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

## Description

`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.

## Usage

 ```1 2 3 4``` ```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", ...) ```

## Arguments

 `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`.

## Details

`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.

## Value

 `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.

## Author(s)

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

Maintainer: Giampiero Marra [email protected]

## References

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`

## Examples

 `1` ```## see examples for SemiParBIV and SemiParTRIV ```

JRM documentation built on July 13, 2017, 5:03 p.m.