Statsmodels 0.5 is a large and very exciting release that brings together a year of work done by 38 authors, including over 2000 commits. It contains many new features and a large amount of bug fixes detailed below.
See the list of fixed issues for specific closed issues.
The following major new features appear in this version.
Statsmodels now supports fitting models with a formula. This functionality is provided by patsy. Patsy is now a dependency for statsmodels. Models can be individually imported from the statsmodels.formula.api namespace or you can import them all as:
import statsmodels.formula.api as smf
Alternatively, each model in the usual statsmodels.api namespace has a from_formula classmethod that will create a model using a formula. Formulas are also available for specifying linear hypothesis tests using the t_test and f_test methods after model fitting. A typical workflow can now look something like this.
import numpy as np import pandas as pd import statsmodels.formula.api as smf url = 'http://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv' data = pd.read_csv(url) # Fit regression model (using the natural log of one of the regressors) results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=data).fit()
Empirical Likelihood-Based Inference for moments of univariate and multivariate variables is available as well as EL-based ANOVA tests. EL-based linear regression, including the regression through the origin model. In addition, the accelerated failure time model for inference on a linear regression model with a randomly right censored endogenous variable is available.
Support for ANOVA is now available including type I, II, and III sums of squares. See ANOVA.
Kernel density estimation has been extended to handle multivariate estimation as well via product kernels. It is available as sm.nonparametric.KDEMultivariate. It supports least squares and maximum likelihood cross-validation for bandwidth estimation, as well as mixed continuous, ordered, and unordered categorical data. Conditional density estimation is also available via sm.nonparametric.KDEMUltivariateConditional.
Kernel regression models are now available via sm.nonparametric.KernelReg. It is based on the product kernel mentioned above, so it also has the same set of features including support for cross-validation as well as support for estimation mixed continuous and categorical variables. Censored kernel regression is also provided by kernel_regression.KernelCensoredReg.
Quantile regression is supported via the sm.QuantReg class. Kernel and bandwidth selection options are available for estimating the asymptotic covariance matrix using a kernel density estimator.
It is now possible to fit negative binomial models for count data via maximum-likelihood using the sm.NegativeBinomial class. NB1, NB2, and geometric variance specifications are available.
A new optimization method has been added to the discrete models, which includes Logit, Probit, MNLogit and Poisson, that makes it possible to estimate the models with an l1, linear, penalization. This shrinks parameters towards zero and can set parameters that are not very different from zero to zero. This is especially useful if there are a large number of explanatory variables and a large associated number of parameters. CVXOPT is now an optional dependency that can be used for fitting these models.
import numpy as np import statsmodels.api as sm x = np.random.normal(loc=1.12, scale=0.25, size=37) y = np.random.normal(loc=0.75, scale=0.45, size=37) ppx = sm.ProbPlot(x) ppy = sm.ProbPlot(y) fig1 = ppx.qqplot() fig2 = ppx.qqplot(other=ppy)
The power module (statsmodel.stats.power) currently implements power and sample size calculations for the t-tests (sm.stats.TTestPower, sm.stats.TTestIndPower), normal based test (sm.stats.NormIndPower), F-tests (sm.stats.FTestPower, :class:sm.stats.FTestAnovaPower <FTestAnovaPower>) and Chisquare goodness of fit (sm.stats.GofChisquarePower) test. The implementation is class based, but the module also provides three shortcut functions, sm.stats.tt_solve_power, sm.stats.tt_ind_solve_power and sm.stats.zt_ind_solve_power to solve for any one of the parameters of the power equations. See this blog post for a more in-depth description of the additions.
IPython notebook examples: Many of our examples have been converted or added as IPython notebooks now. They are available here.
Improved marginal effects for discrete choice models: Expanded options for obtaining marginal effects after the estimation of nonlinear discrete choice models are available. See get_margeff.
OLS influence outlier measures: After the estimation of a model with OLS, the common set of influence and outlier measures and a outlier test are now available attached as methods get_influnce and outlier_test to the Results instance. See OLSInfluence and outlier_test.
New datasets: New datasets are available for examples.
Access to R datasets: We now have access to many of the same datasets available to R users through the Rdatasets project. You can access these using the sm.datasets.get_rdataset function. This function also includes caching of these datasets.
Improved numerical differentiation tools: Numerical differentiation routines have been greatly improved and expanded to cover all the routines discussed in:
Ridout, M.S. (2009) Statistical applications of the complex-step method of numerical differentiation. The American Statistician, 63, 66-74
See the sm.tools.numdiff module.
Consistent constant handling across models: Result statistics no longer rely on the assumption that a constant is present in the model.
Missing value handling across models: Users can now control what models do in the presence of missing values via the missing keyword available in the instantiation of every model. The options are 'none', 'drop', and 'raise'. The default is 'none', which does no missing value checks. To drop missing values use 'drop'. And 'raise' will raise an error in the presence of any missing data.
The previous version (statsmodels 0.4.3) was released on July 2, 2012. Since then we have closed a total of 380 issues, 172 pull requests and 208 regular issues. The detailed list can be viewed.
This release is a result of the work of the following 38 authors who contributed total of 2032 commits. If for any reason, we’ve failed to list your name in the below, please contact us:
Obtained by running git log v0.4.3..HEAD --format='* %aN <%aE>' | sed 's/@/\-at\-/' | sed 's/<>//' | sort -u.