Functions providing access to the Log Likelihood, Gradient, and Hessian
of the collapsed maltipoo model. Note: These are convenience functions
but are not as optimized as direct coding of the MaltipooCollapsed
C++ class due to a lack of Memoization. By contrast function optimMaltipooCollapsed
is much more optimized and massively cuts down on repeated calculations.
A more efficient Rcpp module based implementation of these functions
may following if the future. For model details see optimMaltipooCollapsed
documentation
Usage
loglikMaltipooCollapsed(Y, upsilon, Theta, X, KInv, U, eta, ell, sylv = FALSE)
gradMaltipooCollapsed(Y, upsilon, Theta, X, KInv, U, eta, ell, sylv = FALSE)
hessMaltipooCollapsed(Y, upsilon, Theta, X, KInv, U, eta, ell, sylv = FALSE)Arguments
- Y
D x N matrix of counts
- upsilon
(must be > D)
- Theta
D-1 x Q matrix the prior mean for regression coefficients
- X
Q x N matrix of covariates
- KInv
D-1 x D-1 symmetric positive-definite matrix
- U
a PQxQ matrix of stacked variance components
- eta
matrix (D-1)xN of parameter values at which to calculate quantities
- ell
P-vector of scale factors for each variance component (aka VCScale)
- sylv
(default:false) if true and if N < D-1 will use sylvester determinant identity to speed computation