import numpy as np
import pandas as pd
import dabest
print("We're using DABEST v{}".format(dabest.__version__))We're using DABEST v2023.02.14Repeated Measures
Explanation of how to use dabest for repeated measures analysis.
DABEST version 2023.02.14 expands the repertoire of plots for experiments with repeated-measures designs. DABEST now allows the visualization of paired experiments with one control and multiple test groups, as well as repeated measurements of the same group. This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.
The repeated-measures function supports the calculation of effect sizes for paired data, either based on sequential comparisons (group i vs group i + 1) or baseline comparisons (control vs group i). To use these features, you can simply declare the argument paired = "sequential" or paired = "baseline" correspondingly while running dabest.load(). As in the previous version, you must also pass a column in the dataset that indicates the identity of each observation, using the id_col keyword.
(Please note that paired = True and paired = False are no longer valid in v2023.02.14)
Load Libraries
Create dataset for demo
from scipy.stats import norm # Used in generation of populations.
np.random.seed(9999) # Fix the seed so the results are replicable.
# pop_size = 10000 # Size of each population.
Ns = 20 # The number of samples taken from each population
# Create samples
c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
# Add a `gender` column for coloring the data.
females = np.repeat('Female', Ns/2).tolist()
males = np.repeat('Male', Ns/2).tolist()
gender = females + males
# Add an `id` column for paired data plotting.
id_col = pd.Series(range(1, Ns+1))
# Combine samples and gender into a DataFrame.
df = pd.DataFrame({'Control 1' : c1, 'Test 1' : t1,
'Control 2' : c2, 'Test 2' : t2,
'Control 3' : c3, 'Test 3' : t3,
'Test 4' : t4, 'Test 5' : t5, 'Test 6' : t6,
'Gender' : gender, 'ID' : id_col
})two_groups_paired_sequential = dabest.load(df, idx=("Control 1", "Test 1"),
paired="sequential", id_col="ID")two_groups_paired_sequentialDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:03 2023.
Paired effect size(s) for the sequential design of repeated-measures experiment
with 95% confidence intervals will be computed for:
1. Test 1 minus Control 1
5000 resamples will be used to generate the effect size bootstraps.two_groups_paired_baseline = dabest.load(df, idx=("Control 1", "Test 1"),
paired="baseline", id_col="ID")two_groups_paired_baselineDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:04 2023.
Paired effect size(s) for repeated measures against baseline
with 95% confidence intervals will be computed for:
1. Test 1 minus Control 1
5000 resamples will be used to generate the effect size bootstraps.When only 2 paired data groups are involved, assigning either baseline or sequential to paired will give you the same numerical results.
two_groups_paired_sequential.mean_diffDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:08 2023.
The paired mean difference for the sequential design of repeated-measures experiment
between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].
The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis ofzero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`two_groups_paired_baseline.mean_diffDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:09 2023.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].
The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis ofzero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`For paired data, we use slopegraphs (another innovation from Edward Tufte) to connect paired observations. Both Gardner-Altman and Cumming plots support this.
two_groups_paired_sequential.mean_diff.plot();
two_groups_paired_sequential.mean_diff.plot(float_contrast=False);
two_groups_paired_baseline.mean_diff.plot();
two_groups_paired_baseline.mean_diff.plot(float_contrast=False);
You can also create repeated-measures plots with multiple test groups.In this case, declaring paired to be sequential or baseline will generate different results.
sequential_repeated_measures = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3"),
paired="sequential", id_col="ID")sequential_repeated_measures.mean_diffDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:18 2023.
The paired mean difference for the sequential design of repeated-measures experiment
between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].
The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only.
The paired mean difference for the sequential design of repeated-measures experiment
between Test 1 and Test 2 is -1.02 [95%CI -1.36, -0.716].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for the sequential design of repeated-measures experiment
between Test 2 and Test 3 is 0.716 [95%CI 0.14, 1.22].
The p-value of the two-sided permutation t-test is 0.022, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis ofzero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`sequential_repeated_measures.mean_diff.plot();
baseline_repeated_measures = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3"),
paired="baseline", id_col="ID")baseline_repeated_measures.mean_diffDABEST v2023.02.14
==================
Good evening!
The current time is Sun Mar 19 22:39:26 2023.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.48 [95%CI 0.237, 0.73].
The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 2 is -0.542 [95%CI -0.975, -0.198].
The p-value of the two-sided permutation t-test is 0.014, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 3 is 0.174 [95%CI -0.297, 0.706].
The p-value of the two-sided permutation t-test is 0.505, calculated for legacy purposes only.
5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
Any p-value reported is the probability of observing theeffect size (or greater),
assuming the null hypothesis ofzero difference is true.
For each p-value, 5000 reshuffles of the control and test labels were performed.
To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`baseline_repeated_measures.mean_diff.plot();
Same as that for unpaired data, DABEST empowers you to perform complex visualizations and statistics for paired data as well.
multi_baseline_repeated_measures = dabest.load(df, idx=(("Control 1", "Test 1", "Test 2", "Test 3"),
("Control 2", "Test 4", "Test 5", "Test 6")),
paired="baseline", id_col="ID")
multi_baseline_repeated_measures.mean_diff.plot();