statsmodels.miscmodels.tmodel.TLinearModel.jac TLinearModel.jac(*args, **kwds) jac is deprecated, use score_obs instead! Use score_obs method. jac will be removed in 0.7. Jacobian/Gradient of log-likelihood evaluated at params for each observation.

statsmodels.sandbox.stats.multicomp.ccols statsmodels.sandbox.stats.multicomp.ccols = array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])

statsmodels.tsa.vector_ar.var_model.VARResults class statsmodels.tsa.vector_ar.var_model.VARResults(endog, endog_lagged, params, sigma_u, lag_order, model=None, trend='c', names=None, dates=None) [source] Estimate VAR(p) process with fixed number of lags Parameters: endog : array endog_lagged : array params : array sigma_u : array lag_order : int model : VAR model instance trend : str {?nc?, ?c?, ?ct?} names : array-like List of names of the endogenous variables in order of appearance in e

statsmodels.iolib.summary2.Summary.as_latex Summary.as_latex() [source] Generate LaTeX Summary Table

statsmodels.sandbox.regression.gmm.GMMResults class statsmodels.sandbox.regression.gmm.GMMResults(*args, **kwds) [source] just a storage class right now Methods calc_cov_params(moms, gradmoms[, weights, ...]) calculate covariance of parameter estimates compare_j(other) overidentification test for comparing two nested gmm estimates conf_int([alpha, cols, method]) Returns the confidence interval of the fitted parameters. cov_params(**kwds) f_test(r_matrix[, cov_p, scale, invcov]) Compute

statsmodels.regression.quantile_regression.QuantRegResults.load classmethod QuantRegResults.load(fname) load a pickle, (class method) Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. Returns: unpickled instance :

Contrasts Overview Link to Notebook GitHub In [1]: from __future__ import print_function import numpy as np import statsmodels.api as sm This document is based heavily on this excellent resource from UCLA http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means. That is, each test statistic for these v

statsmodels.tsa.arima_model.ARIMAResults.arparams static ARIMAResults.arparams()

Tools Our tool collection contains some convenience functions for users and functions that were written mainly for internal use. Additional to this tools directory, several other subpackages have their own tools modules, for example statsmodels.tsa.tsatools Module Reference Basic tools tools These are basic and miscellaneous tools. The full import path is statsmodels.tools.tools. tools.add_constant(data[, prepend, has_constant]) This appends a column of ones to an array if prepend==False.

statsmodels.iolib.table.SimpleTable.as_csv SimpleTable.as_csv(**fmt_dict) [source] Return string, the table in CSV format. Currently only supports comma separator.