rama (version 1.46.0)

est.shift: Estimate the shift used in the log transformation

Description

Estimate the shift in the log transformation when fitting the Hierarchical model as in bayes.rob.

Usage

est.shift(sample1,sample2,B=1000,min.iter=0,batch=10,mcmc.obj=NULL,dye.swap=FALSE,nb.col1=NULL,all.out=TRUE,verbose=FALSE)

Arguments

sample1

The matrix of intensity from the sample 1. Each row corresponds to a different gene.

sample2

The matrix of intensity from the sample 2. Each row corresponds to a different gene.

B

The number of iteration used the MCMC algorithm.

min.iter

The length of the burn-in period in the MCMC algorithm.min.iter should be less than B.

batch

The thinning value to be used in the MCMC. Only every batch-th iteration will be stored.

mcmc.obj

An object of type mcmc.shift, as returned by est.shift. If no mcmc.obj, the MCMC is initialized to the least squares estimates.

dye.swap

A logical value indicating if the experiment was a dye swap experiment.

nb.col1

An integer value correspinding to the number of arrays (columns) in the first group of the dye swap experiment. In other words, the number of replicates before the dyes have been swaped.

all.out

A logical value indicating if all the parameters should be outputted. If all.out is FALSE, only the posterior mean is outputted. This could be used to save memory.

verbose

A logical value indicating if the current MCMC iteration number should be printed out.

Value

An object of type mcmc.est containing the sampled values from the posterior distribution.

mu

A vector containing the sampled values from mu, the baseline intensity.

alpha2

A vector containing the sampled values from alpha2, the sample effect.

beta2

A vector containing the sampled values from beta2, the dye effect.

delta22

A vector containing the sampled values from delta_22, the dye*sample interaction.

eta

A matrix, each row contains the sampled values from the corresponding array effect.

gamma1

A matrix, each row contains the sampled values from the corresponding gene effect in sample 1.

gamma2

A matrix, each row contains the sampled values from the corresponding gene effect in sample 1.

lambda.gamma1

A vector containing the sampled values for the precision of the gene effect prior in sample 1.

lambda.gamma2

A vector containing the sampled values for the precision of the gene effect prior in sample 2.

rho

A vector containing the sampled values from between sample correlation coefficient rho

lambda_eps1

A vector containing the sampled values from the gene precision in sample 1.

lambda_eps2

A vector containing the sampled values from the gene precision in sample 2.

shift

A vector containing the sampled values from the shift.

Details

The estimation is done by fitting the same model (as in fit.model) with constant variance, Gaussian errors and a prior for the shift. The main purpose of this function is to estimate the shift in the log transformation. Parameter estimation is carried out using Markov Chain Monte Carlo. The shift is estimated with the posterior mean.

References

Robust Estimation of cDNA Microarray Intensities with Replicates Raphael Gottardo, Adrian E. Raftery, Ka Yee Yeung, and Roger Bumgarner Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322

See Also

fit.model

Examples

data(hiv)
### Initialize the proposals
mcmc.hiv<-est.shift(hiv[1:10,c(1:4)],hiv[1:10,c(5:8)],B=2000,min.iter=000,batch=1,mcmc.obj=NULL,dye.swap=TRUE,nb.col1=2)