Docstrings

Abstract supertype for all statistical hypothesis tests. Subtypes must implement pvalue at a minimum and may also implement functions such as confint, nobs, and dof as appropriate.

Abstract supertype for all regression models.

Abstract supertype for all statistical models.

Adjusted pseudo-coefficient of determination (adjusted pseudo R-squared). For nonlinear models, one of the several pseudo R² definitions must be chosen via variant. The only currently supported variants are :MacFadden, defined as and :devianceratio, defined as . In these formulas, is the likelihood of the model, that of the null model (the model including only the intercept), is the deviance of the model, is the deviance of the null model, is the number of observations (given by nobs) and is the number of consumed degrees of freedom of the model (as returned by dof).

Adjusted coefficient of determination (adjusted R-squared).

For linear models, the adjusted R² is defined as , with the coefficient of determination, the number of observations, and the number of coefficients (including the intercept). This definition is generally known as the Wherry Formula I.

Akaike’s Information Criterion, defined as , with the likelihood of the model, and k its number of consumed degrees of freedom (as returned by dof).

Corrected Akaike’s Information Criterion for small sample sizes (Hurvich and Tsai 1989), defined as , with the likelihood of the model, its number of consumed degrees of freedom (as returned by dof), and the number of observations (as returned by nobs).

Bayesian Information Criterion, defined as , with the likelihood of the model, its number of consumed degrees of freedom (as returned by dof), and the number of observations (as returned by nobs).

Return the coefficients of the model.

Return the names of the coefficients.

Return a table with coefficients and related statistics of the model. level determines the level for confidence intervals (by default, 95%).

The returned CoefTable object implements the Tables.jl interface, and can be converted e.g. to a DataFrame via using DataFrames; DataFrame(coeftable(model)).

Compute confidence intervals for coefficients, with confidence level level (by default 95%).

Compute Cook’s distance for each observation in linear model model, giving an estimate of the influence of each data point.

Return X’X where X is the model matrix of model. This function will return a pre-computed matrix stored in model if possible.

Return the deviance of the model relative to a reference, which is usually when applicable the saturated model. It is equal, up to a constant, to , with the likelihood of the model.

Return the number of degrees of freedom consumed in the model, including when applicable the intercept and the distribution’s dispersion parameter.

Return the residual degrees of freedom of the model.

Fit a statistical model.

Fit a statistical model in-place.

Return the fitted values of the model.

Compute the generalized variance inflation factor (GVIF).

If scale=true, then each GVIF is scaled by the degrees of freedom for (number of coefficients associated with) the predictor: .

The GVIF measures the increase in the variance of a (group of) parameter’s estimate in a model with multiple parameters relative to the variance of a parameter’s estimate in a model containing only that parameter. For continuous, numerical predictors, the GVIF is the same as the VIF, but for categorical predictors, the GVIF provides a single number for the entire group of contrast-coded coefficients associated with a categorical predictor.

See also vif.

References

Fox, J., & Monette, G. (1992). Generalized Collinearity Diagnostics. Journal of the American Statistical Association, 87(417), 178. doi:10.2307/2290467

Return the information matrix of the model. By default the Fisher information matrix is returned, while the observed information matrix can be requested with expected = false.

Indicate whether the model has been fitted.

Indicate whether the model is linear.

Return the diagonal of the projection matrix of the model.

Return the model’s linear predictor, Xβ where X is the model matrix and β is the vector of coefficients, or Xβ + offset if the model was fit with an offset.

In-place version of linearpredictor, storing the result in storage.

Return the log-likelihood of the model.

With an observation argument, return the contribution of observation to the log-likelihood of model.

If observation is a Colon, return a vector of each observation’s contribution to the log-likelihood of the model. In other words, this is the vector of the pointwise log-likelihood contributions.

In general, sum(loglikehood(model, :)) == loglikelihood(model).

Return the mean of the response.

Return the model matrix (a.k.a. the design matrix).

Return the model sum of squares.

Return the number of independent observations on which the model was fitted. Be careful when using this information, as the definition of an independent observation may vary depending on the model, on the format used to pass the data, on the sampling plan (if specified), etc.

Return the deviance of the null model, obtained by dropping all independent variables present in model.

If model includes an intercept, the null model is the one with only the intercept; otherwise, it is the one without any predictor (not even the intercept).

Return the log-likelihood of the null model, obtained by dropping all independent variables present in model.

If model includes an intercept, the null model is the one with only the intercept; otherwise, it is the one without any predictor (not even the intercept).

Return the offset used in the model, i.e. the term added to the linear predictor with known coefficient 1, or nothing if the model was not fit with an offset.

Return all parameters of a model.

Form the predicted response of model. An object with new covariate values newX can be supplied, which should have the same type and structure as that used to fit model; e.g. for a GLM it would generally be a DataFrame with the same variable names as the original predictors.

In-place version of predict.

Compute the p-value for a given significance test.

Pseudo-coefficient of determination (pseudo R-squared).

For nonlinear models, one of several pseudo R² definitions must be chosen via variant. Supported variants are:

In the above formulas, is the likelihood of the model, is the likelihood of the null model (the model with only an intercept), is the deviance of the model (from the saturated model), is the deviance of the null model, is the number of observations (given by nobs).

The Cox-Snell and the deviance ratio variants both match the classical definition of R² for linear models.

Coefficient of determination (R-squared).

For a linear model, the R² is defined as , with the explained sum of squares and the total sum of squares.

Reconstruct explanatory variables from model. An object with new response values newX can be supplied, which should have the same type and structure as the output of predict(model).

In-place version of reconstruct.

Return the residuals of the model.

Return the model response (a.k.a. the dependent variable).

Return the name of the model response (a.k.a. the dependent variable).

Return the residual sum of squares of the model.

Return the score of the model, that is the gradient of the log-likelihood with respect to the coefficients.

Return the standard errors for the coefficients of the model.

Return the variance-covariance matrix for the coefficients of the model.

Compute the variance inflation factor (VIF).

The VIF measures the increase in the variance of a parameter’s estimate in a model with multiple parameters relative to the variance of a parameter’s estimate in a model containing only that parameter.

See also gvif.

Return the weights used in the model.