est.shift: Estimate the shift used in the log transformation
In rama: Robust Analysis of MicroArrays

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

View source: R/est.shift.R

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

1	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)

`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.

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 98195-4322

fit.model

1
2
3

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)