpha value

Returns:

alphafloat: The level of significance (\(\alpha\)).

get_safe()#

Returns the current status of parameter safe

Returns:

safebool, optional: Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);

line_up(seed=None, correct=False)[source]#

This method generates a Figure with the correlation graphs for the line up method.

Parameters:

seedint or None, optional: A numerical value that generates a new set or repeats pseudo-random numbers. Use a positive integer value to be able to repeat results. Default is None what generates a random seed;
correctbool, optional: Whether the x_data is to be drawn in red (False) or black (True, default);

Returns:

figmatplotlib.figure.Figure: A figure with the generated graphics;

See also

line_up

set_alpha(alpha)#

Changes the alpha value

Parameters:

alphafloat: The level of significance (\(\alpha\)).

Notes

This method only allows input of type float and between 0.0 and 1.0.

set_safe(safe)#

Changes the current status of parameter safe

Parameters:

safebool, optional: Whether to check the inputs before performing the calculations (True, default) or not (False). Useful for beginners to identify problems in data entry (may reduce algorithm execution time);