bcov {hglmm} | R Documentation |
Creates an estimate of the covariance matrix of the parameter estimates for a hidden generalized linear Markov model via parametric bootstrapping.
bcov(object, nsim = 500, itmax = 500, verbose = FALSE, data.name = NULL)
object |
An object of class |
nsim |
The number of data sets to simulate, from which
to estimate parameters. From each data set a vector of parameters
is estimated; the estimated covariance matrix is the empirical
covariance matrix of these |
itmax |
The maximum number of iterations to be used in attempting to achieve convergence when fitting models to the simulated data sets. Note that if convergence is not achieved, the simulated data set being used is discarded (i.e. it “doesn't count”) and a replacement data set is simulated. |
verbose |
Logical scalar. Should a progress report be printed out at each step of the fitting procedure? (Probably not, if the fitted procedure is to be undertaken 500 times.) |
data.name |
An identifying name to be included in the returned
value. Defaults to |
Although this documentation refers to “generalized linear models”, currently only log linear Poisson models are provided for. More flexibility may be added at a future date.
A list with components:
C.hat |
The parametric bootstrap estimate of the covariance matrix of the parameter estimates. |
nc.count |
A count of the total number of times that the algorithm failed to converge during the bootstrapping procedure. |
data.name |
An identifying name, perhaps specifying the
data set to which the model was fitted so as to produce
|
Rolf Turner r.turner@auckland.ac.nz
See the help for hglmm()
for references.
fitted.hglmm()
rhglmm()
rhglmm.default()
rhglmm.hglmm()
## Not run: loc4 <- c("LngRf","BondiE","BondiOff","MlbrOff") SCC4 <- SydColCount[SydColCount$locn %in% loc4,] SCC4$locn <- factor(SCC4$locn) # Get rid of unused levels. rownames(SCC4) <- 1:nrow(SCC4) fit <- hglmm(y~locn+depth,SCC4,c("locn","depth"),K=2, contr="sum",verb=TRUE) cvr <- bcov(fit,nsim=500) ## End(Not run)