hett (version 0.3-3)

tsum: Summary function for the scale or location component of a heteroscedastic t model

Description

Summarizes the location or scale components of a heteroscedastic t model

Usage

tsum(object, dispersion = NULL, correlation = FALSE,
             symbolic.cor = FALSE, ...)
# S3 method for tsum
print(x, digits = max(3, getOption("digits") - 3), symbolic.cor =
      x$symbolic.cor, signif.stars = getOption("show.signif.stars"),
      scale = TRUE, ...)

Arguments

object

either the location or scale object created by fitting a heteroscedastic t object with tlm

x

an object of class "tsum"

dispersion

1 if summarizing the location model; 2 if summarizing the scale model (see Details)

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

digits

the number of significant digits to be printed.

symbolic.cor

logical. If TRUE, print the correlations in a symbolic form (see `symnum') rather than as numbers.

signif.stars

logical. if TRUE, "significance stars" are printed for each coefficient.

scale

logical. If TRUE then the dispersion is known in advance (2), and is printed accordingly.

…

further arguments passed to or from other methods.

Value

tsum returns an object of class "tsum", a list with components

call

the component from object

df.residual

the component from object

coefficients

the matrix of coefficients, standard errors, z-values and p-values

dispersion

the supplied dispersion argument

df

a 2-vector of the rank of the model and the number of residual degrees of freedom

cov.unscaled

the unscaled (dispersion = 1) estimated covariance matrix of the estimated coefficients

cov.scaled

ditto, scaled by dispersion

correlation

(only if correlation is true.) The estimated correlations of the estimated coefficients

symbolic.cor

(only if correlation is true.) The value of the argument symbolic.cor

Details

The argument supplied to dispersion must be either 1 (location model) or 2 (scale model). The reason for this is because the fitting of the model has already scaled the covariance matrix for the location coefficients. Hence the scaled and unscaled versions of covariance matrix for the location model are identical.

This function will not be generally called by the user as it will only summarize the location or scale model but not both. Instead the user should refer to summary.tlm to print a summary of both models.

See Also

summary.tlm, tlm

Examples

# NOT RUN {
data(mm, package = "hett")
attach(mm)
tfit <- tlm(m.marietta ~ CRSP, ~ CRSP, data = mm, start = list(dof = 3),
estDof = TRUE) 
tsum(tfit$loc.fit, dispersion = 1) 
# }