summary.aster {aster}

Summarizing Aster Model Fits

Description

These functions are all methods for class aster or summary.aster objects.

Usage

## S3 method for class 'aster'
summary(object, info = c("expected", "observed"),
    info.tol = sqrt(.Machine$double.eps),  show.graph = FALSE, ...)

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

Arguments

object

an object of class "aster", usually, a result of a call to aster.

info

the type of Fisher information use to compute standard errors.

info.tol

tolerance for eigenvalues of Fisher information. If eval is the vector of eigenvalues of the information matrix, then eval < cond.tol * max(eval) are considered zero. Hence the corresponding eigenvectors are directions of constancy or recession of the log likelihood.

show.graph

if TRUE, show the graphical model.

x

an object of class "summary.aster", usually, a result of a call to summary.aster.

digits

the number of significant digits to use when printing.

signif.stars

logical. If TRUE, “significance stars” are printed for each coefficient.

...

further arguments passed to or from other methods.

Value

summary.aster returns an object of class "summary.aster" list with the same components as object, which is of class "aster".

Directions of Recession

This function may give an error message "cannot compute standard errors, apparent directions of recession". There are two reasons why this can happen.

• There may actually be a direction of recession (DOR). Then the maximum likelihood estimate does not exist; increasing the likelihood drives (some of) the coefficients to infinity or minus infinity.

  This function's guess at the DOR can be extracted from the error object obtained by wrapping this function in try and then extracting the dor component of the condition attribute of the error object. An example of this is on the help page for the foobar data set.

  This function's guessed DOR are apparent null eigenvector(s) of the Fisher information matrix. Due to inaccuracy of computer arithmetic, this is only a guess. What are deemed null eigenvectors is controlled by the info.tol argument of this function. Reducing info.tol to perhaps 1e-9 or 1e-10 or even a little lower may make the putative DOR go away. In this case they were probably bogus (see next item). Reducing info.tol to near or below the machine epsilon .Machine$double.eps (.Machine) instructs this function to feed you garbage with no error or warning.

  Putative DOR are probably true DOR if they are highly patterned with many zero or nearly zero components and other components that are nearly (small) integer multiples of each other. Putative DOR are probably bogus if they look like random noise.

  DOR, if true, cannot simply be ignored. For more information, including how to do more rigorous investigation of whether putative DOR are true or bogus, see the example on the help page for the foobar data set and the reference cited on that help page.

• All of the putative directions of recession may be bogus. Due to inaccuracy of computer arithmetic, ill-conditioning of predictor variables, or ill-conditioning of the aster model itself (large graphs cause more inaccurate computation), what appear to be null eigenvectors of the Fisher information matrix need not be true null eigenvectors.

  In this case, the problem will go away when info.tol is decreased slightly. Only when one has proved that there is no DOR, should one use info.tol = 1e-20 which says to ignore the problem altogether (whether putative DOR are true or bogus).

See Also

aster, summary.