summary.asreml – Summarize an asreml object

summary.asreml is a summary method for objects inheriting from class asreml.

## S3 method for class 'asreml'
summary(object, param = c("sigma", "gamma"), coef = FALSE,
vparameters = FALSE, ...)

`object`	An `asreml` object.
`param`	If “`sigma`” (the default), random parameter values are reported on the sigma scale only, otherwise, if “`gamma`“, an additional column of variance ratios is returned.
`coef`	If `TRUE` (default is `FALSE`), the coefficients and their standard errors are included in the return object.
`vparameters`	If `TRUE` (default is `FALSE`), the variance parameters are included in the return object in list form.
`...`	Additional arguments.

A list of class `summary.asreml` with the following components:
`call`	The `call` component from `object`
`loglik`	The `loglik` component from `object`
`nedf`	The `nedf` component from `object`.
`sigma`	`sqrt(object\$sigma2).`
`varcomp`	A dataframe summarising the random parameter vector `(object$vparameters)`. Variance component ratios are included if `param="gamma"`, and a measure of precision is included along with boundary constraints at termination and the percentage change in the final iteration.
`aic`	Akaike information criterion.
`bic`	Bayesian information criterion.
`distribution`	A character string identifying the error distribution(s) if `object$deviance != 0`.
`link`	A character string identifying the link function(s) if `object$deviance != 0`.
`deviance`	The deviance from the fit if `object$deviance != 0`.
`heterogeneity`	Variance heterogeneity (deviance/nedf) if `object$deviance != 0`.
`coef.fixed`	A matrix of coefficients and their standard errors for fixed effects if `coef=TRUE`.
`coef.random`	A matrix of coefficients and their standard errors for random effects if `coef=TRUE`.
`coef.sparse`	A matrix of coefficients and their standard errors for sparse-fixed effects if `coef=TRUE`.
`vparameters`	A list of variance structures with matrices converted to full dense form. For `ante` and `chol` models the components are given in `varcomp` and returned in `gammas` in variance-covariance form.

Updated on June 22, 2021