sam_MVNORM | R Documentation |
This sampler function for BAMLSS uses estimated parameters
and the Hessian
information to create samples from a multivariate normal distribution. Note that smoothing
variance uncertainty is not accounted for, therefore, the resulting credible intervals
are most likely too narrow.
sam_MVNORM(x, y = NULL, family = NULL, start = NULL, n.samples = 500, hessian = NULL, ...) MVNORM(x, y = NULL, family = NULL, start = NULL, n.samples = 500, hessian = NULL, ...)
x |
The |
y |
The model response, as returned from function |
family |
A bamlss family object, see |
start |
A named numeric vector containing possible starting values, the names are based on
function |
n.samples |
Sets the number of samples that should be generated. |
hessian |
The Hessian matrix that should be used. Note that the row and column names
must be the same as the names of the |
... |
Arguments passed to function |
Function MVNORM()
returns samples of parameters. The samples are provided as a
mcmc
matrix.
bamlss
, bamlss.frame
,
bamlss.engine.setup
, set.starting.values
, opt_bfit
,
sam_GMCMC
## Simulated data example illustrating ## how to call the sampler function. ## This is done internally within ## the setup of function bamlss(). d <- GAMart() f <- num ~ s(x1, bs = "ps") bf <- bamlss.frame(f, data = d, family = "gaussian") ## First, find starting values with optimizer. o <- with(bf, opt_bfit(x, y, family)) ## Sample. samps <- with(bf, sam_MVNORM(x, y, family, start = o$parameters)) plot(samps)
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