statsmodels.sandbox.regression.try_ols_anova.form2design statsmodels.sandbox.regression.try_ols_anova.form2design(ss, data) [source] convert string formula to data dictionary ss : string 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,

statsmodels.sandbox.stats.multicomp.catstack statsmodels.sandbox.stats.multicomp.catstack(args) [source]

statsmodels.sandbox.regression.try_ols_anova.dropname statsmodels.sandbox.regression.try_ols_anova.dropname(ss, li) [source] drop names from a list of strings, names to drop are in space delimeted list does not change original list

statsmodels.sandbox.regression.try_ols_anova.data2groupcont statsmodels.sandbox.regression.try_ols_anova.data2groupcont(x1, x2) [source] 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

statsmodels.sandbox.regression.try_ols_anova.data2dummy statsmodels.sandbox.regression.try_ols_anova.data2dummy(x, returnall=False) [source] convert array of categories to dummy variables by default drops dummy variable for last category uses ravel, 1d only

statsmodels.sandbox.regression.try_ols_anova.data2proddummy statsmodels.sandbox.regression.try_ols_anova.data2proddummy(x) [source] 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

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str statsmodels.sandbox.regression.try_catdata.labelmeanfilter_str(ys, x) [source]

statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd statsmodels.sandbox.regression.try_catdata.labelmeanfilter_nd(y, x) [source]

statsmodels.sandbox.regression.try_catdata.labelmeanfilter statsmodels.sandbox.regression.try_catdata.labelmeanfilter(y, x) [source]

statsmodels.sandbox.regression.try_catdata.groupstatsbin statsmodels.sandbox.regression.try_catdata.groupstatsbin(factors, values) [source] uses np.bincount, assumes factors/labels are integers