bbEstDisp {apeglm} | R Documentation

## Simple line-search estimator for dispersion of a beta binomial

### Description

Uses R's `optimize` function to find the maximum likelihood estimate of dispersion for a beta binomial distribution (`theta` for the `dbetabinom` function in the emdbook package). The counts, size, and beta are matrices, such that each row could be treated as a beta-binomial GLM problem.

### Usage

    bbEstDisp(success, size, weights = 1, x, beta, minDisp, maxDisp,
      se = FALSE)

### Arguments

`success` | the observed successes (a matrix)
`size` | the total trials (a matrix)
`weights` | the weights (1 or a matrix)
`x` | the design matrix, as many rows as columns of `success` and `size`
`beta` | a matrix of MLE coefficients, as many rows as `success` and `size`
`minDisp` | the minimum dispersion value
`maxDisp` | the maximum dispersion value
`se` | logical, whether to return standard error estimate on the log of the dispersion (theta)

### Value

a vector of estimated dispersions (theta). if `se=TRUE` a matrix with columns: the vector of estimated dispersions and the standard errors for the log of the estimated dispersions

### Examples

    library(emdbook)
    n <- 100
    m <- 100
    size <- matrix(rnbinom(n*m, mu=100, size=10),ncol=m)
    success <- matrix(rbetabinom(n*m, prob=.5, size=size, theta=100),ncol=m)
    x <- matrix(rep(1,m),ncol=1)
    beta <- matrix(rep(0,n),ncol=1)
    theta <- bbEstDisp(success=success, size=size, x=x, beta=beta, minDisp=1, maxDisp=500)
    summary(theta)

    # with standard error estimates on log of dispersion
    fit <- bbEstDisp(success=success, size=size, x=x, beta=beta, minDisp=1, maxDisp=500, se=TRUE)
    plot(fit[1:20,"theta"], ylim=c(0,500), ylab="theta-hat")
    log.theta <- log(fit[1:20,"theta"])
    log.theta.se <- fit[1:20,"se"]
    segments(1:20, exp(log.theta - 2 * log.theta.se),
             1:20, exp(log.theta + 2 * log.theta.se))
    abline(h=100,col="red")

[Package apeglm version 1.6.0 Index]