In RJauslin/StratifiedSampling: Different Methods for Stratified Sampling

library(knitr)
library(prettydoc)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

In this vignette we will explain how some functions of the package are used in order to estimate a contingency table. We will work on the eusilc dataset of the laeken package. All the functions presented in the following are explained in the proposed manuscript by Raphaël Jauslin and Yves Tillé (2021) .

Contingency table

We will estimate the contingency table when the factor variable which represents the economic status pl030 is crossed with a discretized version of the equivalized household income eqIncome. In order to discretize the equivalized income, we calculate percentiles (0.15,0.30,0.45,0.60,0.75,0.90) of the variable and define the category as intervals between the values.

library(laeken)
library(sampling)
library(StratifiedSampling)

data("eusilc")
eusilc <- na.omit(eusilc)
N <- nrow(eusilc)


# Xm are the matching variables and id are identity of the units
Xm <- eusilc[,c("hsize","db040","age","rb090","pb220a")]
Xmcat <- do.call(cbind,apply(Xm[,c(2,4,5)],MARGIN = 2,FUN = disjunctive))
Xm <- cbind(Xmcat,Xm[,-c(2,4,5)])
id <- eusilc$rb030


# categorial income splitted by the percentile
c_income  <- eusilc$eqIncome
q <- quantile(eusilc$eqIncome, probs = seq(0, 1, 0.15))
c_income[which(eusilc$eqIncome <= q[2])] <- "(0,15]"
c_income[which(q[2] < eusilc$eqIncome & eusilc$eqIncome <= q[3])] <- "(15,30]"
c_income[which(q[3] < eusilc$eqIncome & eusilc$eqIncome <= q[4])] <- "(30,45]"
c_income[which(q[4] < eusilc$eqIncome & eusilc$eqIncome <= q[5])] <- "(45,60]"
c_income[which(q[5] < eusilc$eqIncome & eusilc$eqIncome <= q[6])] <- "(60,75]"
c_income[which(q[6] < eusilc$eqIncome & eusilc$eqIncome <= q[7])] <- "(75,90]"
c_income[which(  eusilc$eqIncome > q[7] )] <- "(90,100]"

# variable of interests
Y <- data.frame(ecostat = eusilc$pl030)
Z <- data.frame(c_income = c_income)

# put same rownames
rownames(Xm) <- rownames(Y) <- rownames(Z)<- id

YZ <- table(cbind(Y,Z))
addmargins(YZ)

Sampling schemes

Here we set up the sampling designs and define all the quantities we will need for the rest of the vignette. The sample are selected with simple random sampling without replacement and the weights are equal to the inverse of the inclusion probabilities.

# size of sample
n1 <- 1000
n2 <- 500

# samples
s1 <- srswor(n1,N)
s2 <- srswor(n2,N)

# extract matching units
X1 <- Xm[s1 == 1,]
X2 <- Xm[s2 == 1,]

# extract variable of interest
Y1 <- data.frame(Y[s1 == 1,])
colnames(Y1) <- colnames(Y)
Z2 <- as.data.frame(Z[s2 == 1,])
colnames(Z2) <- colnames(Z)

# extract correct identities
id1 <- id[s1 == 1]
id2 <- id[s2 == 1]

# put correct rownames
rownames(Y1) <- id1
rownames(Z2) <- id2

# here weights are inverse of inclusion probabilities
d1 <- rep(N/n1,n1)
d2 <- rep(N/n2,n2)

# disjunctive form
Y_dis <- sampling::disjunctive(as.matrix(Y))
Z_dis <- sampling::disjunctive(as.matrix(Z))

Y1_dis <- Y_dis[s1 ==1,]
Z2_dis <- Z_dis[s2 ==1,]

Harmonization

Then the harmonization step must be performed. The harmonize function returns the harmonized weights. If by chance the true population totals are known, it is possible to use these instead of the estimate made within the function.

re <- harmonize(X1,d1,id1,X2,d2,id2)  

# if we want to use the population totals to harmonize we can use 
re <- harmonize(X1,d1,id1,X2,d2,id2,totals = c(N,colSums(Xm)))

w1 <- re$w1
w2 <- re$w2

colSums(Xm)
colSums(w1*X1)
colSums(w2*X2)

Optimal transport matching

The statistical matching is done by using the otmatch function. The estimation of the contingency table is calculated by extracting the id1 units (respectively id2 units) and by using the function tapply with the correct weights.

# Optimal transport matching
object <- otmatch(X1,id1,X2,id2,w1,w2)
head(object[,1:3])

Y1_ot <- cbind(X1[as.character(object$id1),],y = Y1[as.character(object$id1),])
Z2_ot <- cbind(X2[as.character(object$id2),],z = Z2[as.character(object$id2),])
YZ_ot <- tapply(object$weight,list(Y1_ot$y,Z2_ot$z),sum)

# transform NA into 0
YZ_ot[is.na(YZ_ot)] <- 0

# result
round(addmargins(YZ_ot),3)

Balanced sampling

As you can see from the previous section, the optimal transport results generally do not have a one-to-one match, meaning that for every unit in sample 1, we have more than one unit with weights not equal to 0 in sample 2. The bsmatch function creates a one-to-one match by selecting a balanced stratified sampling to obtain a data.frame where each unit in sample 1 has only one imputed unit from sample 2.

# Balanced Sampling 
BS <- bsmatch(object)
head(BS$object[,1:3])


Y1_bs <- cbind(X1[as.character(BS$object$id1),],y = Y1[as.character(BS$object$id1),])
Z2_bs <- cbind(X2[as.character(BS$object$id2),],z = Z2[as.character(BS$object$id2),])
YZ_bs <- tapply(BS$object$weight/BS$q,list(Y1_bs$y,Z2_bs$z),sum)
YZ_bs[is.na(YZ_bs)] <- 0
round(addmargins(YZ_bs),3)

# With Z2 as auxiliary information for stratified balanced sampling.
BS <- bsmatch(object,Z2)

Y1_bs <- cbind(X1[as.character(BS$object$id1),],y = Y1[as.character(BS$object$id1),])
Z2_bs <- cbind(X2[as.character(BS$object$id2),],z = Z2[as.character(BS$object$id2),])
YZ_bs <- tapply(BS$object$weight/BS$q,list(Y1_bs$y,Z2_bs$z),sum)
YZ_bs[is.na(YZ_bs)] <- 0
round(addmargins(YZ_bs),3)

Prediction

# split the weight by id1
q_l <- split(object$weight,f = object$id1)
# normalize in each id1
q_l <- lapply(q_l, function(x){x/sum(x)})
q <- as.numeric(do.call(c,q_l))

Z_pred <- t(q*disjunctive(object$id1))%*%disjunctive(Z2[as.character(object$id2),])
colnames(Z_pred) <- levels(factor(Z2$c_income))
head(Z_pred)