Dear RMA experts,

I recently had a necessity to look into details of the affy package and was
confused on the estimation of alpha parameter for normexp convolution model
in RMA. Namely, alpha is estimated as
1/mode(data > overall_mode). Now,
1. Why mode is used to estimate mean of exponential distribution?
2. for continuous unimodal distribution, wouldn't mode(data > overall_mode)
be the same as overall_mode (disregarding edge effects) ?
What am I missing?
Thank you in advance,
Zufar

Here is affy  R code that estimates RMA model parameters
---
> bg.parameters
function (pm, n.pts = 214)
{
    max.density <- function(x, n.pts) {
        aux <- density(x, kernel = "epanechnikov", n = n.pts,
            na.rm = TRUE)
        aux$x[order(-aux$y)[1]]
    }
    pmbg <- max.density(pm, n.pts)
    bg.data <- pm[pm < pmbg]
    pmbg <- max.density(bg.data, n.pts)
    bg.data <- pm[pm < pmbg]
    bg.data <- bg.data - pmbg
    bgsd <- sqrt(sum(bg.data2)/(length(bg.data) - 1)) * sqrt(2)
    sig.data <- pm[pm > pmbg]
    sig.data <- sig.data - pmbg
    expmean <- max.density(sig.data, n.pts)
    alpha <- 1/expmean
    mubg <- pmbg
    list(alpha = alpha, mu = mubg, sigma = bgsd)
}
<environment: namespace:affy>

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