kmbayes {bkmr} | R Documentation |
Fits the Bayesian kernel machine regression (BKMR) model using Markov chain Monte Carlo (MCMC) methods.
kmbayes(y, Z, X = NULL, iter = 1000, family = "gaussian", id = NULL, verbose = TRUE, Znew = NULL, starting.values = NULL, control.params = NULL, varsel = FALSE, groups = NULL, knots = NULL, ztest = NULL, rmethod = "varying", est.h = FALSE)
y |
a vector of outcome data of length |
Z |
an |
X |
an |
iter |
number of iterations to run the sampler |
family |
a description of the error distribution and link function to be used in the model. Currently implemented for |
id |
optional vector (of length |
verbose |
TRUE or FALSE: flag indicating whether to print intermediate diagnostic information during the model fitting. |
Znew |
optional matrix of new predictor values at which to predict |
starting.values |
list of starting values for each parameter. If not specified default values will be chosen. |
control.params |
list of parameters specifying the prior distributions and tuning parameters for the MCMC algorithm. If not specified default values will be chosen. |
varsel |
TRUE or FALSE: indicator for whether to conduct variable selection on the Z variables in |
groups |
optional vector (of length |
knots |
optional matrix of knot locations for implementing the Gaussian predictive process of Banerjee et al (2008). Currently only implemented for models without a random intercept. |
ztest |
optional vector indicating on which variables in Z to conduct variable selection (the remaining variables will be forced into the model). |
rmethod |
for those predictors being forced into the |
est.h |
TRUE or FALSE: indicator for whether to sample from the posterior distribution of the subject-specific effects h_i within the main sampler. This will slow down the model fitting. |
an object of class "bkmrfit", which has the associated methods:
print
(i.e., print.bkmrfit
)
summary
(i.e., summary.bkmrfit
)
Bobb, JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA (2015). Bayesian Kernel Machine Regression for Estimating the Health Effects of Multi-Pollutant Mixtures. Biostatistics 16, no. 3: 493-508.
Banerjee S, Gelfand AE, Finley AO, Sang H (2008). Gaussian predictive process models for large spatial data sets. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(4), 825-848.
For guided examples, go to https://jenfb.github.io/bkmr/overview.html