simuDataREM {DIRECT} | R Documentation |
Function simuDataREM
simulates data under the Ornstein-Uhlenbeck (OU) (or Brownian Motion; BM) process-based random-effects mixture (REM) model.
simuDataREM(pars.mtx, dt, T, ntime, nrep, nsize, times, method = c("eigen", "svd", "chol"), model = c("OU", "BM"))
pars.mtx |
A K \times 8 matrix, where K is the number of clusters. Each row contains 8 parameters: standard deviation of within-cluster variability, of variability across time points, and of replicates, respectively; mean and standard deviation for the value at the first time point; the overall mean, standard deviation and mean-reverting rate of the OU process. |
dt |
Increment in times. |
T |
Maximum time. |
ntime |
Number of time points to simulate data for. Needs to be same as the length of vector |
nrep |
Number of replicates. |
nsize |
An integer vector containing sizes of simulated clusters. |
times |
Vector of length |
method |
Method to compute the determinant of the covariance matrix in the calculation of the multivariate normal density. Required. Method choices are: "chol" for Choleski decomposition, "eigen" for eigenvalue decomposition, and "svd" for singular value decomposition. |
model |
Model to generate realizations of the mean vector of a mixture component. Required. Choices are: "OU" for an Ornstein-Uhlenbeck process (a.k.a. the mean-reverting process) and "BM" for a Brown motion (without drift). |
means |
A matrix of |
data |
A matrix of N rows and |
Audrey Q. Fu
Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361.
plotSimulation
for plotting simulated data.
outputData
for writing simulated data and parameter values used in simulation into external files.
DIRECT
for clustering the data.
## Not run: # Simulate replicated time-course gene expression profiles # from OU processes # Simulation parameters times = c(0,5,10,15,20,25,30,35,40,50,60,70,80,90,100,110,120,150) ntime=length (times) nrep=4 nclust = 6 npars = 8 pars.mtx = matrix (0, nrow=nclust, ncol=npars) # late weak upregulation or downregulation pars.mtx[1,] = c(0.05, 0.1, 0.5, 0, 0.16, 0.1, 0.4, 0.05) # repression pars.mtx[2,] = c(0.05, 0.1, 0.5, 1, 0.16, -1.0, 0.1, 0.05) # early strong upregulation pars.mtx[3,] = c(0.05, 0.5, 0.2, 0, 0.16, 2.5, 0.4, 0.15) # strong repression pars.mtx[4,] = c(0.05, 0.5, 0.2, 1, 0.16, -1.5, 0.4, 0.1) # low upregulation pars.mtx[5,] = c(0.05, 0.3, 0.3, -0.5, 0.16, 0.5, 0.2, 0.08) # late strong upregulation pars.mtx[6,] = c(0.05, 0.3, 0.3, -0.5, 0.16, 0.1, 1, 0.1) nsize = rep(40, nclust) # Generate data simudata = simuDataREM (pars=pars.mtx, dt=1, T=150, ntime=ntime, nrep=nrep, nsize=nsize, times=times, method="svd", model="OU") # Display simulated data plotSimulation (simudata, times=times, nsize=nsize, nrep=nrep, lty=1, ylim=c(-4,4), type="l", col="black") # Write simulation parameters and simulated data # to external files outputData (datafilename= "simu_test.dat", parfilename= "simu_test.par", meanfilename= "simu_test_mean.dat", simudata=simudata, pars=pars.mtx, nitem=sum(nsize), ntime=ntime, nrep=nrep) ## End(Not run)