summary: summary

In ppstat: Point Process Statistics

Description

Computes and formats a summary of a fitted point process model constructed with e.g. pointProcessModel or pointProcessSmooth.

Usage

## S4 method for signature 'PointProcessModel'
summary(object, ...)
## S4 method for signature 'PointProcessSmooth'
summary(object, ...)
## S4 method for signature 'PointProcessKernel'
summary(object, ...)

## S3 method for class 'summary.glppm'
print(x, digits = max(3, getOption("digits") - 3), 
    signif.stars = getOption("show.signif.stars"), ...)

Arguments

object	an object of class "PointProcessModel"
x	a list of class "summary.glppm", usually the result of a call of summary on a "PointProcessModel" object.
digits	the number of significant digits to use when printing.
signif.stars	logical. If TRUE, 'significance stars' are printed for each coefficient.
...	additional arguments to the call.

Details

In the current implementation the degrees of freedom, df, is simply equal to the number of parameters. Future extensions will offer the possibility of L2 and L1 regularized fits and a corresponding computation of the effective degrees of freedom useful for model selection that differs from the actual number of parameters.

Value

The S4 method summary returns a list of S3-class "summary.glppm". It contains the entries

df	degrees of freedom or effective degrees of freedom.
call	the call used to generate the PointProcessModel object.
mll	the value of the minus-log-likelihood function in the estimated parameters.
iter	the number of function and gradient evaluations used in the numerical optimization.
aic	the AIC model selection criteria defined in this case as 2*(mll + df).
coefficients	a px4 matrix with the columns being the estimated parameters, estimated standard errors, z statistics and corresponding two-sided p-values.

Author(s)

Niels Richard Hansen, [e-mail]

See Also

PointProcessModel

ppstat documentation built on May 2, 2019, 5:26 p.m.