Description Usage Arguments Examples
Fit a structured GPLVM model with fixed structure covariance
1 2 3 |
Z.normal.prior |
1 2 3 4 5 6 7 8 9 10 11 | Z.true <- cbind(rnorm(50, mean=-1), rnorm(50, mean=-1), rnorm(50, mean=-1))
Z.true <- rbind(Z.true, cbind(rnorm(50, mean=1), rnorm(50, mean=1), rnorm(50, mean=1)))
classes <- rep(1:2, each=50)
pairs(Z.true, col=classes, pty=".")
X <- generate.structured.dataset(100,20,20,num.latent.variables=NULL,5,0.1,nonlinear=TRUE, Z=Z.true)
X$data <- scale(X$data, center=T, scale=F)
X.sd <- sd(as.numeric(X$data))
X$data <- X$data / X.sd
X.noisy <- X$data + rnorm(length(X$data))
Z.init <- prcomp(X.noisy)$x[,1:5]
sgplvm.model <- structured.gplvm.fixedC(X=X$data, nrows.X=20, probabilistic.trace.estimate=F, structured.par=c(2, 2, 1), Z=Z.init, maxit=1000, q=5)
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