compactletterdisplay 0.11

A compact letter display implementation in Python, which summarizes results of posthoc comparisons.

Compact Letter Display in Python

Description

This is a collection of scripts that perform analysis of variance (ANOVA), posthoc comparison tests (Tukey HSD and Fisher's LSD), and generate a compact letter display, representing a summary of the results.

Compact Letter Display Algorithm

cld_calculator.py uses the insert-absorb algorithm from "Hans-Peter Piepho (2004), An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons, Journal of Computational and Graphical Statistics, 13(2), 456--466."

Here is the insert-and-absorb algorithm:

1. Generate a column connecting all treatments (i.e., give them all the same letter).
2. For each significant comparison, do the following:

• For each column currently in the display, do the following:
  • If the column connects the two significantly different treatments (i.e., has the same letter for the two significantly different treatments), then do the following:
    • Duplicate the column.
    • In the first of the two columns, delete the letter corresponding to the one treatment. If possible, absorb the column into another column.
    • In the second of the two columns, delete the letter corresponding to the other treatment. If possible, absorb the column into another column.

Parameters for anova_cld()

The following parameters can be customized when using anova_cld():

1. data: This can be a pandas DataFrame or a path to a .csv or .xlsx file which contains the data to be analyzed.

2. columns (default: None): This is an optional parameter, a list of column names to be used in the test. If it is None, the test is performed on all columns in the data.

3. alpha (default: 0.05): This sets the significance level for the ANOVA and pairwise comparison tests.

4. method (default: "TukeyHSD"): This lets you choose the method for pairwise comparison. It can either be "TukeyHSD" for Tukey's Honest Significant Difference test or "FisherLSD" for Fisher's Least Significant Difference test.

5. verbose (default: "False"): This lets you print results of the ANOVA and pairwise comparison tests.

Usage

An example of creating a compact letter display from a pandas DataFrame:

import pandas as pd
import compactletterdisplay

# Create your DataFrame:
df = pd.DataFrame({
'control': [1.2, 3.6, 4.2, 2.9, 3.5],
'treatment1': [33.4, 53.7, 23.8, 43.9, 33.7],
'treatment2': [4.2, 2.7, 3.5, 4.1, 3.3],
'treatment3': [33.3, 51.7, 22.5, 43.0, 32.6]
})

# Define columns to perform comparison test on.
columns = ['control', 'treatment1', 'treatment2', 'treatment3']

# Perform ANOVA, pairwise comparison, get compact letter displays
alpha = 0.1
result_df = compactletterdisplay.anova_cld(df, columns, alpha)

print(result_df)

An example using a CSV file:

from compactletterdisplay.pairwise_comp import anova_cld

filepath = "example.csv"  # update with your csv or xlsx file path
alpha = 0.05

# directly pass the filepath to the function
result_df = anova_cld(filepath, alpha=alpha, method="FisherLSD", verbose = True)

print(result_df)

list_of_cld = result_df['CLD'].tolist()

print(list_of_cld)

Output

The output of the CSV file example above is:

ANOVA results:
F: 29.851698144175202
p-value: 8.654291303569279e-07

 Fisher's LSD results:
==========================================================
  group1     group2   p-value     reject
----------------------------------------------------------
  control   treatment1    0.00015     True
  control   treatment2    0.43360     False
  control   treatment3    0.00015     True
  treatment1   treatment2    0.00016     True
  treatment1   treatment3    0.88336     False
  treatment2   treatment3    0.00016     True
========================================================== 

        Group   Mean CLD
0     control   3.08   a
1  treatment1  37.70   b
2  treatment2   3.56   a
3  treatment3  36.62   b
['a', 'b', 'a', 'b']