tsum {hett} | R Documentation |
Summarizes the location or scale components of a heteroscedastic t model
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, ...)
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. |
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.
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 |
Julian Taylor
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)