est.shift {rama}  R Documentation 
Estimate the shift in the log transformation when fitting the Hierarchical model as in bayes.rob.
est.shift(sample1,sample2,B=1000,min.iter=0,batch=10,mcmc.obj=NULL,dye.swap=FALSE,nb.col1=NULL,all.out=TRUE)
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 burnin 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. 
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.
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. 
Raphael Gottardo
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 981954322
fit.model
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)