Description Usage Arguments Value Author(s) References See Also Examples
View source: R/bayesx.control.R
Various parameters that control fitting of regression models
using bayesx
.
1 2 3 4 5 6 7 8 9 10  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 = 1e05,
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, ...)

model.name 
character, specify a base name model output files are named
with in 
family 
character, specify the distribution used for the model, options
for all methods, 
method 
character, which method should be used for estimation, options
are 
verbose 
logical, should output be printed to the 
dir.rm 
logical, should the the 
outfile 
character, specify a directory where 
replace 
if set to 
iterations 
integer, sets the number of iterations for the sampler. 
burnin 
integer, sets the burnin 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 interval
one parameter will be estimated. The grouping has almost no effect on estimation results as long
as the number of intervals is large enough. With the 
step 
integer, defines the thinning parameter for MCMC simulation.
E.g., 
predict 
logical, option 
seed 
integer, set the seed of the random number generator in
BayesX, usually set using function 
hyp.prior 
numeric, defines the value of the hyperparameters 
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 ( 
reference 
character, option 
zipdistopt 
character, defines the zero inflated distribution for the
regression analysis. The two possibilities are to work with a zero infated Poisson distribution
( 
begin 
character, option 
level 
integer, besides the posterior means and medians, BayesX
provides pointwise posterior credible intervals for every effect in the model. In a Bayesian
approach based on MCMC simulation techniques credible intervals are estimated by computing the
respective quantiles of the sampled effects. By default, BayesX computes (pointwise)
credible intervals for nominal levels of 80\% and 95\%. The option 
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 
lowerlim 
numeric, since small variances are close to the boundary of
their parameter space, the usual fisherscoring algorithm for their determination has to be
modified. If the fraction of the penalized part of an effect relative to the total effect is
less than 
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 results
based on the current values of all parameters even if no convergence could be achieved within

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 of all parameters even if the estimation procedure is stopped due to numerical problems, but an error message will be printed in the output window. 
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_{up} . If leftint is missing, all observations are assumed to be right censored or uncensored, depending on the corresponding value of the censoring indicator. 
lefttrunc 
character, option 
state 
character, for multistate models, 
algorithm 
character, specifies the selection algorithm. Possible values
are 
criterion 
character, specifies the goodness of fit criterion. If

proportion 
numeric, this option may be used in combination with option

startmodel 
character, defines the start model for variable selection.
Options are 
trace 
character, specifies how detailed the output in the output window
will be. Options are 
steps 
integer, defines the maximum number of iterations. If the
selection process has not converged after 
CI 
character, compute confidence intervals for linear and nonlinear
terms. Option 
bootstrapsamples 
integer, defines the number of bootstrap samples used
for 
... 
not used 
A list with the arguments specified is returned.
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
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 JM, Hastie TJ (eds.) (1992). Statistical Models in S. Chapman \& Hall, London.
Umlauf N, Adler D, Kneib T, Lang S, Zeileis A (2015). Structured Additive Regression Models: An R Interface to BayesX. Journal of Statistical Software, 63(21), 1–46. http://www.jstatsoft.org/v63/i21/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  bayesx.control()
## Not run:
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)
## End(Not run)

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