| hiv {GJRM} | R Documentation |
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.
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$region (order unimportant).
Source
The data have been produced as described in:
McGovern M.E., Barnighausen T., Marra G. and Radice R. (2015), On the Assumption of Joint Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence. Epidemiology, 26(2), 229-237.
References
Marra G., Radice R., Barnighausen T., Wood S.N. and McGovern M.E. (2017), A Simultaneous Equation Approach to Estimating HIV Prevalence with Non-Ignorable Missing Responses. Journal of the American Statistical Association, 112(518), 484-496.
Examples
## Not run:
#########################################################
#########################################################
library("GJRM")
data("hiv", package = "GJRM")
data("hiv.polys", package = "GJRM")
#########################################################
#########################################################
## The stuff below is useful if the user wishes to employ
## a Markov Random Field (MRF) smoother. It provides
## the instructions to set up polygons automatically
## and the dataset variable needed to fit a model with
## MRF.
#########################################################
#########################################################
#
# ## hiv.polys was already created and
# ## made available via the call
# ## data("hiv.polys", package = "GJRM")
# ## hiv.polys was created using the code below
#
# obj <- readRDS("ZMB_adm1.rds")
# ## RDS Zambian Level 1 file obtained from
# ## http://www.gadm.org.
#
# pol <- polys.setup(obj)
#
# hiv.polys <- pol$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 <- gjrm(fl, data = hiv, copula = "J90", model = "BSS",
margins = c("probit", "probit"))
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)
#