hyperparameter.update: hyperparameter.update
In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

Update hyperparameters Update shape (alpha) and scale (beta) parameters of the inverse gamma distribution.

1	hyperparameter.update(dat, alpha, beta, th = 0.01)

`dat`	A probes x samples matrix (probeset).
`alpha`	Shape parameter of inverse gamma density for the probe variances.
`beta`	Scale parameter of inverse gamma density for the probe variances.
`th`	Convergence threshold.

Shape update: alpha <- alpha + T/2; Scale update: beta <- alpha * s2 where s2 is the updated variance for each probe (the mode of variances is given by beta/alpha). The variances (s2) are updated by EM type algorithm, see s2.update.

A list with elements alpha, beta (corresponding to the shape and scale parameters of inverse gamma distribution, respectively).

Leo Lahti leo.lahti@iki.fi

See citation("RPA")

s2.update, rpa.online

# 
## Generate and fit toydata, learn hyperparameters
#set.seed(11122)
#P <- 11   # number of probes
#N <- 5000 # number of arrays
#real <- sample.probeset(P = P, n = N, shape = 3, scale = 1, mu.real = 4)
#dat <- real$dat # probes x samples#
#
## Set priors
#alpha <- 1e-2
#beta  <- rep(1e-2, P)
## Operate in batches
#step <- 1000
#for (ni in seq(1, N, step)) {
#  batch <- ni:(ni+step-1)  
#  hp <- hyperparameter.update(dat[,batch], alpha, beta, th = 1e-2)
#  alpha <- hp$alpha
#  beta <- hp$beta
#}
## Final variance estimate
#s2 <- beta/alpha
#
## Compare real and estimated variances
#plot(sqrt(real$tau2), sqrt(s2), main = cor(sqrt(real$tau2), sqrt(s2))); abline(0,1)