SemiParBIVProbit (version 3.7-1)

hiv: HIV Zambian data

Description

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

Usage

data(hiv)
data(hiv.polys)

Format

hiv is a 6416 row data frame with the following columns

hivconsent

binary variable indicating consent to test for HIV.

hiv

binary variable indicating whether an individual is HIV positive.

age

age in years.

education

years of education.

region

code identifying region, and matching names(hiv.polys). It can take nine possible values: 1 central, 2 copperbelt, 3 eastern, 4 luapula, 5 lusaka, 6 northwestern, 7 northern, 8 southern, 9 western.

marital

never married, currently married, formerly married.

std

had a sexually transmitted disease.

highhiv

had high risk sex.

condom

used condom during last intercourse.

aidscare

equal to 1 if would care for an HIV-infected relative.

knowsdiedofaids

equal to 1 if know someone who died of HIV.

evertestedHIV

equal to 1 if previously tested for HIV.

smoke

smoker.

ethnicity

bemba, lunda (luapula), lala, ushi, lamba, tonga, luvale, lunda (northwestern), mbunda, kaonde, lozi, chewa, nsenga, ngoni, mambwe, namwanga, tumbuka, other.

language

English, Bemba, Lozi, Nyanja, Tonga, other.

interviewerID

interviewer identifier.

sw

survey weights.

hiv.polys contains the polygons defining the areas in the format described below.

Source

The data have been produced as 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

## Not run:  
# 
# #########################################################
# #########################################################
# 
# 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 instructions 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(wealth) + 
#                        s(region, bs = "mrf", xt = xt, k = 7) + 
#                        marital + std + age1sex_cat + highhiv + 
#                        partner + condom + aidscare + 
#                        knowsdiedofaids + evertestedHIV + 
#                        smoke + religion + ethnicity + 
#                        language + s(interviewerID, bs = "re")
#  
# out.eq <- hiv        ~ s(age) + s(education) + s(wealth) + 
#                        s(region, bs = "mrf", xt = xt, k = 7) + 
#                        marital + std + age1sex_cat + highhiv + 
#                        partner + condom + aidscare + 
#                        knowsdiedofaids + evertestedHIV + 
#                        smoke + religion + ethnicity + 
#                        language      
# 
# theta.eq <-          ~ s(region, bs = "mrf", xt = xt, k = 7)                       
#     
# fl <- list(sel.eq, out.eq, theta.eq)    
#      
# # the above model specification is fairly
# # complex and it serves to illustrate the 
# # flexibility of the modelling approach
#      
# bss <- SemiParBIVProbit(fl, data = hiv, BivD = "J90", 
#                         Model = "BSS")
# 
# conv.check(bss)
# 
# set.seed(1)
# sb <- summary(bss)
# sb
# 
# plot(bss, eq = 1, seWithMean = TRUE, scheme = 1,   
#      scale = 0, pages = 1, jit = TRUE)
#                     
# plot(bss, eq = 2, seWithMean = TRUE, scheme = 1,
#      scale = 0, pages = 1, jit = TRUE)
# 
# 
# 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),")")))
#           
# sb$CItheta[1,]
# ## End(Not run)

#