WLEB {edgeR} | R Documentation |
Estimates the parameters which maximize the given log-likelihood matrix using empirical Bayes method.
WLEB(theta, loglik, prior.n=5, covariate=NULL, trend.method="locfit", mixed.df=FALSE, span=NULL, overall=TRUE, trend=TRUE, individual=TRUE, m0=NULL, m0.out=FALSE)
theta |
numeric vector of values of the parameter at which the log-likelihoods are calculated. |
loglik |
numeric matrix of log-likelihood of all the candidates at those values of parameter. |
prior.n |
numeric scaler, estimate of the prior weight, i.e. the smoothing parameter that indicates the weight to put on the common likelihood compared to the individual's likelihood. |
covariate |
numeric vector of values across which a parameter trend is fitted |
trend.method |
method for estimating the parameter trend. Possible values are |
mixed.df |
logical, only used when |
span |
width of the smoothing window, as a proportion of the data set. |
overall |
logical, should a single value of the parameter which maximizes the sum of all the log-likelihoods be estimated? |
trend |
logical, should a parameter trend (against the covariate) which maximizes the local shared log-likelihoods be estimated? |
individual |
logical, should individual estimates of all the candidates after applying empirical Bayes method along the trend be estimated? |
m0 |
numeric matrix of local shared log-likelihoods. If |
m0.out |
logical, should local shared log-likelihoods be included in the output? |
This function is a generic function that calculates an overall estimate, trend estimates and individual estimates for each candidate given the values of the log-likelihood of all the candidates at some specified parameter values.
A list with the following:
overall |
the parameter estimate that maximizes the sum of all the log-likelihoods. |
trend |
the estimated trended parameters against the covariate. |
individual |
the individual estimates of all the candidates after applying empirical Bayes method along the trend. |
shared.loglik |
the estimated numeric matrix of local shared log-likelihoods |
Yunshun Chen, Gordon Smyth
locfitByCol
, movingAverageByCol
and loessByCol
implement the local fit, moving average or loess smoothers.
y <- matrix(rpois(100, lambda=10), ncol=4) theta <- 7:14 loglik <- matrix(0,nrow=nrow(y),ncol=length(theta)) for(i in 1:nrow(y)) for(j in 1:length(theta)) loglik[i,j] <- sum(dpois(y[i,], theta[j] ,log=TRUE)) covariate <- log(rowSums(y)) out <- WLEB(theta, loglik, prior.n=3, covariate) out