GRENITS (version 1.24.0)

mcmc.defaultParams_Linear: Default Parameters for Linear Model

Description

Create parameter vector with default parameters for LinearNet function

Usage

mcmc.defaultParams_Linear()

Value

Returns a single vector with the following elements (in this order):

(1) samples

Number of MCMC iterations to run.

(2) burn.in

Number of initial iterations to discard as burn in.

(3) thin

Subsampling frequency

(4) c

Shape parameter 1 for Beta(c,d) prior on rho (connectivity parameter)

(5) d

Shape parameter 2 for Beta(c,d) prior on rho (connectivity parameter)

(6) sigma.s

Standard deviation parameter for N(0,sigma.s) prior on B (Regression coefficients)

(7) a

Shape parameter for Gamma(a,b) prior on lambda (Regression precision)

(8) b

Rate parameter for Gamma(a,b) prior on lambda (Regression precision)

(9) sigma.mu

Standard deviation parameter for N(0,sigma.mu) prior on mu (Regression intercept)

Details

Use this function to generate a template parameter vector to use non-default parameters for the LinearNet model.

References

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2010. On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics 2010; doi: 10.1093/bioinformatics/btq421

Morrissey, E.R., Juarez, M.A., Denby, K.J. and Burroughs, N.J. 2011 Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression Biostatistics 2011; doi: 10.1093/biostatistics/kxr009

See Also

plotPriors, LinearNet.

Examples

    # Get default parameters
    linearNet.params <- mcmc.defaultParams_Linear()

    # Change run length
    linearNet.params[1] <- 150000

    # Change prior regression precision 
    linearNet.params[7] <- 0.001
    linearNet.params[8] <- 0.001

    # Plot to check changes
    plotPriors(linearNet.params)

    ## Use to run LinearNet ...