AutoStatLib 0.2.20

AutoStatLib - a simple statistical analysis tool

AutoStatLib - python library for automated statistical analysis

To install run the command:

pip install autostatlib

Example use case:

See the /demo directory on Git repo or use the following example:

import numpy as np
import AutoStatLib

# generate random data:
groups = 2
n = 30

# normal data
data_norm = [list(np.random.normal(.5*i + 4, abs(1-.2*i), n))
        for i in range(groups)]

# non-normal data
data_uniform = [list(np.random.uniform(i+3, i+1, n)) for i in range(groups)]


# set the parameters:
paired = False     # is groups dependent or not
tails = 2          # two-tailed or one-tailed result
popmean = 0        # population mean - only for single-sample tests needed

# initiate the analysis
analysis = AutoStatLib.StatisticalAnalysis(
    data_norm, paired=paired, tails=tails, popmean=popmean)

now you can preform automated statistical test selection:

analysis.RunAuto()

or you can choose specific tests:

# 2 groups independent:
analysis.RunTtest()
analysis.RunMannWhitney()

# 2 groups paired"
analysis.RunTtestPaired()
analysis.RunWilcoxon()

# 3 and more independed groups comparison:
analysis.RunOnewayAnova()
analysis.RunKruskalWallis()

# 3 and more depended groups comparison:
analysis.RunOnewayAnovaRM()
analysis.RunFriedman()

# single group tests"
analysis.RunTtestSingleSample()
analysis.RunWilcoxonSingleSample()

Test summary will be printed to the console. You can also get it as a python string via GetSummary() method.

---

Test results are accessible as a dictionary via GetResult() method:

results = analysis.GetResult()

The results dictionary keys with representing value types:

{
    'p_value' :                    String
    'Significance(p<0.05)' :       Boolean
    'Stars_Printed' :              String
    'Test_Name' :                  String
    'Groups_Compared' :            Integer
    'Population_Mean' :            Float   (taken from the input)
    'Data_Normaly_Distributed' :   Boolean
    'Parametric_Test_Applied' :    Boolean
    'Paired_Test_Applied' :        Boolean
    'Tails' :                      Integer (taken from the input)
    'p_value_exact' :              Float
    'Stars' :                      Integer
    'Warnings' :                   String
    'Groups_N' :                   List of integers
    'Groups_Median' :              List of floats
    'Groups_Mean' :                List of floats
    'Groups_SD' :                  List of floats
    'Groups_SE' :                  List of floats
    'Samples' :                    List of input values by groups
                                           (taken from the input)
    'Posthoc_Matrix' :             2D List of floats
    'Posthoc_Matrix_bool' :        2D List of Boolean
    'Posthoc_Matrix_printed':      2D List of String
    'Posthoc_Matrix_stars':        2D List of String
}

If errors occured, GetResult() returns an empty dictionary

---

Alpha dev status.

TODO:

-- Anova: posthocs
-- Anova: add 2-way anova and 3-way anova
-- onevay Anova: add repeated measures (for normal dependent values) with and without Gaisser-Greenhouse correction
-- onevay Anova: add Brown-Forsithe and Welch (for normal independent values with unequal SDs between groups)
-- paired T-test: add ratio-paired t-test (ratios of paired values are consistent)
-- add Welch test (for norm data unequal variances)
-- add Kolmogorov-smirnov test (unpaired nonparametric 2 sample, compare cumulative distributions)
-- add independent t-test with Welch correction (do not assume equal SDs in groups)
-- add correlation test, correlation diagram
-- add linear regression, regression diagram
-- add QQ plot
-- n-sample tests: add onetail option

✅ done -- detailed normality test results
✅ done -- added posthoc: Kruskal-Wallis Dunn's multiple comparisons

tests check:
1-sample:
✅ok --Wilcoxon 2,1 tails
✅ok --t-tests 2,1 tails

2-sample:
✅ok --Wilcoxon 2,1 tails
✅ok --Mann-whitney 2,1 tails
✅ok --t-tests 2,1 tails

n-sample:
✅ok --Kruskal-Wallis 2 tail
✅ok --Dunn's multiple comparisons
✅ok --Friedman 2 tail
✅ok --one-way ANOVA 2-tailed
✅ok --Tukey`s multiple comparisons