nlmeNDiff {BayesSenMC} | R Documentation |
Fit a bivariate generalized linear mixed-effects model (GLMM) for non-differential sensitivity and specificity using the glmer
function in lme4
.
Lower and upper bounds for Se and Sp can be specified according to the assumptions of the study.
nlmeNDiff(data, lower = 0.5, upper = 1, id = FALSE, ...)
data |
a data frame containing the 2 by 2 data of the diagnostics table of exposure status for every study in a meta-analysis.
It contains at least 4 columns in the data named as following: |
lower |
an optional argument specifying the lower bound assumption of Se and Sp. Default to 0.5 (or the lowest Se/Sp of all studies, whichever is lower), which provides the mild assumption that Se and Sp are better than chance. |
upper |
an optional argument specifying the upper bound assumption of Se and Sp. Default to 1. |
id |
a TRUE of FALSE argument indicating if the supplied data has a |
... |
optional parameters passed to glmer. |
It returns an object of class merMod.
Besides generic class methods, paramEst()
is implemented in BayesSenMC
to get the parameter estimates used in the Bayesian misclassification model functions.
data(bd_meta)
mod <- nlmeNDiff(bd_meta, lower = 0)