fit.model {rama}  R Documentation 
Estimate the log transformed intensities of each sample of a replicated microarray experiment. The estimation is done via Hiearchical Bayesian Modeling.
fit.model(sample1,sample2,B=1000,min.iter=0,batch=10,shift=NULL,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, as returned by fit.model . mcmc.obj is used to initialized the MCMC.
If no mcmc.obj , the MCMC is initialized to the least squares estimates. 
shift 
The shift to be used in the log transformation. If
shift=NULL is specified (default), it is estimated using est.shift 
dye.swap 
A logical value indicating if the experiment was a dye swap experiment. 
nb.col1 
An integer value corresponding 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 function fits a hierarchical Bayesian model for robust estimation of cDNA microarray intensities. Our model addresses classical issues such as design effects, normalization and transformation. Outliers are modeled explicitly using a tdistribution. Parameter estimation is carried out using Markov Chain Monte Carlo.
An object of type mcmc
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 matrix, each row contains the sampled values from the corresponding gene precision in sample 1. 
lambda_eps2 
A matrix, each row contains the sampled values from the corresponding gene precision in sample 2. 
a.eps 
A vector containing the sampled values for the mean of the prior of the genes precision. 
b.eps 
A vector containing the sampled values for the variance of the prior of the genes precision. 
w 
A matrix, each element (i,j) correspond to the posterior mean of the sampled weights of replicate j in gene i.To save memory, we only store the posterior means of the weigths. 
shift 
The value of 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
est.shift
data(hiv) mcmc.hiv<fit.model(hiv[1:10,c(1:4)],hiv[1:10,c(5:8)],B=2000,min.iter=000,batch=1,shift=30,mcmc.obj=NULL,dye.swap=TRUE,nb.col1=2)