This is the documentation of the main model fitting function of the interface. Within function
bayesx
, three inferential concepts are available for estimation: Markov chain Monte Carlo
simulation (MCMC), estimation based on mixed model technology and restricted maximum likelihood
(REML), and a penalized least squares (respectively penalized likelihood) approach for
estimating models using model selection tools (STEP).
1 2 3 4 5 
formula 
symbolic description of the model (of type 
data 
a 
weights 
prior weights on the data. 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
offset 
can be used to supply a model offset for use in fitting. 
na.action 
a function which indicates what should happen when the data contain 
contrasts 
an optional list. See the 
control 
specify several global control parameters for 
model 
a logical value indicating whether 
chains 
integer. The number of sequential chains that should be run, the default is one
chain if 
cores 
integer. How many cores should be used? The default is one core if

... 
arguments passed to 
In BayesX, estimation of regression parameters is based on three inferential concepts:
Full Bayesian inference via MCMC: A fully Bayesian interpretation of structured additive regression models is obtained by specifying prior distributions for all unknown parameters. Estimation can be facilitated using Markov chain Monte Carlo simulation techniques. BayesX provides numerically efficient implementations of MCMC schemes for structured additive regression models. Suitable proposal densities have been developed to obtain rapidly mixing, wellbehaved sampling schemes without the need for manual tuning.
Inference via a mixed model representation: The other concept used for estimation is based on mixed model methodology. Within BayesX this concept has been extended to structured additive regression models and several types of nonstandard regression situations. The general idea is to take advantage of the close connection between penalty concepts and corresponding random effects distributions. The smoothing parameters of the penalties then transform to variance components in the random effects (mixed) model. While the selection of smoothing parameters has been a difficult task for a long time, several estimation procedures for variance components in mixed models are already available since the 1970's. The most popular one is restricted maximum likelihood in Gaussian mixed models with marginal likelihood as the nonGaussian counterpart. While regression coefficients are estimated based on penalized likelihood, restricted maximum likelihood or marginal likelihood estimation forms the basis for the determination of smoothing parameters. From a Bayesian perspective, this yields empirical Bayes/posterior mode estimates for the structured additive regression models. However, estimates can also merely be interpreted as penalized likelihood estimates from a frequentist perspective.
Penalized likelihood including variable selection: As a third alternative BayesX provides a penalized least squares (respectively penalized likelihood) approach for estimating structured additive regression models. In addition, a powerful variable and model selection tool is included. Model choice and estimation of the parameters is done simultaneously. The algorithms are able to
decide whether a particular covariate enters the model,
decide whether a continuous covariate enters the model linearly or nonlinearly,
decide whether a spatial effect enters the model,
decide whether a unit or cluster specific heterogeneity effect enters the model
select complex interaction effects (two dimensional surfaces, varying coefficient terms)
select the degree of smoothness of nonlinear covariate, spatial or cluster specific heterogeneity effects.
Inference is based on penalized likelihood in combination with fast algorithms for selecting relevant covariates and model terms. Different models are compared via various goodness of fit criteria, e.g. AIC, BIC, GCV and 5 or 10 fold cross validation.
Within the model fitting function bayesx
, the different inferential concepts may be chosen
by argument method
of function bayesx.control
. Options are "MCMC"
,
"REML"
and "STEP"
.
The wrapper function bayesx
basically starts by setting up the necessary BayesX
program file using function bayesx.construct
, parse.bayesx.input
and
write.bayesx.input
. Afterwards the generated program file is send to the
commandline binary executable version of BayesX with run.bayesx
.
As a last step, function read.bayesx.output
will read the estimated model object
returned from BayesX back into R.
For estimation of STAR models, function bayesx
uses formula syntax as provided in package
mgcv
(see formula.gam
), i.e., models may be specified using
the R2BayesX
main model term constructor functions sx
or the
mgcv
constructor functions s
. For a detailed description
of the model formula syntax used within bayesx
models see also
bayesx.construct
and bayesx.term.options
.
After the BayesX binary has successfully finished processing an object of class "bayesx"
is
returned, wherefore a set of standard extractor functions and methods is available, including
methods to the generic functions print
, summary
,
plot
, residuals
and fitted
.
See fitted.bayesx
, plot.bayesx
, and summary.bayesx
for
more details on these methods.
A list of class "bayesx"
, see function read.bayesx.output
.
For geographical effects, note that BayesX may crash if the region identification covariate
is a factor
, it is recommended to code these variables as integer
,
please see the example below.
If a model is specified with a structured and an unstructured spatial effect, e.g. the model
formula is something like y ~ sx(id, bs = "mrf", map = MapBnd) + sx(id, bs = "re")
, the
model output contains of one additional total spatial effect, named with "sx(id):total"
.
Also see the last example.
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
Belitz C, Brezger A, Kneib T, Lang S (2011). BayesX  Software for Bayesian Inference in Structured Additive Regression Models. Version 2.0.1. URL 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.
Brezger A, Kneib T, Lang S (2005). BayesX: Analyzing Bayesian Structured Additive Regression Models. Journal of Statistical Software, 14(11), 1–22. URL http://www.jstatsoft.org/v14/i11/.
Brezger A, Lang S (2006). Generalized Structured Additive Regression Based on Bayesian PSplines. Computational Statistics \& Data Analysis, 50, 947–991.
Fahrmeir L, Kneib T, Lang S (2004). Penalized Structured Additive Regression for Space Time Data: A Bayesian Perspective. Statistica Sinica, 14, 731–761.
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/
parse.bayesx.input
, write.bayesx.input
,
run.bayesx
, read.bayesx.output
,
summary.bayesx
, plot.bayesx
,
fitted.bayesx
, bayesx.construct
, bayesx.term.options
,
sx
, formula.gam
, s
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251  ## generate some data
set.seed(111)
n < 200
## regressor
dat < data.frame(x = runif(n, 3, 3))
## response
dat$y < with(dat, 1.5 + sin(x) + rnorm(n, sd = 0.6))
## estimate models with
## bayesx REML and MCMC
b1 < bayesx(y ~ sx(x), method = "REML", data = dat)
## same using mgcv syntax
b1 < bayesx(y ~ s(x, bs = "ps", k = 20), method = "REML", data = dat)
## now with MCMC
b2 < bayesx(y ~ sx(x), method = "MCMC",
iter = 1200, burnin = 200, data = dat)
## compare reported output
summary(c(b1, b2))
## plot the effect for both models
plot(c(b1, b2), residuals = TRUE)
## use confint
confint(b1, level = 0.99)
confint(b2, level = 0.99)
## Not run:
## more examples
set.seed(111)
n < 500
## regressors
dat < data.frame(x = runif(n, 3, 3), z = runif(n, 3, 3),
w = runif(n, 0, 6), fac = factor(rep(1:10, n/10)))
## response
dat$y < with(dat, 1.5 + sin(x) + cos(z) * sin(w) +
c(2.67, 5, 6, 3, 4, 2, 6, 7, 9, 7.5)[fac] + rnorm(n, sd = 0.6))
## estimate models with
## bayesx MCMC and REML
## and compare with
## mgcv gam()
b1 < bayesx(y ~ sx(x) + sx(z, w, bs = "te") + fac,
data = dat, method = "MCMC")
b2 < bayesx(y ~ sx(x) + sx(z, w, bs = "te") + fac,
data = dat, method = "REML")
b3 < gam(y ~ s(x, bs = "ps") + te(z, w, bs = "ps") + fac,
data = dat)
## summary statistics
summary(b1)
summary(b2)
summary(b3)
## plot the effects
op < par(no.readonly = TRUE)
par(mfrow = c(3, 2))
plot(b1, term = "sx(x)")
plot(b1, term = "sx(z,w)")
plot(b2, term = "sx(x)")
plot(b2, term = "sx(z,w)")
plot(b3, select = 1)
vis.gam(b3, c("z","w"), theta = 40, phi = 40)
par(op)
## combine models b1 and b2
b < c(b1, b2)
## summary
summary(b)
## only plot effect 2 of both models
plot(b, term = "sx(z,w)")
## with residuals
plot(b, term = "sx(z,w)", residuals = TRUE)
## same model with kriging
b < bayesx(y ~ sx(x) + sx(z, w, bs = "kr") + fac,
method = "REML", data = dat)
plot(b)
## now a mrf example
## note: the regional identification
## covariate and the map regionnames
## should be coded as integer
set.seed(333)
## simulate some geographical data
data("MunichBnd")
N < length(MunichBnd); n < N*5
## regressors
dat < data.frame(x1 = runif(n, 3, 3),
id = as.factor(rep(names(MunichBnd), length.out = n)))
dat$sp < with(dat, sort(runif(N, 2, 2), decreasing = TRUE)[id])
## response
dat$y < with(dat, 1.5 + sin(x1) + sp + rnorm(n, sd = 1.2))
## estimate models with
## bayesx MCMC and REML
b1 < bayesx(y ~ sx(x1) + sx(id, bs = "mrf", map = MunichBnd),
method = "MCMC", data = dat)
b2 < bayesx(y ~ sx(x1) + sx(id, bs = "mrf", map = MunichBnd),
method = "REML", data = dat)
## summary statistics
summary(b1)
summary(b2)
## plot the spatial effects
plot(b1, term = "sx(id)", map = MunichBnd,
main = "bayesx() MCMC estimate")
plot(b2, term = "sx(id)", map = MunichBnd,
main = "bayesx() REML estimate")
plotmap(MunichBnd, x = dat$sp, id = dat$id,
main = "Truth")
## try geosplines instead
b < bayesx(y ~ sx(id, bs = "gs", map = MunichBnd) + sx(x1), data = dat)
summary(b)
plot(b, term = "sx(id)", map = MunichBnd)
## geokriging
b < bayesx(y ~ sx(id, bs = "gk", map = MunichBnd) + sx(x1),
method = "REML", data = dat)
summary(b)
plot(b, term = "sx(id)", map = MunichBnd)
## perspective plot of the effect
plot(b, term = "sx(id)")
## image and contour plot
plot(b, term = "sx(id)", image = TRUE,
contour = TRUE, grid = 200)
## model with random effects
set.seed(333)
N < 30
n < N*10
## regressors
dat < data.frame(id = sort(rep(1:N, n/N)), x1 = runif(n, 3, 3))
dat$re < with(dat, rnorm(N, sd = 0.6)[id])
## response
dat$y < with(dat, 1.5 + sin(x1) + re + rnorm(n, sd = 0.6))
## estimate model
b < bayesx(y ~ sx(x1) + sx(id, bs = "re"), data = dat)
summary(b)
plot(b)
## extract estimated random effects
## and compare with true effects
plot(fitted(b, term = "sx(id)")$Mean ~ unique(dat$re))
## now a spatial example
## with structured and
## unstructered spatial
## effect
set.seed(333)
## simulate some geographical data
data("MunichBnd")
N < length(MunichBnd); names(MunichBnd) < 1:N
n < N*5
## regressors
dat < data.frame(id = rep(1:N, n/N), x1 = runif(n, 3, 3))
dat$sp < with(dat, sort(runif(N, 2, 2), decreasing = TRUE)[id])
dat$re < with(dat, rnorm(N, sd = 0.6)[id])
## response
dat$y < with(dat, 1.5 + sin(x1) + sp + re + rnorm(n, sd = 0.6))
## estimate model
b < bayesx(y ~ sx(x1) +
sx(id, bs = "mrf", map = MunichBnd) +
sx(id, bs = "re"), method = "MCMC", data = dat)
summary(b)
## plot all spatial effects
plot(b, term = "sx(id):mrf", map = MunichBnd,
main = "Structured spatial effect")
plot(b, term = "sx(id):re", map = MunichBnd,
main = "Unstructured spatial effect")
plot(b, term = "sx(id):total", map = MunichBnd,
main = "Total spatial effect", digits = 4)
## some experiments with the
## stepwise algorithm
## generate some data
set.seed(321)
n < 1000
## regressors
dat < data.frame(x1 = runif(n, 3, 3), x2 = runif(n),
x3 = runif(n, 3, 6), x4 = runif(n, 0, 1))
## response
dat$y < with(dat, 1.5 + sin(x1) + 0.6 * x2 + rnorm(n, sd = 0.6))
## estimate model with STEP
b < bayesx(y ~ sx(x1) + sx(x2) + sx(x3) + sx(x4),
method = "STEP", algorithm = "cdescent1", CI = "MCMCselect",
iter = 10000, step = 10, data = dat)
summary(b)
plot(b)
## a probit example
set.seed(111)
n < 1000
dat < data.frame(x < runif(n, 3, 3))
dat$z < with(dat, sin(x) + rnorm(n))
dat$y < rep(0, n)
dat$y[dat$z > 0] < 1
b < bayesx(y ~ sx(x), family = "binomialprobit", data = dat)
summary(b)
plot(b)
## estimate varying coefficient models
set.seed(333)
n < 1000
dat < data.frame(x = runif(n, 3, 3), id = factor(rep(1:4, n/4)))
## response
dat$y < with(dat, 1.5 + sin(x) * c(1, 0.2, 1, 5)[id] + rnorm(n, sd = 0.6))
## estimate model
b < bayesx(y ~ sx(x, by = id, center = TRUE),
method = "REML", data = dat)
summary(b)
plot(b, resid = TRUE, cex.resid = 0.1)
## End(Not run)

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