GRENITS (version 1.24.0)

mcmc.defaultParams_nonLinear: Default Parameters for non-Linear Model

Description

Create parameter vector with default parameters for NonLinearNet function

Usage

mcmc.defaultParams_nonLinear()

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) trunc

Truncation parameter for InvertedPareto prior on tau (smoothness parameter)

(7) tau0

Precision parameter for N(0, tau0^(-0.5)) prior on B (first two coefficients)

(8) M

Numer of knots used for each spline function

(9) a

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

(10) b

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

(11) sigma.mu

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

(12) a_pareto

Pareto parameter for InvertedPareto prior on tau (smoothness parameter)

Details

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

References

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, NonLinearNet.

Examples

    # Get default parameters
    nonLinearNet.params <- mcmc.defaultParams_nonLinear()

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

    # Change prior on smoothness parameter
    nonLinearNet.params[6] <- 30000 # Change truncation 
    nonLinearNet.params[12] <- 3 # Concentrate more mass close to linear region

    # Plot to check changes
    plotPriors(nonLinearNet.params)

    ## Use to run LinearNet ...