bamlss: randomize – R documentation

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randomize

Transform Smooth Constructs to Random Effects

Description

The transformer function takes a bamlss.frame object and transforms all smooth.constructs into a random effects representation. Note that this is only possible for smooth terms with a single smoothing variance. The function is based on function smooth2random.

Usage

randomize(x)

Arguments

`x`	Object returned from function `bamlss.frame`.

Details

The decomposition is achieved by a spectral decomposition of the penalty and design matrix by finding a basis of the null space of the penalty matrix. This feature is used, e.g., for the JAGS sampler function. For more details see also jagam.

Value

A transformed bamlss.frame. To each smooth.construct model term an element named "Xf", the fixed effects design matrix, and an element "rand$Xr", the random effects design matrix, is added. In addition, for re-transforming parameters elements "trans.U" and "trans.D" are supplied. See also function smooth2random.

References

Fahrmeir L, Kneib T, Lang S, Marx B (2013). Regression - Models, Methods and Applications. Springer-Verlag, Berlin. ISBN 978-3-642-34332-2.

Wood S.N. (2006). Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC.

Examples

## Simulate data.
d <- GAMart()

## Create a "bamlss.frame".
bf <- bamlss.frame(num ~ s(x1) + s(x2) + s(x3) + s(lon,lat), data = d)

## Structure of the "s(x1)" smooth.construct.
str(bf$x$mu$smooth.construct[["s(x1)"]])

## Transform.
bf <- randomize(bf)

## New structure adding fixed
## and random effect matrices.
str(bf$x$mu$smooth.construct[["s(x1)"]])

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25