Sample Balancing

The goal of the “Sample Balancing” module is to provide a weight for each respondent in the sample such that the weighted marginals on each of a set of characteristics matches preset values of those marginals. This process is sometimes called “raking” or “rim weighting.”

The most common procedure used to produce these weights is “iterative proportional fitting”, a procedure devised by W. Edwards Deming and Frederick F. Stephan, which is the current algorithm used by Simmons.

The new functions are based on current sample balancing functions used at Simmons, but with lots of modifications. Compared to original version, the new features include:

Two functions related to sample balancing are included in the package:

Examples:

# load the libraries
library(haven)
library(dplyr)
library(openxlsx)
library(SimmonsResearchR)
# read the data
DatIn <- read_sav("W50123 AdultMasterDemoFile SB v2.sav") %>%
  filter(WAVE_ID %in% c('1516', '1616'))

# read targ file which is a spreadsheet contains targ information
targ <- read.xlsx("targ.xlsx")

# subset the data
work1.new <- DatIn %>%
  filter(group==1,
         dma %in% c(501, 504, 505, 506, 510, 511, 524, 528, 602, 618, 623, 641, 803, 807))

work2.new <- DatIn %>%
  filter(group==3,
         dma %in% c(501, 504, 505, 506, 510, 511, 524, 528, 602, 618, 623, 641, 803, 807)) 

work1.wgt <- work1.new[["DESIGN_WGT"]] /2 
work2.wgt <- work2.new[["DESIGN_WGT"]] /2 

# Initialize sample balance, save targ information to plan text file
targs.list <- sample_balance_init(data=DatIn, targ=targ, out="targ.txt")

# sample balancing using 6 times mean cap, and save diagnosis information to out1.xlsx
sb1 <- sample_blance(data=work1.new, 
                     ID="BOOK_ID",
                     targstr=targs.list[[1]],
                     dweights=work1.wgt,
                     cap=T,
                     typeofcap=1,
                     capval=6,
                     floor=T,
                     floorval=50,
                     eps=.001,
                     rounding=F,
                     klimit=F,
                     klimitval=Inf, 
                     out="out1.xlsx")

# sample balancing using 2 times std cap, and save diagnosis information to out2.xlsx
sb2 <- sample_blance(data=work2.new, 
                     ID="BOOK_ID",
                     targstr=targs.list[[2]],
                     dweights=work2.wgt,
                     cap=T,
                     typeofcap=2,
                     capval=2,
                     floor=T,
                     floorval=50,
                     eps=.001,
                     rounding=F,
                     klimit=F,
                     klimitval=Inf, 
                     out="out2.xlsx")

# Get new capped weights from the 2 sample balancing modules
cap_wgt1<-sb1[[2]]
cap_wgt2<-sb2[[2]]

Note:

Example of the targ spreadsheet is listed below. Age, race, F6A10A, F10A3P, F3P7P, S6A12A, M-S Prime and M-S All Day are used as sample balancing variables. There are 4 sample balancing modules to be conducted.

 library(SimmonsResearchR)

 data(targ)

 knitr::kable(targ, caption = "Example of the Targ Spreadsheet") 


yangx227/SimmonsResearchR documentation built on Oct. 5, 2017, 4:09 p.m.