bbEstDisp: Simple line-search estimator for dispersion of a beta...

In apeglm: Approximate posterior estimation for GLM coefficients

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). Warning: the standard error estimates are not reliable at the boundary (log of minDisp and maxDisp), and should be interpreted with caution!

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")

apeglm documentation built on Nov. 8, 2020, 5:55 p.m.