GIV: Generate Initial Values
In LaplacesDemon: Complete Environment for Bayesian Inference

Description Usage Arguments Details Value Author(s) See Also Examples

The GIV function generates initial values for use with the IterativeQuadrature, LaplaceApproximation, LaplacesDemon, PMC, and VariationalBayes functions.

1	GIV(Model, Data, n=1000, PGF=FALSE)

`Model`	This required argument is a model specification function. For more information, see `LaplacesDemon`.
`Data`	This required argument is a list of data. For more information, see `LaplacesDemon`.
`n`	This is the number of attempts to generate acceptable initial values.
`PGF`	Logical. When `TRUE`, a Parameter-Generating Function (PGF) is required to be in `Data`, and `GIV` will generate initial values according to the user-specified PGF. This argument defaults to `FALSE`, in which case initial values are generated randomly without respect to a user-specified function.

Initial values are required for optimization or sampling algorithms. A user may specify initial values, or use the GIV function for random generation. Initial values determined by the user may fail to produce a finite posterior in complicated models, and the GIV function is here to help.

GIV has several uses. First, the IterativeQuadrature, LaplaceApproximation, LaplacesDemon, and VariationalBayes functions use GIV internally if unacceptable initial values are discovered. Second, the user may use GIV when developing their model specification function, Model, to check for potential problems. Third, the user may prefer to randomly generate acceptable initial values. Lastly, GIV is recommended when running multiple or parallel chains with the LaplacesDemon.hpc function (such as for later use with the Gelman.Diagnostic) for dispersed starting locations. For dispersed starting locations, GIV should be run once for each parallel chain, and the results should be stored per row in a matrix of initial values. For more information, see the LaplacesDemon.hpc documentation for initial values.

It is strongly recommended that the user specifies a Parameter-Generating Function (PGF), and includes this function in the list of data. Although the PGF may be specified according to the prior distributions (possibly considered as a Prior-Generating Function), it is often specified with a more restricted range. For example, if a user has a model with the following prior distributions

beta_j ~ N(0, 1000), j=1,…,5

sigma ~ HC(25)

then the PGF, given the prior distributions, is

PGF <- function(Data) return(c(rnormv(5,0,1000),rhalfcauchy(1,25)))

However, the user may not want to begin with initial values that could be so far from zero (as determined by the variance of 1000), and may instead prefer

PGF <- function(Data) return(c(rnormv(5,0,10),rhalfcauchy(1,5)))

When PGF=FALSE, initial values are attempted to be constrained to the interval [-100,100]. This is done to prevent numeric overflows with parameters that are exponentiated within the model specification function. First, GIV passes the upper and lower bounds of this interval to the model, and any changed parameters are noted.

At this point, it is hoped that a non-finite posterior is not found. If found, then the remainder of the process is random and without the previous bounds. This can be particularly problematic in the case of, say, initial values that are the elements of a matrix that must be positive-definite, especially with large matrices. If a random solution is not found, then GIV will fail.

If the posterior is finite and PGF=FALSE, then initial values are randomly generated with a normal proposal and a small variance at the center of the returned range of each parameter. As GIV fails to find acceptable initial values, the algorithm iterates toward its maximum number of iterations, n. In each iteration, the variance increases for the proposal.

Initial values are considered acceptable only when the first two returned components of Model (which are LP and Dev) are finite, and when initial values do not change through constraints, as returned in the fifth component of the list: parm.

If GIV fails to return acceptable initial values, then it is best to study the model specification function. When the model is complicated, here is a suggestion. Remove the log-likelihood, LL, from the equation that calculates the logarithm of the unnormalized joint posterior density, LP. For example, convert LP <- LL + beta.prior to LP <- beta.prior. Now, maximize LP, which is merely the set of prior densities, with any optimization algorithm. Replace LL, and run the model with initial values that are in regions of high prior density (preferably with PGF=TRUE. If this fails, then the model specification should be studied closely, because a non-finite posterior should (especially) never be associated with regions of high prior density.

The GIV function returns a vector equal in length to the number of parameters, and each element is an initial value for the associated parameter in Data$parm.names. When GIV fails to find acceptable initial values, each returned element is NA.

Statisticat, LLC. software@bayesian-inference.com

as.initial.values, Gelman.Diagnostic, IterativeQuadrature, LaplaceApproximation, LaplacesDemon, LaplacesDemon.hpc, PMC, and VariationalBayes.

library(LaplacesDemon)

##############################  Demon Data  ###############################
data(demonsnacks)
y <- log(demonsnacks$Calories)
X <- cbind(1, as.matrix(log(demonsnacks[,c(1,4,10)]+1)))
J <- ncol(X)
for (j in 2:J) X[,j] <- CenterScale(X[,j])

#########################  Data List Preparation  #########################
mon.names <- c("LP","sigma")
parm.names <- as.parm.names(list(beta=rep(0,J), sigma=0))
pos.beta <- grep("beta", parm.names)
pos.sigma <- grep("sigma", parm.names)
PGF <- function(Data) {
     beta <- rnorm(Data$J)
     sigma <- runif(1)
     return(c(beta, sigma))
     }
MyData <- list(J=J, PGF=PGF, X=X, mon.names=mon.names,
     parm.names=parm.names, pos.beta=pos.beta, pos.sigma=pos.sigma, y=y)

##########################  Model Specification  ##########################
Model <- function(parm, Data)
     {
     ### Parameters
     beta <- parm[Data$pos.beta]
     sigma <- interval(parm[Data$pos.sigma], 1e-100, Inf)
     parm[Data$pos.sigma] <- sigma
     ### Log-Prior
     beta.prior <- sum(dnormv(beta, 0, 1000, log=TRUE))
     sigma.prior <- dhalfcauchy(sigma, 25, log=TRUE)
     ### Log-Likelihood
     mu <- tcrossprod(Data$X, t(beta))
     LL <- sum(dnorm(Data$y, mu, sigma, log=TRUE))
     ### Log-Posterior
     LP <- LL + beta.prior + sigma.prior
     Modelout <- list(LP=LP, Dev=-2*LL, Monitor=LP,
          yhat=rnorm(length(mu), mu, sigma), parm=parm)
     return(Modelout)
     }

########################  Generate Initial Values  ########################
Initial.Values <- GIV(Model, MyData, PGF=TRUE)