Last chance! 50% off unlimited learning
Sale ends in
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)
"none"
, "movingave"
and "loess"
.trend.method="locfit"
. If FALSE
, locfit
uses a polynomial of degree 0. If TRUE
, locfit
uses a polynomial of degree 1 for rows with small covariate values. Care is taken to smooth the curve.Null
, it will be calculated using the method selected by trend.method
.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
Run the code above in your browser using DataLab