Database of household expenses for two sampling frames

This dataset contains some variables regarding household expenses for a sample of 105 households selected from a list of landline phones (frame A) and a sample of 135 from a list of mobile phones (frame B) in a particular city in a specific month. These data are taken from the Frames2 package under the GPL-2 or GPL-3 licence.

Usage

data(phoneframes)

Format

Domain

A factor indicating the domain each household belongs to. In sample A, possible values are "a" if household belongs to domain a or "ab" if household belongs to overlap domain; in sample B, the values are "b" or "ba"

Feed

Feeding expenses (in euros) at the househould

Clo

Clothing expenses (in euros) at the household

Lei

Leisure expenses (in euros) at the household

Inc

Household income (in euros). Values for this variable are only available for households included in frame A. For households included in domain b, value of this variable is missing

Tax

Household municipal taxes (in euros) paid. Values for this variable are only available for households included in frame A. For households included in domain b, value of this variable is missing

M2

Square meters of the house. Values for this variable are only available for households included in frame B. For households included in domain a, value of this variable is missing

Size

Household size. Values for this variable are only available for households included in frame B. For households included in domain a, value of this variable is missing

ProbA

First order inclusion probability in frame A. This probability is 0 for households included in domain b.

ProbB

First order inclusion probability in frame B. This probability is 0 for households included in domain a.

Stratum

A numeric value indicating the stratum each household belongs to.

Details

The frame A sample, of size \(n_A = 105\), has been drawn from a population of \(N_A = 1735\) households with landline phone according to a stratified random sampling. Population units were divided in 6 different strata. Population sizes of these strata are \(N_A^h = (727, 375, 113, 186, 115, 219)\). \(N_{ab} = 601\) of the households composing the population have, also, mobile phone. On the other hand, frame totals for auxiliary variables in this frame are \(X_{Income}^A = 4300260\) and \(X_{Taxes}^A = 215577\).

The frame B sample, of size \(n_B = 135\), has been drawn from a population of \(N_B = 1191\) households with mobile phone according to a simple random sampling without replacement design. \(N_{ab} = 601\) of these households have, also, landline phone. On the other hand, frame totals for auxiliary variables in this frame are \(X_{Metres2}^B = 176553\) and \(X_{Size}^B = 3529\)

PiklA and PiklB are matrices of pairwise sampling probabilities for the two frames.

See also

multiframe, reweight

Original package: https://CRAN.R-project.org/package=Frames2

Examples

data(phoneframes)
A_in_frames<-cbind(1, DatA$Domain=="ab")
B_in_frames<-cbind(DatB$Domain=="ba",1)

Bdes_pps<-svydesign(id=~1, fpc=~ProbB, data=DatB,pps=ppsmat(PiklB))
Ades_pps <-svydesign(id=~1, fpc=~ProbA,data=DatA,pps=ppsmat(PiklA))

## optimal constant (Hartley) weighting
mf_pps<-multiframe(list(Ades_pps,Bdes_pps),list(A_in_frames,B_in_frames),theta=0.74) 
svytotal(~Lei,mf_pps)
#>     total     SE
#> Lei 53251 1285.5

Awts<-cbind(1/DatA$ProbA, ifelse(DatA$ProbB==0,0,1/DatA$ProbB))
Bwts<-cbind(ifelse(DatB$ProbA==0,0,1/DatB$ProbA),1/DatB$ProbB )
## dividing by the expected number of selections (BKA or HH estimator)
mf_pps2<-multiframe(list(Ades_pps,Bdes_pps),list(Awts,Bwts),estimator="expected") 
svymean(~Lei,mf_pps2)
#>       mean     SE
#> Lei 22.298 0.3166

## Metcalf and Scott approximation
DatB$Stratum<-10
DatB$Frame<-2
DatA$Frame<-1
Dat_both<-rbind(DatA,DatB)
frame_weights<-c(0.742,1-0.742)
Dat_both$fweights<-with(Dat_both, ifelse(Frame==1, 
   ifelse(Domain=="ab", frame_weights[1]*1/ProbA,1/ProbA),
   ifelse(Domain=="ba", frame_weights[2]*1/ProbB, 1/ProbB)))

MSdesign<-svydesign(id=~1, strata=~Stratum, weights=~fweights,data=Dat_both)
svymean(~Lei,MSdesign)
#>       mean     SE
#> Lei 22.488 0.3773