iterateBMAlm: Iterative BMA for linear modeling with prior variable...
In networkBMA: Regression-based network inference using Bayesian Model Averaging

Description Usage Arguments Details Value References See Also Examples

An iterative version of Bayesian Model Averaging (BMA) for linear models with many variables. Incorporates prior probabilities for inclusion of variables in models.

1 2	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 `iBMAcontrolLM()`.
`verbose`	A logical variable indicating whether or not the details of the method's progress shoul be printed during computation. The default value is `FALSE`.

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 `ols`
`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.

iBMAcontrolLM, varord, bicreg

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)