R: Create Samples for BAMLSS by Multivariate Normal...

sam_MVNORM {bamlss} | R Documentation

## Create Samples for BAMLSS by Multivariate Normal Approximation

### Description

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.

### Usage

    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, ...)

### Arguments

`x` | The `x` list, as returned from function `bamlss.frame`, holding all model matrices and other information that is used for fitting the model. Or an object returned from function `bamlss`.
`y` | The model response, as returned from function `bamlss.frame`.
`family` | A bamlss family object, see `family.bamlss`.
`start` | A named numeric vector containing possible starting values, the names are based on function `parameters`.
`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 `parameters`. If `hessian = NULL` the function uses `optim` to compute the Hessian if it is not provided within `x`.
`...` | Arguments passed to function `optim`.

### Value

Function `MVNORM()` returns samples of parameters. The samples are provided as a `mcmc` matrix.

### See Also

`bamlss`, `bamlss.frame`, `bamlss.engine.setup`, `set.starting.values`, `opt_bfit`, `sam_GMCMC`

### Examples

    ## 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)

[Package bamlss version 1.1-6 Index]