Source code for statsmodels.sandbox.regression.try_ols_anova

''' convenience functions for ANOVA type analysis with OLS

Note: statistical results of ANOVA are not checked, OLS is
checked but not whether the reported results are the ones used

includes form2design for creating dummy variables

 * ...


from statsmodels.compat.python import lmap
import numpy as np
#from scipy import stats
import statsmodels.api as sm

[docs] def data2dummy(x, returnall=False): '''convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only''' x = x.ravel() groups = np.unique(x) if returnall: return (x[:, None] == groups).astype(int) else: return (x[:, None] == groups).astype(int)[:,:-1]
[docs] def data2proddummy(x): '''creates product dummy variables from 2 columns of 2d array drops last dummy variable, but not from each category singular with simple dummy variable but not with constant quickly written, no safeguards ''' #brute force, assumes x is 2d #replace with encoding if possible groups = np.unique(lmap(tuple, x.tolist())) #includes singularity with additive factors return (x==groups[:,None,:]).all(-1).T.astype(int)[:,:-1]
[docs] def data2groupcont(x1,x2): '''create dummy continuous variable Parameters ---------- x1 : 1d array label or group array x2 : 1d array (float) continuous variable Notes ----- useful for group specific slope coefficients in regression ''' if x2.ndim == 1: x2 = x2[:,None] dummy = data2dummy(x1, returnall=True) return dummy * x2
# Result strings #the second leaves the constant in, not with NIST regression #but something fishy with res.ess negative in examples ? #not checked if these are all the right ones anova_str0 = ''' ANOVA statistics (model sum of squares excludes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ess)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' anova_str = ''' ANOVA statistics (model sum of squares includes constant) Source DF Sum Squares Mean Square F Value Pr > F Model %(df_model)i %(ssmwithmean)f %(mse_model)f %(fvalue)f %(f_pvalue)f Error %(df_resid)i %(ssr)f %(mse_resid)f CTotal %(nobs)i %(uncentered_tss)f %(mse_total)f R squared %(rsquared)f ''' def anovadict(res): '''update regression results dictionary with ANOVA specific statistics not checked for completeness ''' ad = {} ad.update(res.__dict__) #dict does not work with cached attributes anova_attr = ['df_model', 'df_resid', 'ess', 'ssr','uncentered_tss', 'mse_model', 'mse_resid', 'mse_total', 'fvalue', 'f_pvalue', 'rsquared'] for key in anova_attr: ad[key] = getattr(res, key) ad['nobs'] = res.model.nobs ad['ssmwithmean'] = res.uncentered_tss - res.ssr return ad
[docs] def form2design(ss, data): '''convert string formula to data dictionary ss : str * I : add constant * varname : for simple varnames data is used as is * F:varname : create dummy variables for factor varname * P:varname1*varname2 : create product dummy variables for varnames * G:varname1*varname2 : create product between factor and continuous variable data : dict or structured array data set, access of variables by name as in dictionaries Returns ------- vars : dictionary dictionary of variables with converted dummy variables names : list list of names, product (P:) and grouped continuous variables (G:) have name by joining individual names sorted according to input Examples -------- >>> xx, n = form2design('I a F:b P:c*d G:c*f', testdata) >>> xx.keys() ['a', 'b', 'const', 'cf', 'cd'] >>> n ['const', 'a', 'b', 'cd', 'cf'] Notes ----- with sorted dict, separate name list would not be necessary ''' vars = {} names = [] for item in ss.split(): if item == 'I': vars['const'] = np.ones(data.shape[0]) names.append('const') elif ':' not in item: vars[item] = data[item] names.append(item) elif item[:2] == 'F:': v = item.split(':')[1] vars[v] = data2dummy(data[v]) names.append(v) elif item[:2] == 'P:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2proddummy(np.c_[data[v[0]],data[v[1]]]) names.append(''.join(v)) elif item[:2] == 'G:': v = item.split(':')[1].split('*') vars[''.join(v)] = data2groupcont(data[v[0]], data[v[1]]) names.append(''.join(v)) else: raise ValueError('unknown expression in formula') return vars, names
[docs] def dropname(ss, li): '''drop names from a list of strings, names to drop are in space delimited list does not change original list ''' newli = li[:] for item in ss.split(): newli.remove(item) return newli
if __name__ == '__main__': # Test Example with created data # ------------------------------ nobs = 1000 testdataint = np.random.randint(3, size=(nobs,4)).view([('a',int),('b',int),('c',int),('d',int)]) testdatacont = np.random.normal( size=(nobs,2)).view([('e',float), ('f',float)]) import numpy.lib.recfunctions dt2 = numpy.lib.recfunctions.zip_descr((testdataint, testdatacont),flatten=True) # concatenate structured arrays testdata = np.empty((nobs,1), dt2) for name in testdataint.dtype.names: testdata[name] = testdataint[name] for name in testdatacont.dtype.names: testdata[name] = testdatacont[name] #print(form2design('a',testdata) if 0: # print(only when nobs is small, e.g. nobs=10 xx, n = form2design('F:a',testdata) print(xx) print(form2design('P:a*b',testdata)) print(data2proddummy(np.c_[testdata['a'],testdata['b']])) xx, names = form2design('a F:b P:c*d',testdata) #xx, names = form2design('I a F:b F:c F:d P:c*d',testdata) xx, names = form2design('I a F:b P:c*d', testdata) xx, names = form2design('I a F:b P:c*d G:a*e f', testdata) X = np.column_stack([xx[nn] for nn in names]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).fit() #results print(rest1.params) print(anova_str % anovadict(rest1)) X = np.column_stack([xx[nn] for nn in dropname('ae f', names)]) # simple test version: all coefficients equal to one y = X.sum(1) + 0.01*np.random.normal(size=(nobs)) rest1 = sm.OLS(y,X).fit() print(rest1.params) print(anova_str % anovadict(rest1)) # Example: from Bruce # ------------------- #get data and clean it #^^^^^^^^^^^^^^^^^^^^^ # requires file '' posted by Bruce # read data set and drop rows with missing data dt_b = np.dtype([('breed', int), ('sex', int), ('litter', int), ('pen', int), ('pig', int), ('age', float), ('bage', float), ('y', float)]) dta = np.genfromtxt('', dt_b,missing='.', usemask=True) print('missing', [dta.mask[k].sum() for k in dta.dtype.names]) m = dta.mask.view(bool) droprows = m.reshape(-1,len(dta.dtype.names)).any(1) # get complete data as plain structured array # maybe does not work with masked arrays dta_use_b1 = dta[~droprows,:].data print(dta_use_b1.shape) print(dta_use_b1.dtype) #Example b1: variables from Bruce's glm #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # prepare data and dummy variables xx_b1, names_b1 = form2design('I F:sex age', dta_use_b1) # create design matrix X_b1 = np.column_stack([xx_b1[nn] for nn in dropname('', names_b1)]) y_b1 = dta_use_b1['y'] # estimate using OLS rest_b1 = sm.OLS(y_b1, X_b1).fit() # print(results) print(rest_b1.params) print(anova_str % anovadict(rest_b1)) #compare with original version only in original version #print(anova_str % anovadict(res_b0)) # Example: use all variables except pig identifier #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ allexog = ' '.join(dta.dtype.names[:-1]) #'breed sex litter pen pig age bage' xx_b1a, names_b1a = form2design('I F:breed F:sex F:litter F:pen age bage', dta_use_b1) X_b1a = np.column_stack([xx_b1a[nn] for nn in dropname('', names_b1a)]) y_b1a = dta_use_b1['y'] rest_b1a = sm.OLS(y_b1a, X_b1a).fit() print(rest_b1a.params) print(anova_str % anovadict(rest_b1a)) for dropn in names_b1a: print(('\nResults dropping', dropn)) X_b1a_ = np.column_stack([xx_b1a[nn] for nn in dropname(dropn, names_b1a)]) y_b1a_ = dta_use_b1['y'] rest_b1a_ = sm.OLS(y_b1a_, X_b1a_).fit() #print(rest_b1a_.params) print(anova_str % anovadict(rest_b1a_))

Last update: Jul 16, 2024