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", ...)
A fitted SemiParBIV
/SemiParTRIV
object.
Survey weights.
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).
Binary logical variable. It can be used to calculate the prevalence for a subset of the data.
If TRUE
then the delta method is used for confidence interval calculations, otherwise Bayesian posterior
simulation is employed.
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.
Overall probability of the left and right tails of the prevalence distribution used for interval calculations.
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
.
Title for the plot.
Title for the x axis.
Other graphics parameters to pass on to plotting commands. These are used only when hd.plot = TRUE
.
It returns three values: lower confidence interval limit, estimated prevalence and upper confidence interval limit.
Probability level used.
If delta = FALSE
then it returns a vector containing simulated values of the prevalence. This
is used to calculate an interval.
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.
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.
# NOT RUN {
## see examples for SemiParBIV and SemiParTRIV
# }
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