Description Usage Arguments Details Value Author(s) See Also Examples
The simulation follows the LA framework, namely dynamic correlation is in the form of X~N(0,1), Y~N(0,1), Z~N(0,1), E(XY|Z) is a function of Z.
1 | la.simu.gen(n, p, n.grp, n.noise.gene, rho, pwr)
|
n |
Sample size (number of columns of the data matrix). |
p |
The number of genes in each LA module, i.e. a group of genes regulated by the same latent dynamic correlation factor. |
n.grp |
The number of LA modules to simulate. |
n.noise.gene |
The number of pure noise genes to add to the matrix. |
rho |
The standard deviation of the Gaussian noise to be added to the simulated data in the modules. |
pwr |
The power for the transformation (see details) |
Between modules, the latent LA factor z's are independent.
Within each module, 10 sub-modules are simulated. For each sub-module, we first generate a pair of X and Y vectors, which follows:
X~N(0,1), Y~N(0,1) u=(pnorm(z)-0.5)*2 E(XY|z)=sign(u)*abs(u)^pwr
Then white noise with SD of rho is added to the hidden X, Y pair to generate pairs of observed X, Y vectors.
A list is returned.
dat |
The data matrix. |
z |
The true z vectors. |
Tianwei Yu <tianwei.yu@emory.edu>
dca()
1 2 3 | x<-la.simu.gen(n=100,p=200,n.grp=3, n.noise.gene=100, rho=0.5, pwr=1)
x$dat[1:5,1:5]
x$z[1:5,]
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