inla {INLA} | R Documentation |
inla
performs a
full Bayesian analysis of additive models using Integrated Nested
Laplace approximation
inla( formula, family = "gaussian", contrasts = NULL, data, quantiles=c(0.025, 0.5, 0.975), E = NULL, offset=NULL, scale = NULL, weights = NULL, Ntrials = NULL, strata = NULL, link.covariates = NULL, verbose = FALSE, lincomb = NULL, selection = NULL, lp.scale = NULL, control.compute = list(), control.predictor = list(), control.family = list(), control.inla = list(), control.fixed = list(), control.mode = list(), control.expert = list(), control.hazard = list(), control.lincomb = list(), control.update = list(), control.lp.scale = list(), control.pardiso = list(), only.hyperparam = FALSE, inla.call = inla.getOption("inla.call"), inla.arg = inla.getOption("inla.arg"), num.threads = inla.getOption("num.threads"), blas.num.threads = inla.getOption("blas.num.threads"), keep = inla.getOption("keep"), working.directory = inla.getOption("working.directory"), silent = inla.getOption("silent"), inla.mode = c("classic", "twostage", "experimental"), debug = inla.getOption("debug"), .parent.frame = parent.frame() )
formula |
A |
family |
A string indicating the likelihood
family. The default is |
contrasts |
Optional contrasts for the fixed
effects; see |
data |
A data frame or list containing the variables in the model. The data frame MUST be provided |
quantiles |
A vector of quantiles, p(0), p(1),… to compute for each posterior marginal. The function returns, for each posterior marginal, the values x(0), x(1),… such that Prob(X<x)=p |
E |
Known component in the mean for the Poisson likelihoods defined as E exp(eta) where eta is the linear
predictor. If not provided it is set to |
offset |
This argument is used to specify an
a-priori known and fixed component to be included in
the linear predictor during fitting. This should be
|
scale |
Fixed (optional) scale parameters of the precision for Gaussian and Student-T response models. Default value is rep(1, n.data). |
weights |
Fixed (optional) weights parameters of the likelihood, so the log-likelihood[i] is changed into weights[i]*log-likelihood[i]. Default value is rep(1, n.data). WARNING: The normalizing constant for the likelihood is NOT recomputed, so ALL marginals (and the marginal likelihood) must be interpreted with great care. |
Ntrials |
A vector containing the number of trials for the |
strata |
Fixed (optional) strata indicators for tstrata likelihood model. |
lp.scale |
A vector with same length as the predictor going into the likelihood with
either |
link.covariates |
A vector or matrix with covariates for link functions |
verbose |
Boolean indicating if the |
lincomb |
Used to define linear combination of
nodes in the latent field. The posterior distribution
of such linear combination is computed by the
|
selection |
This is a similar argument to the one in
|
control.compute |
See |
control.predictor |
See
|
control.family |
See |
control.inla |
See |
control.fixed |
See |
control.mode |
See |
control.expert |
See |
control.hazard |
See |
control.lincomb |
See |
control.update |
See |
control.lp.scale |
See |
control.pardiso |
See |
only.hyperparam |
A boolean variable saying if only the hyperparameters should be computed. This option is mainly used internally. (TODO: This option should not be located here, change it!) |
inla.call |
The path to, or the name of, the
|
inla.arg |
A string indicating ALL arguments to the 'inla' program and do not include default arguments. (OOPS: This is an expert option!) |
num.threads |
Maximum number of threads the
|
blas.num.threads |
The absolute value of |
keep |
A boolean variable indicating that the
working files (ini file, data files and results
files) should be kept. If TRUE and no
|
working.directory |
A string giving the name of an non-existing directory where to store the working files. |
silent |
If equal to 1L or TRUE, then the
|
inla.mode |
Run |
debug |
If |
.parent.frame |
Internal use only |
inla
returns an object of class "inla"
. This is a list
containing at least the following arguments:
summary.fixed |
Matrix containing the mean and standard deviation (plus, possibly quantiles and cdf) of the the fixed effects of the model. |
marginals.fixed |
A list containing the posterior marginal densities of the fixed effects of the model. |
summary.random |
List of matrices containing the mean and
standard deviation (plus, possibly quantiles and cdf) of the
the smooth or spatial effects defined through |
marginals.random |
A list containing the
posterior marginal densities of the random effects defined
through |
summary.hyperpar |
A matrix containing the mean and sd (plus, possibly quantiles and cdf) of the hyperparameters of the model |
marginals.hyperpar |
A list containing the posterior marginal densities of the hyperparameters of the model. |
summary.linear.predictor |
A matrix containing the mean and sd (plus, possibly quantiles and cdf) of the linear predictors η in the model |
marginals.linear.predictor |
If |
summary.fitted.values |
A matrix containing the mean and
sd (plus, possibly quantiles and cdf) of the fitted values
g^{-1}(η) obtained by transforming the linear
predictors by the inverse of the link function. This quantity
is only computed if |
marginals.fitted.values |
If |
summary.lincomb |
If |
marginals.lincomb |
If |
selection |
Provide the approximated joint
distribution for the |
dic |
If |
cpo |
If |
po |
If |
waic |
If |
mlik |
If |
neffp |
Expected effective number of parameters in the model. The standard deviation of the expected number of parameters and the number of replicas for parameter are also returned |
mode |
A list of two elements: |
call |
The matched call. |
formula |
The formula supplied |
nhyper |
The number of hyperparameters in the model |
cpu.used |
The cpu time used by the |
Havard Rue hrue@r-inla.org and Sara Martino
Rue, H. and Martino, S. and Chopin, N. (2009) Approximate Bayesian Inference for latent Gaussian models using Integrated Nested Laplace Approximations, JRSS-series B (with discussion), vol 71, no 2, pp 319-392. Rue, H and Held, L. (2005) Gaussian Markov Random Fields - Theory and Applications Chapman and Hall
## Not run: ##See the web page \url{www.r-inla.org} for a series of worked out examples ## End(Not run)