# 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]) Add a column of ones to an array.

The next group are mostly helper functions that are not separately tested or insufficiently tested.

 tools.clean0(matrix) Erase columns of zeros: can save some time in pseudoinverse. tools.fullrank(x[, r]) Return an array whose column span is the same as x. True if (Q, P) contrast c is estimable for (N, P) design d. Reciprocal of an array with entries less than or equal to 0 set to 0. Reciprocal of an array with entries less than 0 set to 0. tools.unsqueeze(data, axis, oldshape) Unsqueeze a collapsed array.

### Numerical Differentiation¶

 numdiff.approx_fprime(x, f[, epsilon, args, …]) Gradient of function, or Jacobian if function f returns 1d array numdiff.approx_fprime_cs(x, f[, epsilon, …]) Calculate gradient or Jacobian with complex step derivative approximation numdiff.approx_hess1(x, f[, epsilon, args, …]) Calculate Hessian with finite difference derivative approximation numdiff.approx_hess2(x, f[, epsilon, args, …]) Calculate Hessian with finite difference derivative approximation numdiff.approx_hess3(x, f[, epsilon, args, …]) Calculate Hessian with finite difference derivative approximation numdiff.approx_hess_cs(x, f[, epsilon, …]) Calculate Hessian with complex-step derivative approximation

### Measure for fit performance eval_measures¶

The first group of function in this module are standalone versions of information criteria, aic bic and hqic. The function with _sigma suffix take the error sum of squares as argument, those without, take the value of the log-likelihood, llf, as argument.

The second group of function are measures of fit or prediction performance, which are mostly one liners to be used as helper functions. All of those calculate a performance or distance statistic for the difference between two arrays. For example in the case of Monte Carlo or cross-validation, the first array would be the estimation results for the different replications or draws, while the second array would be the true or observed values.

 eval_measures.aic(llf, nobs, df_modelwc) Akaike information criterion eval_measures.aic_sigma(sigma2, nobs, df_modelwc) Akaike information criterion eval_measures.aicc(llf, nobs, df_modelwc) Akaike information criterion (AIC) with small sample correction eval_measures.aicc_sigma(sigma2, nobs, …) Akaike information criterion (AIC) with small sample correction eval_measures.bic(llf, nobs, df_modelwc) Bayesian information criterion (BIC) or Schwarz criterion eval_measures.bic_sigma(sigma2, nobs, df_modelwc) Bayesian information criterion (BIC) or Schwarz criterion eval_measures.hqic(llf, nobs, df_modelwc) Hannan-Quinn information criterion (HQC) eval_measures.hqic_sigma(sigma2, nobs, …) Hannan-Quinn information criterion (HQC) eval_measures.bias(x1, x2[, axis]) bias, mean error eval_measures.iqr(x1, x2[, axis]) Interquartile range of error eval_measures.maxabs(x1, x2[, axis]) maximum absolute error eval_measures.meanabs(x1, x2[, axis]) mean absolute error eval_measures.medianabs(x1, x2[, axis]) median absolute error eval_measures.medianbias(x1, x2[, axis]) median bias, median error eval_measures.mse(x1, x2[, axis]) mean squared error eval_measures.rmse(x1, x2[, axis]) root mean squared error eval_measures.rmspe(y, y_hat[, axis, zeros]) Root Mean Squared Percentage Error eval_measures.stde(x1, x2[, ddof, axis]) standard deviation of error eval_measures.vare(x1, x2[, ddof, axis]) variance of error