prev can be used to calculate the overall estimated prevalence from a sample selection model
with binay outcome, with corresponding interval
obtained using posterior simulation.
prev(x, sw = NULL, joint = TRUE, n.sim = 100, prob.lev = 0.05)
It returns three values: lower confidence interval limit, estimated prevalence and upper confidence interval limit.
Probability level used.
Vector containing simulated values of the prevalence. This is used to calculate an interval.
A fitted gjrm object.
Survey weights.
If FALSE then the prevalence is obtained from the univariate model
which neglects the presence of unobserved confounders. When TRUE, the prevalence is obtained from
the simultaneous model which accounts for observed and unobserved confounders.
Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. It may be increased if more precision is required.
Overall probability of the left and right tails of the prevalence distribution used for interval calculations.
Authors: Giampiero Marra, Rosalba Radice, Guy Harling, Mark E McGovern
Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk
prev estimates the overall prevalence of a disease (e.g., HIV) when there are missing values that are not at random.
An interval for the estimated prevalence can be obtained using posterior simulation.
Marra G., Radice R., Barnighausen T., Wood S.N. and McGovern M.E. (2017), A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses. Journal of the American Statistical Association, 112(518), 484-496.
GJRM-package, gjrm