Extract native-scale covariance parameters from an mmes fit

Returns covariance parameters in the native, model-facing scale defined by each CovarianceFactor descriptor, rather than the unconstrained working coordinates or normalized variance ratios used internally by the optimizer.

Usage

covparams_mmes(object, term = NULL)

Arguments

object: A fitted object of class "mmes".
term: Optional covariance-term name or numeric index. By default, all random and residual covariance terms are returned.

Details

Every CovarianceFactor descriptor carries a native reporting callback. Built-in structures use it to report meaningful quantities such as level-specific variances for dsm(), variances and covariances for usm(), and variance-correlation parameters for correlation structures. Structural zeros and internal coordinates such as log standard deviations, normalized Cholesky coefficients, and variance ratios are not returned where the covariance model provides a more direct native representation.

All built-in CovarianceFactor families provide native reporters, including identity, diagonal, unstructured, AR(1–3), compound-symmetry, moving-average, general-correlation, factor-analytic, antedependence, reduced-rank, Matern, Toeplitz, SAR, and CAR structures. Heterogeneous AR and compound-symmetry models report their dependence parameter together with level-specific variances rather than an overall variance plus variance ratios.

Both fixed and estimated covariance parameters are included. For an arbitrary Kronecker product, the product-level variance scale is absorbed by the first covariance-shaping factor. Subsequent factors are necessarily reported on their normalized relative scale because the product has only one identifiable overall scale.

User-defined ownm() structures can supply a native_report callback. If none is supplied, their overall variance and naturally transformed parameter values are returned.

Use covparams_mmes_se() when native-scale delta-method standard errors are also required.

Value

A data frame with columns term, factor, section, parameter, and estimate. section identifies the dsumm section a parameter belongs to and is NA otherwise.

Examples

if (FALSE) { # \dontrun{
fit <- mmes(Yield ~ Env,
            random = ~ vsm(dsm(Env), ism(Name)),
            rcov = ~ units, data = DT_example)
covparams_mmes(fit)
fit$covParNative
} # }