statsmodels.graphics.correlation.plot_corr statsmodels.graphics.correlation.plot_corr(dcorr, xnames=None, ynames=None, title=None, normcolor=False, ax=None, cmap='RdYlBu_r') [source] Plot correlation of many variables in a tight color grid. Parameters: dcorr : ndarray Correlation matrix, square 2-D array. xnames : list of str, optional Labels for the horizontal axis. If not given (None), then the matplotlib defaults (integers) are used. If it is an empty list, [], then no ticks and label

statsmodels.graphics.boxplots.violinplot statsmodels.graphics.boxplots.violinplot(data, ax=None, labels=None, positions=None, side='both', show_boxplot=True, plot_opts={}) [source] Make a violin plot of each dataset in the data sequence. A violin plot is a boxplot combined with a kernel density estimate of the probability density function per point. Parameters: data : sequence of ndarrays Data arrays, one array per value in positions. ax : Matplotlib AxesSubplot instance, optional If giv

statsmodels.graphics.correlation.plot_corr_grid statsmodels.graphics.correlation.plot_corr_grid(dcorrs, titles=None, ncols=None, normcolor=False, xnames=None, ynames=None, fig=None, cmap='RdYlBu_r') [source] Create a grid of correlation plots. The individual correlation plots are assumed to all have the same variables, axis labels can be specified only once. Parameters: dcorrs : list or iterable of ndarrays List of correlation matrices. titles : list of str, optional List of titles for t

Graphics Goodness of Fit Plots gofplots.qqplot(data[, dist, distargs, a, ...]) Q-Q plot of the quantiles of x versus the quantiles/ppf of a distribution. gofplots.qqline(ax, line[, x, y, dist, fmt]) Plot a reference line for a qqplot. gofplots.qqplot_2samples(data1, data2[, ...]) Q-Q Plot of two samples? quantiles. gofplots.ProbPlot(data[, dist, fit, ...]) Class for convenient construction of Q-Q, P-P, and probability plots. Boxplots boxplots.violinplot(data[, ax, labels, ...]) Make a v

statsmodels.graphics.boxplots.beanplot statsmodels.graphics.boxplots.beanplot(data, ax=None, labels=None, positions=None, side='both', jitter=False, plot_opts={}) [source] Make a bean plot of each dataset in the data sequence. A bean plot is a combination of a violinplot (kernel density estimate of the probability density function per point) with a line-scatter plot of all individual data points. Parameters: data : sequence of ndarrays Data arrays, one array per value in positions. ax : M

statsmodels.stats.power.GofChisquarePower.solve_power GofChisquarePower.solve_power(effect_size=None, nobs=None, alpha=None, power=None, n_bins=2) [source] solve for any one parameter of the power of a one sample chisquare-test for the one sample chisquare-test the keywords are: effect_size, nobs, alpha, power Exactly one needs to be None, all others need numeric values. n_bins needs to be defined, a default=2 is used. Parameters: effect_size : float standardized effect size, according to

statsmodels.stats.power.GofChisquarePower.plot_power GofChisquarePower.plot_power(dep_var='nobs', nobs=None, effect_size=None, alpha=0.05, ax=None, title=None, plt_kwds=None, **kwds) plot power with number of observations or effect size on x-axis Parameters: dep_var : string in [?nobs?, ?effect_size?, ?alpha?] This specifies which variable is used for the horizontal axis. If dep_var=?nobs? (default), then one curve is created for each value of effect_size. If dep_var=?effect_size? or alpha

statsmodels.stats.power.GofChisquarePower.power GofChisquarePower.power(effect_size, nobs, alpha, n_bins, ddof=0) [source] Calculate the power of a chisquare test for one sample Only two-sided alternative is implemented Parameters: effect_size : float standardized effect size, according to Cohen?s definition. see statsmodels.stats.gof.chisquare_effectsize nobs : int or float sample size, number of observations. alpha : float in interval (0,1) significance level, e.g. 0.05, is the proba

statsmodels.sandbox.regression.gmm.GMMResults.wald_test GMMResults.wald_test(r_matrix, cov_p=None, scale=1.0, invcov=None, use_f=None) Compute a Wald-test for a joint linear hypothesis. Parameters: r_matrix : array-like, str, or tuple array : An r x k array where r is the number of restrictions to test and k is the number of regressors. It is assumed that the linear combination is equal to zero. str : The full hypotheses to test can be given as a string. See the examples. tuple : A tuple o

statsmodels.sandbox.regression.gmm.GMMResults.summary GMMResults.summary(yname=None, xname=None, title=None, alpha=0.05) [source] Summarize the Regression Results Parameters: yname : string, optional Default is y xname : list of strings, optional Default is var_## for ## in p the number of regressors title : string, optional Title for the top table. If not None, then this replaces the default title alpha : float significance level for the confidence intervals Returns: smry : Summa