Methods for fitted models

Accessors for gs_fit objects. residuals() returns, by type: "raw" the residuals \(e_t\), "standardized" the innovations \(e_t / \sigma_t\), "pit" their probability integral transform \(F(e_t / \sigma_t)\) under the fitted innovation distribution (uniform under a correct model; the input of copula models and of KS diagnostics). sigma() returns the conditional scale \(\sigma_t\) (not a standard deviation when the innovation variance is infinite), fitted() the conditional location \(x_t - e_t\).

Usage

# S3 method for class 'gs_fit'
coef(object, ...)

# S3 method for class 'gs_fit'
vcov(object, ...)

# S3 method for class 'gs_fit'
logLik(object, ...)

# S3 method for class 'gs_fit'
nobs(object, ...)

# S3 method for class 'gs_fit'
residuals(object, type = c("raw", "standardized", "pit"), ...)

# S3 method for class 'gs_fit'
sigma(object, ...)

# S3 method for class 'gs_fit'
fitted(object, ...)

# S3 method for class 'gs_fit'
print(x, ...)

# S3 method for class 'gs_fit'
summary(object, ...)

Arguments

object, x

A gs_fit object.

...

Unused.

type

Type of residuals.

Value

• coef(): named numeric vector of the estimated parameters.

• vcov(): numeric matrix, the estimated covariance matrix of the parameters (rows and columns in the order of coef()).

• logLik(): an object of class "logLik", the maximized log-likelihood, with attributes df (number of parameters) and nobs.

• nobs(): integer, the number of observations used in the fit.

• residuals(): numeric vector with one value per observation, as described by type.

• sigma(), fitted(): numeric vectors with one value per observation, the conditional scale and the conditional location.

• print(), summary(): print the coefficient table (estimates, standard errors, t and p values), information criteria, persistence and convergence diagnostics, and return x (object) invisibly.