User-defined covariance structure

User-defined covariance structure. The returned dimensionless covariance shape is intended for use inside vsm, which supplies the single overall variance scale.

Usage

ownm(x, K = NULL, fun = NULL, par = numeric(), fixed = NULL,
  dfun = NULL, par_names = NULL, native_report = NULL)

Arguments

x: Variable or design defining q covariance levels.
K: Optional known positive-definite q by q covariance matrix. If supplied, the structure is fixed.
fun: Optional function fun(par) returning a finite q by q covariance matrix.
par: Starting parameter vector for fun.
fixed: Logical vector of length par indicating fixed parameters.
dfun: Optional derivative function dfun(par, k) returning the derivative of the raw covariance matrix with respect to parameter k.
par_names: Optional names for the parameters.
native_report: Optional function with arguments scale, par, factor, and absorb_scale. It must return a named numeric vector giving the fitted parameters in the model's preferred native scale. This callback is used by covparams_mmes().

Details

There are two modes. With K, ownm creates a fixed covariance factor. With fun, the matrix is evaluated at every optimizer point. The returned matrix is symmetrized and normalized by its first diagonal element so that vsm retains the unique overall variance scale. If dfun is supplied, its derivative is normalized analytically using the quotient rule; otherwise a central numerical derivative of the normalized factor is used. This is the general extension mechanism for covariance structures that do not require a native C++ evaluator. When native_report is omitted, the overall variance and naturally transformed par values are reported.

Value

A list containing the incidence/design matrix in Z and a compiled CovarianceFactor v2 descriptor in covFactor, for use inside vsm.

Examples

if (FALSE) { # \dontrun{
K <- matrix(c(1,.3,.3,1),2,2)
vsm(ownm(group, K=K), ism(id))

cfun <- function(p) {
  r <- tanh(p[1]); matrix(c(1,r,r,1),2,2)
}
native <- function(scale, par, factor, absorb_scale=TRUE) {
  c(variance=scale, correlation=tanh(par[1]))
}
vsm(ownm(group, fun=cfun, par=0, native_report=native), ism(id))
} # }