prev: Estimated overall prevalence from sample selection model
In JRM: Joint Regression Modelling

Description Usage Arguments Details Value Author(s) References See Also Examples

prev can be used to calculate the overall estimated prevalence from a sample selection model with binay outcome, with corresponding interval obtained using the delta method or posterior simulation.

prev(x, sw = NULL, type = "simultaneous", ind = NULL, delta = FALSE,  
     n.sim = 100, prob.lev = 0.05, hd.plot = FALSE, 
     main = "Histogram and Kernel Density of Simulated Prevalences", 
     xlab = "Simulated Prevalences", ...)

`x`	A fitted `SemiParBIV`/`SemiParTRIV` object.
`sw`	Survey weights.
`type`	This argument can take three values: `"naive"` (the prevalence is calculated ignoring the presence of observed and unobserved confounders), `"univariate"` (the prevalence is obtained from the univariate probit/single imputation model which neglects the presence of unobserved confounders) and `"simultaneous"` (the prevalence is obtained from the bivariate/trivariate model which accounts for observed and unobserved confounders).
`ind`	Binary logical variable. It can be used to calculate the prevalence for a subset of the data.
`delta`	If `TRUE` then the delta method is used for confidence interval calculations, otherwise Bayesian posterior simulation is employed.
`n.sim`	Number of simulated coefficient vectors from the posterior distribution of the estimated model parameters. This is used when `delta = FALSE`. It may be increased if more precision is required.
`prob.lev`	Overall probability of the left and right tails of the prevalence distribution used for interval calculations.
`hd.plot`	If `TRUE` then a plot of the histogram and kernel density estimate of the simulated prevalences is produced. This can only be produced when `delta = FALSE`.
`main`	Title for the plot.
`xlab`	Title for the x axis.
`...`	Other graphics parameters to pass on to plotting commands. These are used only when `hd.plot = TRUE`.

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 the delta method or posterior simulation.

`res`	It returns three values: lower confidence interval limit, estimated prevalence and upper confidence interval limit.
`prob.lev`	Probability level used.
`sim.prev`	If `delta = FALSE` then it returns a vector containing simulated values of the prevalence. This is used to calculate an interval.

Authors: Giampiero Marra, Rosalba Radice, Guy Harling, Mark E McGovern

Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk

McGovern M.E., Barnighausen T., Marra G. and Radice R. (2015), On the Assumption of Joint Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology, 26(2), 229-237.

Marra G., Radice R., Barnighausen T., Wood S.N. and McGovern M.E. (in press), A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses. Journal of the American Statistical Association.

JRM-package, SemiParBIV, SemiParTRIV