# tsum: Summary function for the scale or location component of a... In hett: Heteroscedastic t-Regression

## Description

Summarizes the location or scale components of a heteroscedastic t model

## Usage

 ```1 2 3 4 5 6 7``` ```tsum(object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...) ## S3 method for class '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.

## 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.

## 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`

## Author(s)

Julian Taylor

`summary.tlm`, `tlm`
 ```1 2 3 4 5``` ```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) ```