Loading Data

Loading data and relevant groups

---

load

def load(
    data, idx:NoneType=None, # List of column names (if 'x' is not supplied) or of category names
    # (if 'x' is supplied). This can be expressed as a tuple of tuples,
    # with each individual tuple producing its own contrast plot
    x:NoneType=None, # Column name(s) of the independent variable. This can be expressed as
    # a list of 2 elements if and only if 'delta2' is True; otherwise it
    # can only be a string.
    y:NoneType=None, # Column names for data to be plotted on the x-axis and y-axis.
    paired:NoneType=None, # The type of the experiment under which the data are obtained. If 'paired'
    # is None then the data will not be treated as paired data in the subsequent
    # calculations. If 'paired' is 'baseline', then in each tuple of x, other
    # groups will be paired up with the first group (as control). If 'paired' is
    # 'sequential', then in each tuple of x, each group will be paired up with
    # its previous group (as control).
    id_col:NoneType=None, # Required if `paired` is True.
    ci:int=95, # The confidence interval width. The default of 95 produces 95%
    # confidence intervals.
    resamples:int=5000, # The number of resamples taken to generate the bootstraps which are used
    # to generate the confidence intervals.
    random_seed:int=12345, # This integer is used to seed the random number generator during
    # bootstrap resampling, ensuring that the confidence intervals
    # reported are replicable.
    proportional:bool=False, # An indicator of whether the data is binary or not. When set to True, it
    # specifies that the data consists of binary data, where the values are
    # limited to 0 and 1. The code is not suitable for analyzing proportion
    # data that contains non-numeric values, such as strings like 'yes' and 'no'.
    # When False or not provided, the algorithm assumes that
    # the data is continuous and uses a non-proportional representation.
    delta2:bool=False, # Indicator of delta-delta experiment
    experiment:NoneType=None, # The name of the column of the dataframe which contains the label of
    # experiments
    experiment_label:NoneType=None,
    x1_level:NoneType=None, # A list of String to specify the order of subplots for delta-delta plots.
    # This can be expressed as a list of 2 elements if and only if 'delta2'
    # is True; otherwise it can only be a string.
    mini_meta:bool=False, # Indicator of weighted delta calculation.
    ps_adjust:bool=False, # Indicator of whether to adjust calculated p-value according to Phipson & Smyth (2010)
    # # https://doi.org/10.2202/1544-6115.1585
):

Loads data in preparation for estimation statistics.

This is designed to work with pandas DataFrames.

---

prop_dataset

def prop_dataset(
    group:Union[list, tuple, np.ndarray, dict],
    group_names:Optional[list]=None, # Accepts lists, tuples, or numpy ndarrays of numeric types.
):

Convenient function to generate a dataframe of binary data.

Example

import numpy as np
import pandas as pd
import scipy as sp
import dabest

Create dummy data for demonstration.

np.random.seed(88888)
N = 10
c1 = sp.stats.norm.rvs(loc=100, scale=5, size=N)
t1 = sp.stats.norm.rvs(loc=115, scale=5, size=N)
df = pd.DataFrame({"Control 1": c1, "Test 1": t1})

Load the data.

my_data = dabest.load(df, idx=("Control 1", "Test 1"))
my_data

DABEST v2024.03.29
==================
                  
Good afternoon!
The current time is Tue Mar 19 15:34:58 2024.

Effect size(s) 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.

For proportion plot.

np.random.seed(88888)
N = 10
c1 = np.random.binomial(1, 0.2, size=N)
t1 = np.random.binomial(1, 0.5, size=N)
df = pd.DataFrame({"Control 1": c1, "Test 1": t1})
my_data = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)