Description Usage Details Value References See Also Examples
Create parameter vector with default parameters for ReplicatesNet_gauss function
1 |
Use this function to generate a template parameter vector to use non-default parameters for the ReplicatesNet_gauss 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) 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) a_exp |
Shape parameter for Gamma(a_exp,b_exp) prior on tau (Replicates precision) |
(10) b_exp |
Rate parameter for Gamma(a_exp,b_exp) prior on tau (Replicates precision) |
(11) sigma.mu |
Standard deviation parameter for N(0,sigma.mu) prior on mu (Regression intercept) |
(12) fix.y.iter |
Number of iterations for which sampled data Y is fixed |
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
plotPriors
, ReplicatesNet_gauss
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # Get default parameters
linearNet_Gauss.params <- mcmc.defaultParams_gauss()
# Change run length
linearNet_Gauss.params[1] <- 200000
# Change prior regression precision
linearNet_Gauss.params[7] <- 0.001
linearNet_Gauss.params[8] <- 0.001
# Plot to visualise changes
plotPriors(linearNet_Gauss.params)
## Use to run ReplicatesNet_gauss ...
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