SemiParBIVProbit (version 3.6-1)

hiv: HIV Zambian data

Description

HIV Zambian data by region, together with polygons describing the regions' shapes.

Usage

data(hiv)
data(hiv.polys)

source

The data have been produced using the procedure described in:

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.

Details

The data frame hiv relates to the regions whose boundaries are coded in hiv.polys. hiv.polys[[i]] is a 2 column matrix, containing the vertices of the polygons defining the boundary of the ith region. names(hiv.polys) matches hiv$region (order unimportant).

Examples

#########################################################
#########################################################

library("SemiParBIVProbit")

data("hiv", package = "SemiParBIVProbit")        
data("hiv.polys", package = "SemiParBIVProbit")  

#########################################################
#########################################################
## The stuff below is useful if the user wishes to employ  
## a Markov Random Field (MRF) smoother. It provides
## the instruction to set up polygons automatically
## and the dataset variable needed to fit a model with 
## MRF.
#########################################################
#########################################################
#
# ## hiv.polys was already created and
# ## made available via the call 
# ## data("hiv.polys", package = "SemiParBIVProbit") 
# ## hiv.polys was created using the code below
#
# obj <- readRDS("ZMB_adm1.rds") 
# ## RDS Zambian Level 1 file obtained from 
# ## http://www.gadm.org. 
#
# pol <- polys.setup(obj)
#
# hiv.polys <- pol$polys   
# name <- cbind(names(hiv.polys), pol$names1)
# name
#
## last step was to create a factor variable with range
## range(name[,1]) where the numerical values were linked   
## to the regions in name[,2]. This is what was done in 
## the hiv dataset; see hiv$region. Specifically,
## the procedure used was
##
# reg <- NULL
# 
# for(i in 1:dim(hiv)[1]){
# 
# if(hiv$region[i] == "Central")       reg[i] <- 1
# if(hiv$region[i] == "Copperbelt")    reg[i] <- 2
# if(hiv$region[i] == "Eastern")       reg[i] <- 3
# if(hiv$region[i] == "Luapula")       reg[i] <- 4
# if(hiv$region[i] == "Lusaka")        reg[i] <- 5
# if(hiv$region[i] == "North-Western") reg[i] <- 6
# if(hiv$region[i] == "Northern")      reg[i] <- 7
# if(hiv$region[i] == "Southern")      reg[i] <- 8
# if(hiv$region[i] == "Western")       reg[i] <- 9
# 
# }
# 
# hiv$region <- as.factor(reg)
# 
# 
#########################################################
#########################################################

xt <- list(polys = hiv.polys) 

# neighbourhood structure info for MRF  
# to use in model specification

#########################################################
# Bivariate brobit model with non-random sample selection
#########################################################          
            
sel.eq <- hivconsent ~ s(age) + s(education) + 
                       s(region, bs = "mrf", xt = xt) + marital + std + 
                       highhiv + condom + aidscare + knowsdiedofaids + 
                       evertestedHIV + smoke + et + language + 
                       s(interviewerID, bs = "re") 
 
out.eq <- hiv ~ s(age) + s(education) + s(region, bs = "mrf", xt = xt) +  
                marital + std + highhiv + condom + aidscare + 
                knowsdiedofaids + evertestedHIV + smoke  + et +  
                language     
                       
theta.eq <- ~ s(region, bs = "mrf", xt = xt)                        
  

bss <- SemiParBIVProbit(list(sel.eq, out.eq, theta.eq), 
                        data = hiv, BivD = "J90", Model = "BSS")

conv.check(bss)

set.seed(1)
summary(bss)

par(mfrow = c(2, 3), mar=c(4, 4.7, 3.1, 2), 
    cex.lab = 1.6, cex.axis = 1.6) 
for (i in 1:3) plot(bss, eq = 1, seWithMean = TRUE, scheme = 1,   
                    scale = 0, select = i, jit = TRUE)
for (i in 1:3) plot(bss, eq = 2, seWithMean = TRUE, scheme = 1,
                    scale = 0, select = i, jit = TRUE)

#dev.copy(postscript, "smoothHIV.eps", height=7)
#dev.off()  

prev(bss, sw = hiv$sw, type = "naive") 

set.seed(1)
prev(bss, sw = hiv$sw, type = "univariate") 

prev(bss, sw = hiv$sw) 


lr <- length(hiv.polys) 
prevBYreg  <- matrix(NA, lr, 2)
thetaBYreg <- NA

for(i in 1:lr) {
prevBYreg[i,1] <- prev(bss, sw = hiv$sw, ind = hiv$region==i, 
                       type = "univariate")$res[2]
prevBYreg[i,2] <- prev(bss, sw = hiv$sw, ind = hiv$region==i)$res[2]
thetaBYreg[i]  <- bss$theta[hiv$region==i][1]
}


zlim <- range(prevBYreg)  # to establish a common prevalence range

par(mfrow = c(1, 3), cex.axis = 1.3)
polys.map(hiv.polys, prevBYreg[,1], zlim = zlim, lab = "",  
          cex.lab = 1.5, cex.main = 1.5, 
          main = "HIV - Imputation Model")
polys.map(hiv.polys, prevBYreg[,2], zlim = zlim, cex.main = 1.5, 
          main = "HIV - Selection Model")
polys.map(hiv.polys, thetaBYreg, rev.col = FALSE, cex.main = 1.7, 
          main = expression(paste("Copula parameter (",hat(theta),")")))

#dev.copy(postscript, "PrevMaps.eps", height=3.5)
#dev.off() 

set.seed(1)
CItheta <- summary(bss)$CItheta
CItheta[1,]

#