Given multivariable predictions and prediction (co)variances, calculate contrasts and their (co)variance
get.contr(data, gstat.object, X, ids = names(gstat.object$data))data frame, output of predict
object of class gstat, used to
extract ids; may be missing if ids is used
contrast vector or matrix; the number of variables in
gstat.object should equal the number of elements in X
if X is a vector, or the number of rows in X if X
is a matrix.
character vector with (selection of) id names, present in data
a data frame containing for each row in data the generalized
least squares estimates (named beta.1, beta.2, ...), their
variances (named var.beta.1, var.beta.2, ...) and covariances
(named cov.beta.1.2, cov.beta.1.3, ...)
From data, we can extract the \(n \times 1\) vector with multivariable predictions, say $y$, and its \(n \times n\) covariance matrix $V$. Given a contrast matrix in $X$, this function computes the contrast vector $C=X'y$ and its variance $Var(C)=X'V X$.