R: Estimated overall prevalence from sample selection model

prev {GJRM}

R Documentation

Estimated overall prevalence from sample selection model

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


prev(x, sw = NULL, type = "joint", 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 `gjrm` 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 `"joint"` (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 giampiero.marra@ucl.ac.uk

References

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.