mcmc.defaultParams_nonLinear {GRENITS} | R Documentation |
Create parameter vector with default parameters for NonLinearNet function
mcmc.defaultParams_nonLinear()
Use this function to generate a template parameter vector to use non-default parameters for the NonLinearNet model.
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) |
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
# 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 ...