LaplacesDemon (version 16.1.4)

as.parm.names: Parameter Names

Description

This function creates a vector of parameter names from a list of parameters, and the list may contain any combination of scalars, vectors, matrices, upper-triangular matrices, and arrays.

Usage

as.parm.names(x, uppertri=NULL)

Arguments

x

This required argument is a list of named parameters. The list may contain scalars, vectors, matrices, and arrays. The value of the named parameters does not matter here, though they are usually set to zero. However, if a missing value occurs, then the associated element is omitted in the output.

uppertri

This optional argument must be a vector with a length equal to the number of named parameters. Each element in uppertri must be either a 0 or 1, where a 1 indicates that an upper triangular matrix will be used for the associated element in the vector of named parameters. Each element of uppertri is associated with a named parameter. The uppertri argument does not function with arrays.

Value

This function returns a vector of parameter names.

Details

Each model function for IterativeQuadrature, LaplaceApproximation, LaplacesDemon, PMC, or VariationalBayes requires a vector of parameters (specified at first as Initial.Values) and a list of data. One component in the list of data must be named parm.names. Each element of parm.names is a name associated with the corresponding parameter in Initial.Values.

The parm.names vector is easy to program explicitly for a simple model, but can require considerably more programming effort for more complicated models. The as.parm.names function is a utility function designed to minimize programming by the user.

For example, a simple model may only require parm.names <- c("alpha", "beta[1]", "beta[2]", "sigma"). A more complicated model may contain hundreds of parameters that are a combination of scalars, vectors, matrices, upper-triangular matrices, and arrays, and is the reason for the as.parm.names function. The code for the above is as.parm.names(list(alpha=0, beta=rep(0,2), sigma=0)).

In the case of an upper-triangular matrix, simply pass the full matrix to as.parm.names and indicate that only the upper-triangular will be used via the uppertri argument. For example, as.parm.names(list(beta=rep(0,J),U=diag(K)), uppertri=c(0,1)) creates parameter names for a vector of $\beta$ parameters of length $J$ and an upper-triangular matrix $\mathbf{\text{U}}$ of dimension $K$.

Numerous examples may be found in the accompanying ``Examples'' vignette.

See Also

IterativeQuadrature LaplaceApproximation, LaplacesDemon, PMC, and VariationalBayes.

Examples

# NOT RUN {
library(LaplacesDemon)
N <- 100
J <- 5
y <- rnorm(N,0,1)
X <- matrix(runif(N*J,-2,2),N,J)
S <- diag(J)
T <- diag(2)
mon.names <- c("LP","sigma")
parm.names <- as.parm.names(list(log.sigma=0, beta=rep(0,J), S=diag(J),
     T=diag(2)), uppertri=c(0,0,0,1))
MyData <- list(J=J, N=N, S=S, T=T, X=X, mon.names=mon.names,
     parm.names=parm.names, y=y)
MyData
# }