Updated existing methods' documentation to new format

source/anova_documentation.rst

@@ -42,7 +42,7 @@ anova methods
 
 
 
-Effect size measures formulas
+Effect Size Measures Formulas
 =============================
 By default, this method will return the measures of :math:`R^2`, :math:`\text{Adj. }R^2`, :math:`\eta^2`, and :math:`\omega^2`;
 note that for the factor terms the reported :math:`\eta^2` and :math:`\omega^2` will be partial, i.e. :math:`\eta^2_p` and :math:`\omega^2_p` respectively.

source/conf.py

@@ -20,13 +20,13 @@
 # -- Project information -----------------------------------------------------
 
 project = 'researchpy'
-copyright = '2018-2021, Corey Bryant'
+copyright = '2018-2022, Corey Bryant'
 author = 'Corey Bryant'
 
 # The short X.Y version
-version = '0.3.2'
+version = '0.3.5'
 # The full version, including alpha/beta/rc tags
-release = '0.3.2'
+release = '0.3.5'
 
 
 # -- General configuration ---------------------------------------------------

source/corr_case_documentation.rst

@@ -1,4 +1,8 @@
+*************
 corr_case()
+*************
+
+Description
 ===========
 Conducts Pearson (default method), Spearman rank, or Kendall's Tau-b correlation analysis using
 case wise deletion. Returns the relevant information and results in 3 DataFrames
@@ -11,8 +15,11 @@ DataFrame 2 contains the r value results in a matrix style look.
 
 DataFrame 3 contains the p-values in a matrix style look.
 
-Arguments
----------
+Parameters
+==========
+
+Input
+-----
 **def corr_case(dataframe, method = "pearson")**
 
   * **dataframe** can either be a single Pandas Series or multiple Series/an
@@ -31,7 +38,10 @@ Arguments
  .. _Kendall Tau-b: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kendalltau.html
 
 Examples
---------
+========
+
+Loading Packages and Data
+-------------------------
 .. code:: python
 
     import researchpy, numpy, pandas
@@ -42,6 +52,10 @@ Examples
     df = pandas.DataFrame(numpy.random.randint(10, size= (100, 2)),
                       columns= ['beck', 'srq'])
 
+
+Pearson r
+---------
+
 .. code:: python
 
     # Since it returns 3 DataFrames for easy exporting, if the DataFrames

source/corr_pair_documentation.rst

@@ -1,4 +1,8 @@
+***********
 corr_pair()
+***********
+
+Description
 ===========
 Conducts Pearson (default method), Spearman rank, or Kendall's Tau-b correlation analysis using
 pair wise deletion. Returns the relevant information and results in 1 DataFrame
@@ -8,9 +12,11 @@ DataFrame 1 contains the variables being compared in the index, followed by the
 corresponding r value, p-value, and N for the groups being compared.
 
 
+Parameters
+==========
 
-Arguments
----------
+Input
+-----
 **corr_pair(dataframe, method= "pearson")**
 
 * **dataframe** can either be a single Pandas Series or multiple Series/an
@@ -30,7 +36,10 @@ Arguments
 
 
 Examples
---------
+========
+
+Loading Packages and Data
+-------------------------
 .. code:: python
 
     import researchpy, numpy, pandas
@@ -42,6 +51,8 @@ Examples
                       columns= ['mental_score', 'physical_score', 'emotional_score',
                                'happiness_index'])
 
+Pearson r
+---------
 .. code:: python
 
     # Can pass the entire DataFrame or multiple Series

source/crosstab_documentation.rst

@@ -1,5 +1,9 @@
+**********
 crosstab()
-==========
+**********
+
+Description
+===========
 Returns up to 3 DataFrames depending on what desired. Can calculate row, column,
 or cell percentages if requested. Otherwise, counts are returned as the default.
 
@@ -12,8 +16,11 @@ the expected frequency table.
 
 
 
-Arguments
-----------
+Parameters
+==========
+
+Input
+------
 **crosstab(group1, group2, prop= None, test = False, margins= True,
 correction = None, cramer_correction = None, exact = False, expected_freqs= False)**
 
@@ -22,6 +29,7 @@ correction = None, cramer_correction = None, exact = False, expected_freqs= Fals
     the row percentages, 'column' will calculate the column percentages, and 'cell'
     will calculate the cell percentage based on the entire sample
   * **test**, can take "chi-square", "g-test", "mcnemar", or "fisher".
+
       * If "chi-square", the chi-square (:math:`\chi^2`) test of independence :cite:`scipy_chi2` will
         be calculated and returned in a second DataFrame.
       * If "g-test", will conduct the G-test (likelihood-ratio :math:`\chi^2`) :cite:`scipy_chi2` and
@@ -42,8 +50,11 @@ correction = None, cramer_correction = None, exact = False, expected_freqs= Fals
   * **expected_freqs**, if True, will return a DataFrame that contains the
     expected counts for each cell. Not a valid argurment for *mcnemar* test.
 
-**returns**
-  * Up to 3 Pandas DataFrames as a tuple;
+Returns
+--------
+
+Up to 3 Pandas DataFrames will be returned within a tuple:
+
       * First DataFrame is always the crosstab table with either the counts,
         cell, row, or column percentages
       * Second DataFrame is either the test results or the expected frequencies.
@@ -60,14 +71,14 @@ correction = None, cramer_correction = None, exact = False, expected_freqs= Fals
 
 
 
-Effect size measures formulas
------------------------------
+Effect Size Measures Formulas
+=============================
 .. note::
   If adjusted :math:`\chi^2` values are used in the test's calculation, then those
   adjusted :math:`\chi^2` values are also used to calculate effect size.
 
 Cramer's Phi (2x2 table)
-^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------
 For analyses were it's a 2x2 table, the following formula is used to
 calculate Cramer's Phi (:math:`\phi`) :cite:`cramer2016`:
 
@@ -79,7 +90,7 @@ Where N = total number of observations in the analysis
 
 
 Cramer's V (RxC where R or C > 2)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+---------------------------------
 For analyses were it's a table that is larger than a 2x2, the
 following formula is used to calculate Cramer's V :cite:`cramer2016`:
 
@@ -101,7 +112,10 @@ Where r is the number of rows and c is the number of columns, and
 
 
 Examples
---------
+========
+
+Loading Packages and Data
+-------------------------
 .. code:: python
 
     import researchpy, pandas, numpy
@@ -161,6 +175,8 @@ Examples
     </div>
 
 
+Crosstabulation with Frequency
+------------------------------
 
 .. code:: python
 
@@ -228,13 +244,14 @@ Examples
     </div>
 
 
+Crosstabulation with Cell Percentages
+-------------------------------------
+Cell percentages are calculated by taking the frequency of the cell and dividing it by the total N.
+For example, the cell proportion for :math:`\text{disease}_0` and :math:`\text{alive}_0` = :math:`\frac{9}{101}`.
 
 .. code:: python
 
-    # Demonstration of calculating cell proportions
-
     crosstab = researchpy.crosstab(df['disease'], df['alive'], prop= "cell")
-
     crosstab
 
 .. raw:: html
@@ -295,13 +312,11 @@ Examples
     </div>
 
 
-
+Crosstabulation with Row Percentages
+-------------------------------------
 .. code:: python
 
-    # Demonstration of calculating row proportions
-
     crosstab = researchpy.crosstab(df['disease'], df['alive'], prop= "row")
-
     crosstab
 
 .. raw:: html
@@ -362,13 +377,12 @@ Examples
     </div>
 
 
+Crosstabulation with Column Percentages
+---------------------------------------
 
 .. code:: python
 
-    # Demonstration of calculating column proportions
-
     crosstab = researchpy.crosstab(df['disease'], df['alive'], prop= "col")
-
     crosstab
 
 .. raw:: html
@@ -429,6 +443,8 @@ Examples
     </div>
 
 
+Chi Squared (:math:`\chi^2`) Test of Independence
+--------------------------------------------------
 
 .. code:: python
 
@@ -615,13 +631,11 @@ Examples
     </div>
 
 
-
+G-test
+--------
 .. code:: python
 
-    # Can also conduct the G-test (likelihood-ratio chi-square)
-
     crosstab, res = researchpy.crosstab(df['disease'], df['alive'], test= "g-test")
-
     res
 
 .. raw:: html
@@ -656,16 +670,15 @@ Examples
     </div>
 
 
-
+Fisher's Exact test
+-------------------
 .. code:: python
 
-    # Can also conduct Fisher's exact test
-
     # Need 2x2 data for Fisher's test.
     numpy.random.seed(345)
 
     df = pandas.DataFrame(numpy.random.randint(2, size= (90, 2)),
-                      columns= ['tx', 'cured'])
+                          columns= ['tx', 'cured'])
 
     crosstab, res = researchpy.crosstab(df['tx'], df['cured'], test= "fisher")
 
@@ -764,16 +777,16 @@ Examples
     </div>
 
 
+McNemar test
+-------------
+Make sure that the outcomes are labelled the same in both variables.
 
 .. code:: python
 
-    # Lastly, the McNemar test
-    # Make sure your outcomes are labelled the same in
-    # both variables
     numpy.random.seed(345)
 
     df = pandas.DataFrame(numpy.random.randint(2, size= (90, 2)),
-                      columns= ['time1', 'time2'])
+                          columns= ['time1', 'time2'])
 
     crosstab, res = researchpy.crosstab(df['time1'], df['time2'], test= "mcnemar")
 
@@ -869,7 +882,7 @@ Examples
 
 
 References
-----------
+==========
 .. bibliography:: refs.bib
    :list: bullet
    :cited:

source/difference_test_documentation.rst

@@ -3,17 +3,17 @@ difference_test()
 *****************
 
 Description
-============
+===========
 Conducts a few different statistical tests which test for a difference between
 independent or related samples with or without equal variances and has the ability
 to calculate the effect size of the observed difference. The data is
 returned in a Pandas DataFrame by default, but can be returned as a dictionary
 if specified.
 
-This method is similar to researchpy.ttest(), except it allows the user to use
-the formula syntax.
+This method is similar to researchpy.ttest(), except it allows the user to use the formula syntax.
 
 This method can perform the following tests:
+
   * Independent sample t-test :cite:`2018:scipy_ttest_ind`
 
       * `psudo-code: difference_test(formula_like, data, equal_variances = True, independent_samples = True)`
@@ -125,8 +125,8 @@ Welch (1947)
   -2 + \frac{(\frac{s^2_x}{n_x} + \frac{s^2_y}{n_y})^2}{\frac{(\frac{s^2_x}{n_x})^2}{n_x+1} + \frac{(\frac{s^2_y}{n_y})^2}{n_y+1}}
 
 
-Effect size measures formulas
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Effect Size Measures Formulas
+=============================
 
 Cohen's d\ :sub:`s` (between subjects design)
 """"""""""""""""""""""""""""""""""""""""""""""

source/ttest_documentation.rst

@@ -8,6 +8,7 @@ Conducts various comparison tests between two groups and returns data tables as
 Pandas DataFrames with relevant information pertaining to the statistical test conducted.
 
 This method can perform the following tests:
+
   * Independent sample t-test :cite:p:`scipy_ttest_ind`
 
       * `psudo-code: ttest(group1, group2, equal_variances = True, paired = False)`
@@ -101,7 +102,7 @@ Welch (1947)
 
 
 
-Effect size measures formulas
+Effect Size Measures Formulas
 =============================
 
 Cohen's d\ :sub:`s` (between subjects design)