loglikeSTGrad {SpatioTemporal} | R Documentation |
Computes finite difference gradients and hessians for the log-likelihood
functions loglikeST
and loglikeSTnaive
.
Uses genGradient
and genHessian
to compute
finite difference derivatives of the log-likelihood function in
loglikeST
and loglikeSTnaive
.
loglikeSTGrad(x, STmodel, type = "p", x.fixed = NULL, h = 0.001, diff.type = 0) loglikeSTHessian(x, STmodel, type = "p", x.fixed = NULL, h = 0.001) loglikeSTnaiveGrad(x, STmodel, type = "p", x.fixed = NULL, h = 0.001, diff.type = 0) loglikeSTnaiveHessian(x, STmodel, type = "p", x.fixed = NULL, h = 0.001)
x |
Point at which to compute the gradient or hessian, see
|
STmodel |
|
type |
A single character indicating the type of log-likelihood to compute. Valid options are "f", "p", and "r", for full, profile or restricted maximum likelihood (REML). |
x.fixed |
Parameters to keep fixed, see |
h, diff.type |
Step length and type of finite difference to use when
computing gradients, see |
Returns the gradient or Hessian for the loglikeST
and loglikeSTnaive
functions.
loglikeSTnaiveGrad
and
loglikeSTnaiveHhessian
may take very long time to run,
use with extreme caution.
Johan Lindstrom
Other likelihood functions: loglikeST
Other numerical derivatives: genGradient
## Not run: ##load the data data(mesa.model) ##Compute dimensions for the data structure dim <- loglikeSTdim(mesa.model) ##Let's create random vectors of values x <- runif(dim$nparam.cov) x.all <- runif(dim$nparam) ##Compute the gradients Gf <- loglikeSTGrad(x.all, mesa.model, "f") Gp <- loglikeSTGrad(x, mesa.model, "p") Gr <- loglikeSTGrad(x, mesa.model, "r") ##And the Hessian, this may take some time... Hf <- loglikeSTHessian(x.all, mesa.model, "f") Hp <- loglikeSTHessian(x, mesa.model, "p") Hr <- loglikeSTHessian(x, mesa.model, "r") ## End(Not run)