mcmc.defaultParams_student {GRENITS} | R Documentation

## Default Parameters for Linear Model with Student distributed replicates

### Description

Create parameter vector with default parameters for ReplicatesNet_student function

### Usage

    mcmc.defaultParams_student()

### Details

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

### 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) 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) a_deg` | Shape parameter for Gamma(a_deg,b_deg) prior on nu (Student degrees of freedom)
`(12) b_deg` | Rate parameter for Gamma(a_deg,b_deg) prior on nu (Student degrees of freedom)
`(13) sigma.mu` | Standard deviation parameter for N(0,sigma.mu) prior on mu (Regression intercept)
`(14) fix.y.iter` | Number of iterations for which sampled data Y is fixed

### 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

### See Also

`plotPriors`, `ReplicatesNet_student`.

### Examples

        # Get default parameters
        linearNet_Student.params <- mcmc.defaultParams_student()

        # Change run length
        linearNet_Student.params[1] <- 200000

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

        # Plot to visualise changes
        plotPriors(linearNet_Student.params)

        ## Use to run ReplicatesNet_student ...

[Package GRENITS version 1.42.0 Index]