bayesx.control: Control Parameters for BayesX

Description

Various parameters that control fitting of regression models using bayesx.

Usage

bayesx.control(model.name = "bayesx.estim", 
  family = "gaussian", method = "MCMC", verbose = FALSE, 
  dir.rm = TRUE, outfile = NULL, replace = FALSE, iterations = 12000L,
  burnin = 2000L, maxint = NULL, step = 10L, predict = TRUE,
  seed = NULL, hyp.prior = NULL, distopt = NULL, reference = NULL,
  zipdistopt = NULL, begin = NULL, level = NULL, eps = 1e-05,
  lowerlim = 0.001, maxit = 400L, maxchange = 1e+06, leftint = NULL,
  lefttrunc = NULL, state = NULL, algorithm = NULL, criterion = NULL, 
  proportion = NULL, startmodel = NULL, trace = NULL, 
  steps = NULL, CI = NULL, bootstrapsamples = NULL, ...)

Arguments

model.name

character, specify a base name model output files are named with in outfile.

family

character, specify the distribution used for the model, options for all methods, "MCMC", "REML" and "STEP" are: "binomial", "binomialprobit", "gamma", "gaussian"<

method

character, which method should be used for estimation, options are "MCMC", "HMCMC" (hierarchical MCMC), "REML" and "STEP".

verbose

logical, should output be printed to the R console during runtime of bayesx.

dir.rm

logical, should the the output files and directory removed after estimation?

outfile

character, specify a directory where bayesx should store all output files, all output files will be named with model.name as the base name.

replace

if set to TRUE, the files in the output directory specified in argument outfile will be replaced.

iterations

integer, sets the number of iterations for the sampler.

burnin

integer, sets the burn-in period of the sampler.

maxint

integer, if first or second order random walk priors are specified, in some cases the data will be slightly grouped: The range between the minimal and maximal observed covariate values will be divided into (small) intervals, and for each interva

step

integer, defines the thinning parameter for MCMC simulation. E.g., step = 50 means, that only every 50th sampled parameter will be stored and used to compute characteristics of the posterior distribution as means, standard deviatio

predict

logical, option predict may be specified to compute samples of the deviance D, the effective number of parameters pD and the deviance information criterion DIC of the model. In addition, if

seed

integer, set the seed of the random number generator in BayesX, usually set using function set.seed.

hyp.prior

numeric, defines the value of the hyper-parameters a and b for the inverse gamma prior of the overall variance parameter $\sigma^2$, if the response distribution is Gaussian. numeric, must be a positive rea

distopt

character, defines the implemented formulation for the negative binomial model if the response distribution is negative binomial. The two possibilities are to work with a negative binomial likelihood (distopt = "nb") or to work with

reference

character, option reference is meaningful only if either family = "multinomial" or family = "multinomialprobit" is specified as the response distribution. In this case reference defines the

zipdistopt

character, defines the zero inflated distribution for the regression analysis. The two possibilities are to work with a zero infated Poisson distribution (zipdistopt = "zip") or to work with the zero inflated negative binomial likel

begin

character, option begin is meaningful only if family = "cox" is specified as the response distribution. In this case begin specifies the variable that records when the observation became at risk. This option can be used

level

integer, besides the posterior means and medians, BayesX provides point-wise posterior credible intervals for every effect in the model. In a Bayesian approach based on MCMC simulation techniques credible intervals are estimated by co

eps

numeric, defines the termination criterion of the estimation process. If both the relative changes in the regression coefficients and the variance parameters are less than eps, the estimation process is assumed to have converged.

lowerlim

numeric, since small variances are close to the boundary of their parameter space, the usual fisher-scoring algorithm for their determination has to be modified. If the fraction of the penalized part of an effect relative to the total effect is

maxit

integer, defines the maximum number of iterations to be used in estimation. Since the estimation process will not necessarily converge, it may be useful to define an upper bound for the number of iterations. Note, that BayesX returns

maxchange

numeric, defines the maximum value that is allowed for relative changes in parameters in one iteration to prevent the program from crashing because of numerical problems. Note, that BayesX produces results based on the current values

leftint

character, gives the name of the variable that contains the lower (left) boundary $T_{lo}$ of the interval $[T_{lo}, T_{up}]$ for an interval censored observation. for right censored or uncensored observations we have to specify $T_{lo} = T

lefttrunc

character, option lefttrunc specifies the name of the variable containing the left truncation time $T_{tr}$. For observations that are not truncated, we have to specify $T_{tr} = 0$. If lefttrunc is missing, all observa

state

character, for multi-state models, state specifies the current state variable of the process.

algorithm

character, specifies the selection algorithm. Possible values are "cdescent1" (adaptive algorithms in the methodology manual, see subsection 6.3), "cdescent2" (adaptive algorithms 1 and 2 with backfitting, see remarks 1

criterion

character, specifies the goodness of fit criterion. If criterion = "MSEP" is specified the data are randomly divided into a test- and validation data set. The test data set is used to estimate the models and the validation data set

proportion

numeric, this option may be used in combination with option criterion = "MSEP", see above. In this case the data are randomly divided into a training and a validation sample. proportion defines the fraction (between 0 and 1) of the

startmodel

character, defines the start model for variable selection. Options are "linear", "empty", "full" and "userdefined".

trace

character, specifies how detailed the output in the output window will be. Options are "trace_on", "trace_half" and "trace_off".

steps

integer, defines the maximum number of iterations. If the selection process has not converged after steps iterations the algorithm terminates and a warning is raised. Setting steps = 0 allows the user to estimate a cert

character, compute confidence intervals for linear and nonlinear terms. Option CI allows to compute confidence intervals. Options are CI = "none", confidence intervals conditional on the selected model CI = "MCMCs

bootstrapsamples

integer, defines the number of bootstrap samples used for "CI = MCMCbootstrap".

...

not used

Value

A list with the arguments specified is returned.

References

For methodological and reference details see the BayesX manuals available at: http://www.BayesX.org.

Belitz C, Lang S (2008). Simultaneous selection of variables and smoothing parameters in structured additive regression models. Computational Statistics & Data Analysis, 53, 61--81.

Chambers J. M., Hastie T. J. (eds.) (1992). Statistical Models in S. Chapman & Hall, London.

Examples

Run this code

bayesx.control()

set.seed(111)
n <- 500
## regressors
dat <- data.frame(x = runif(n, -3, 3))
## response
dat$y <- with(dat, 10 + sin(x) + rnorm(n, sd = 0.6))

## estimate models with
## bayesx MCMC and REML
b1 <- bayesx(y ~ sx(x), method = "MCMC", data = dat)
b2 <- bayesx(y ~ sx(x), method = "REML", data = dat)

## compare reported output
summary(b1)
summary(b2)