hyperparameter.update: hyperparameter.update

In microbiome/RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

hyperparameter.update

Description

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

Usage

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

Arguments

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.

Details

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.

Value

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

Author(s)

Leo Lahti [e-mail]

References

See citation("RPA")

See Also

s2.update, rpa.online

Examples

# 
## 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)

microbiome/RPA documentation built on April 9, 2023, 10:59 a.m.