iterateBMAlm {networkBMA} | R Documentation |
An iterative version of Bayesian Model Averaging (BMA) for linear models with many variables. Incorporates prior probabilities for inclusion of variables in models.
iterateBMAlm( x, y, prior.prob = NULL, control = iBMAcontrolLM(), verbose = FALSE)
x |
A matrix of real-valued predictor variables. Rows correspond to observations and columns to variables. |
y |
A real-valued response vector. |
prior.prob |
An optional vector of prior probabilities for each predictor variable belonging to a linear model for the data. If not specified, predictor variables are assumed to have equal prior probability. |
control |
A list of values controling the underlying algorithm.
The default is given by |
verbose |
A logical variable indicating whether or not the details of the
method's progress shoul be printed during computation. The default
value is |
iterateBMAlm
is intended for datasets that have more variables
(e.g. gene expression values) than observations (e.g. subjects).
There is currently no mechanism for handling factor variables in
iterateBMAlm
, as there is in the underlying function
bicreg
in the BMA
package. However factors can be encoded
by users and included with other variables as input.
A list with the following components, similar to the output of function
bicreg
in the BMA
package:
bic |
values of BIC for the models |
postprob |
the posterior probabilities of the models selected |
priorprob |
the prior probabilities of the variables in the models |
namesx |
the names of the variables |
label |
labels identifying the models selected |
r2 |
R2 values for the models |
size |
the number of independent variables in each of the models |
which |
a logical matrix with one row per model and one column per variable indicating whether that variable is in the model |
probne0 |
the posterior probability that each variable is non-zero (in percent) |
postmean |
the posterior mean of each coefficient (from model averaging) |
condpostmean |
the posterior mean of each coefficient conditional on the variable being included in the model |
condpostsd |
the posterior standard deviation of each coefficient conditional on the variable being included in the model |
ols |
matrix with one row per model and one column per variable giving the OLS estimate of each coefficient for each model |
mle |
the same as |
n.models |
the number of models |
n.vars |
the number of variables |
K. Y. Yeung, R. E. Bumgarner and A. E. Raftery (2005), Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data, Bioinformatics 21(10) 2394-2402.
K. Lo, A. E. Raftery, K. M. Dombek, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung (2012), Integrating External Biological Knowledge in the Construction of Regulatory Networks from Time-series Expression Data, BMC Systems Biology, 6:101.
K. Y. Yeung, K. M. Dombek, K. Lo, J. E. Mittler, J. Zhu, E. E. Schadt, R. E. Bumgarner and A. E. Raftery (2011), Construction of regulatory networks using expression time-series data of a genotyped population, Proceedings of the National Academy of Sciences, 108(48):19436-41.
data(dream4) network <- 1 Time <- as.numeric(dream4ts100[[network]]$time) xIndex <- which(Time != max(Time)) yIndex <- which(Time != min(Time)) gene <- "G1" x <- dream4ts100[[network]][xIndex,-(1:2)] y <- dream4ts100[[network]][yIndex,gene] nvar <- 50 ord <- varord( x, y, ordering = "bic1")[1:nvar] result <- iterateBMAlm( x = x[,ord], y = y)