Fixups and update documentation
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@ -6,6 +6,8 @@ Data wrangling
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.. autofunction:: yli.utils.as_2groups
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.. autofunction:: yli.utils.as_numeric
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.. autofunction:: yli.utils.convert_pandas_nullable
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*p* values
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@ -31,7 +33,7 @@ Data wrangling
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.. attribute:: HTML
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Format as HTML (i.e. escape ``<``)
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Format as HTML (e.g. escape ``<``)
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Formula manipulation
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--------------------
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@ -41,3 +43,26 @@ Formula manipulation
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.. autofunction:: yli.utils.formula_factor_ref_category
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.. autofunction:: yli.utils.parse_patsy_term
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Library style
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-------------
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For API nomenclature, the following guidelines are used:
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* Prefer to call a test by its specific name (e.g. *anova* rather than *ftest* where applicable), unless most commonly known only by the distribution of the test statistic (e.g. *chi2*, *ttest*).
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..
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* A test/statistic is not referred to by both a distribution and specific name (e.g. *mannwhitney* rather than *mannwhitneyu*), unless required for disambiguation (e.g. *pearsonr* to distinguish the Pearson *χ*:sup:`2` test).
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..
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* The word "test" is omitted (e.g. *chi2* rather than *chi2test*), unless the name would otherwise be a single letter (e.g. *ttest*, *ftest*), or unless required for disambiguation (e.g. *LikelihoodRatioTestResult* to distinguish from the unrelated meaning of "likelihood ratio" in epidemiology).
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..
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* Underscores are usually omitted from the names of specific tests, test families and statistics (e.g. *ttest*, *oddsratio*, *pearsonr*, *pvalue*), but are used to separate these from other components (e.g. *ttest_ind*, *anova_oneway*, *lrtest_null*). There are a few exceptions (e.g. *rank_biserial*, *pseudo_rsquared*, *f_statistic*).
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..
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* The result class for a test has the same naming convention as the test function (e.g. *TTestResult* for *ttest_ind*), with abbreviations spelled out (e.g. *PearsonChiSquaredResult*, *LikelihoodRatioTestResult*); unless the result class is generic among several tests (e.g. *FTestResult* for *anova_oneway* and *RegressionResult.ftest*), or unless required for disambiguation (e.g. *PearsonChiSquaredResult* for *chi2*, as there are other *χ*:sup:`2` tests).
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@ -6,23 +6,23 @@ Functions
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.. autofunction:: yli.logit_then_regress
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.. autoclass:: yli.OrdinalLogit
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.. autoclass:: yli.PenalisedLogit
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.. autofunction:: yli.regress
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.. autofunction:: yli.vif
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Additional regression models
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----------------------------
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.. autoclass:: yli.OrdinalLogit
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.. autoclass:: yli.PenalisedLogit
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Result classes
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--------------
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.. autoclass:: yli.regress.BrantResult
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:members:
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.. autoclass:: yli.regress.CategoricalTerm
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:members:
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.. autoclass:: yli.regress.LikelihoodRatioTestResult
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:members:
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:inherited-members:
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@ -30,5 +30,11 @@ Result classes
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.. autoclass:: yli.regress.RegressionResult
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:members:
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Model terms
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-----------
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.. autoclass:: yli.regress.CategoricalTerm
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:members:
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.. autoclass:: yli.regress.SingleTerm
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:members:
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@ -26,9 +26,14 @@ def reload_me():
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import importlib
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import sys
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for _ in range(2): # Do it twice to make sure imports are also reloaded fully
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for k, v in list(sys.modules.items()):
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if k == 'yli' or k.startswith('yli.'):
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try:
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importlib.reload(v)
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except ModuleNotFoundError:
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except ModuleNotFoundError as ex:
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if ex.name.startswith('yli.'):
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# Must be due to a module which we deleted - can safely ignore
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pass
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else:
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raise ex
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@ -51,6 +51,7 @@ def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
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if data_cleaned.dtype in ('bool', 'boolean', 'category', 'object'):
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# Categorical data
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# FIXME: Sort order
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values = sorted(data_cleaned.unique())
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# Value counts
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@ -130,7 +130,7 @@ class RegressionResult:
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full_name, model_name, fit_method,
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nobs, dof_model, fitted_dt, cov_type,
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terms,
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llf, llnull,
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ll_model, ll_null,
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dof_resid, rsquared, f_statistic,
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comments,
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exp
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@ -178,9 +178,9 @@ class RegressionResult:
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# Model log-likelihood
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#: Log-likelihood of fitted model (*float*)
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self.llf = llf
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self.ll_model = ll_model
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#: Log-likelihood of null model (*float*)
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self.llnull = llnull
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self.ll_null = ll_null
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# Extra statistics (not all regression models have these)
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#: Degrees of freedom for the residuals (*int*; *None* if N/A)
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@ -201,7 +201,7 @@ class RegressionResult:
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def pseudo_rsquared(self):
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"""McFadden's pseudo *R*:sup:`2` statistic"""
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return 1 - self.llf/self.llnull
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return 1 - self.ll_model/self.ll_null
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def lrtest_null(self):
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"""
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@ -210,7 +210,7 @@ class RegressionResult:
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:rtype: :class:`LikelihoodRatioTestResult`
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"""
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statistic = -2 * (self.llnull - self.llf)
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statistic = -2 * (self.ll_null - self.ll_model)
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pvalue = 1 - stats.chi2.cdf(statistic, self.dof_model)
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return LikelihoodRatioTestResult(statistic, self.dof_model, pvalue)
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@ -308,7 +308,7 @@ class RegressionResult:
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# Fit individual logistic regressions
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logit_models = []
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for upper_limit in sorted(df[dep].unique())[:-1]: # FIXME: Sort order
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for upper_limit in sorted(df[dep].unique())[:-1]:
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dep_dichotomous = (df[dep] <= upper_limit).astype(int).reset_index(drop=True)
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logit_result = sm.Logit(dep_dichotomous, dmatrix_right).fit(disp=False, **self.fit_kwargs)
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@ -454,7 +454,7 @@ class RegressionResult:
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self.full_name, self.model_name, self.fit_method,
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self.nobs, self.dof_model, datetime.now(), 'Bootstrap',
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terms,
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self.llf, self.llnull,
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self.ll_model, self.ll_null,
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self.dof_resid, self.rsquared, self.f_statistic,
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self.comments,
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self.exp
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@ -499,8 +499,8 @@ class RegressionResult:
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# Otherwise report likelihood ratio test as overall test
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lrtest_result = self.lrtest_null()
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right_col.append(('LL-Model:', format(self.llf, '.2f')))
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right_col.append(('LL-Null:', format(self.llnull, '.2f')))
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right_col.append(('LL-Model:', format(self.ll_model, '.2f')))
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right_col.append(('LL-Null:', format(self.ll_null, '.2f')))
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if html:
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right_col.append(('<i>p</i> (LR):', fmt_p(lrtest_result.pvalue, PValueStyle.VALUE_ONLY | PValueStyle.HTML)))
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else:
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@ -859,9 +859,11 @@ def regress(
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# Single term
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terms[column] = SingleTerm(raw_name, beta, pvalues[raw_name])
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# Fit null model (for llnull)
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if hasattr(result, 'llnull'):
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llnull = result.llnull
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# Fit null model (for ll_null)
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if hasattr(result, 'll_null'):
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ll_null = result.ll_null
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elif hasattr(result, 'llnull'):
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ll_null = result.llnull
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else:
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# Construct null (intercept-only) model
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#result_null = model_class.from_formula(formula=dep + ' ~ 1', data=df).fit()
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@ -870,7 +872,7 @@ def regress(
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dm_exog['Intercept'].fillna(1, inplace=True)
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result_null = model_class(endog=dmatrices[0], exog=dm_exog).fit()
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llnull = result_null.llf
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ll_null = result_null.llf
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if model_class is sm.OLS:
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method_name = 'Least Squares'
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@ -897,7 +899,7 @@ def regress(
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full_name, model_class.__name__, method_name,
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result.nobs, result.df_model, datetime.now(), getattr(result, 'cov_type', 'nonrobust'),
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terms,
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result.llf, llnull,
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result.llf, ll_null,
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getattr(result, 'df_resid', None), getattr(result, 'rsquared', None), getattr(result, 'fvalue', None),
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[],
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exp
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@ -1030,7 +1032,7 @@ class OrdinalLogit(statsmodels.miscmodels.ordinal_model.OrderedModel):
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statsmodels-compatible model for computing ordinal logistic (or probit) regression
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The implementation subclasses statsmodels' native *OrderedModel*, but substitutes an alternative parameterisation used by R and Stata.
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The the native statsmodels implementation, the first cutoff term is the true cutoff, and further cutoff terms are log differences between consecutive cutoffs.
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In the native statsmodels implementation, the first cutoff parameter is the true cutoff, but further cutoff parameter are log differences between consecutive cutoffs.
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In this parameterisation, cutoff terms are represented directly in the model.
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**Example:**
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@ -109,6 +109,7 @@ def as_2groups(df, data, group):
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raise Exception('Got {} values for {}, expected 2'.format(len(groups), group))
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# Get 2 groups
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# FIXME: Sort order
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group1 = groups[0][0]
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data1 = df.loc[groups[0][1], data]
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group2 = groups[1][0]
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@ -118,7 +119,7 @@ def as_2groups(df, data, group):
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def as_numeric(data):
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"""
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Convert the given data to a numeric type, factorising if required
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Convert the data to a numeric type, factorising if required
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:param data: Data to convert
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:type df: Series
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