Confidence intervals for ratios of proportions based on the quasibinomial assumption
Confidence intervals for ratios of proportions based on the quasibinomial assumption
Confidence intervals for ratios of proportions with overdispersed binomial data in a one-factor quasibinomial generalized linear model. Intervals are computed using the MOVER-R method on profile deviance intervals (as implemented in mcprofile) for the single proportions.
QBmover(succ, fail, trt, conf.level =0.95, alternative ="two.sided", grid =NULL)
Arguments
succ: vector of counts of successes
fail: vector of counts of failures
trt: factor variable distinguishing the treatment groups
conf.level: a single numeric value, the confidence level
alternative: a character string, "two.sided" for two-sided intervals, "less" for upper limits, "greater" for lower limits only
grid: optional, a numeric vector to be supplied to the profiling used internally in quasibin.ratio to obtain profile deviance intervals for each samples proportion on the logit-scale.
Returns
A data.frame with three columns - est: estimated ratios
lower: lower confidence limits
upper: upper confidence limits
References
Donner and Zou (2012): Closed-form confidence intervals for functions of the normal mean and standard deviation. Statistical Methods in Medical Research 21(4):347-359. Gerhard (2014): Simultaneous Small Sample Inference For Linear Combinations Of Generalized Linear Model Parameters. Communications in Statistics - Simulation and Computation. DOI:10.1080/03610918.2014.895836
Author(s)
Frank Schaarschmidt
Note
Experimental
Examples
QBmover(succ=c(0,0,1,0,6,8), fail=c(20,20,18,20,14,12), trt=factor(rep(c("A","B"), c(3,3))), conf.level =0.95, alternative ="two.sided", grid =NULL)