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Mean income with different design assumptions
Raking to match Scottish totals 
Jacknife estimation for mean
Subgroup lone parents
 Percentiles



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> frs.des <- svydesign(id=~PSU, weights=~GROSS2,data=frs)
> summary(frs.des)
1 - level Cluster Sampling design
With (320) clusters.
svydesign(id = ~PSU, weights = ~GROSS2, data = frs)
Probabilities:
     Min.   1st Qu.    Median      Mean   3rd Qu.
0.0001369 0.0019080 0.0022420 0.0022720 0.0026040
     Max.
0.0049750
Data variables:
[1] "SERNUM"   "CTBAND"   "ADULTH"   "DEPCHLDH" "GROSS2"
[6] "HHINC"    "PSU"      "TENURE"
> svymean(~HHINC,design=frs.des,deff=T)
         mean      SE   DEff
HHINC 483.091  10.639 2.9066
> frs.des <- svydesign(id=~SERNUM, weights=~GROSS2,data=frs)
> svymean(~HHINC,design=frs.des,deff=T)
          mean       SE   DEff
HHINC 483.0913   7.8775 1.5934
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> tab.ctband <- xtabs(GROSS2~CTBAND,data=frs)
> tab.ctband[1:9]<-c( 24.83 ,24.62, 15.45 ,11.91 ,11.96 ,5.95 ,3.94 ,0.45 ,0.89)*sum(frs$GROSS2)/100
> unclass(tab.ctband)
CTBAND
     1      2      3      4      5      6      7      8
515672 547548 351599 291425 266257 147851  87767   9190
     9
 19670
attr(,"call")
xtabs(formula = GROSS2 ~ CTBAND, data = frs)
>
> tab.tenure <- xtabs(GROSS2~TENURE,data=frs)
> tab.tenure[1:4]<-c(  62.63 , 21.59, 5.58 , 10.20)*sum(frs$GROSS2)/100
> unclass(tab.ctband)
CTBAND
     1      2      3      4      5      6      7      8
515672 547548 351599 291425 266257 147851  87767   9190
     9
 19670
attr(,"call")
xtabs(formula = GROSS2 ~ CTBAND, data = frs)
> frs.des <- svydesign(id=~PSU, weights=~GROSS2,data=frs)
> frs.des <- as.svrepdesign(frs.des)
> frs.des<-rake(frs.des,list(~CTBAND,~TENURE),list(tab.ctband,tab.tenure))
> svymean(~HHINC,design=frs.des,deff=T)
        mean     SE   DEff
HHINC 479.58   7.46 1.4352
>
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> svymean(~HHINC,design=frs.des,deff=T)

        mean     SE   DEff
HHINC 479.58   7.46 1.4352


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 my.svrepquantile(~HHINC,design=frs.des,quantile=c(0.05,0.1,.25,.5,.75,.9,.95))
     quantiles  Quantile        se   l.limit   u.limit
[1,]      0.05  100.0000  3.073927   92.0000  106.9191
[2,]      0.10  129.0000  3.500000  121.0000  135.0000
[3,]      0.25  200.0000  4.000000  192.7797  208.0000
[4,]      0.50  350.0000  7.000000  338.0000  365.0000
[5,]      0.75  618.0000 11.791250  596.0000  636.0000
[6,]      0.90  981.2433 23.531862  930.0000 1019.7699
[7,]      0.95 1275.0000 32.899771 1204.0000 1337.8400
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> xtabs(~ADULTH+DEPCHLDH,data=frs)
      DEPCHLDH
ADULTH    0    1    2    3    4    5    6    7
     1 1524  172  118   33    8    1    1    1
     2 1472  348  419  131   15    8    4    1
     3  235   78   23    5    2    2    0    0
     4   62   13    5    3    1    1    0    0
     5    6    2    0    0    0    0    0    0
     6    1    0    0    0    0    0    0    0
> frs$LONEP<-0
> frs$LONEP[frs$ADULTH==1 & frs$DEPCHLDH>0]<-1
> xtabs(~LONEP+DEPCHLDH,data=frs)
     DEPCHLDH
LONEP    0    1    2    3    4    5    6    7
    0 3300  441  447  139   18   11    4    1
    1    0  172  118   33    8    1    1    1
> sum(frs$LONEP)
[1] 334
> frs.des <- svydesign(id=~PSU, weights=~GROSS2,data=frs)
> frs.des.lonep<-subset.survey.design(frs.des,LONEP==1)
> svymean(~HHINC,frs.des.lonep,deff=T)
          mean       SE   DEff
HHINC 276.5555   8.4954 1.0101
> frs.des <- svydesign(id=~PSU, weights=~GROSS2,data=frs)
> frs.des <- as.svrepdesign(frs.des)
> frs.des<-rake(frs.des,list(~CTBAND,~TENURE),list(tab.ctband,tab.tenure))
> frs.des.lonep<-subset.survey.design(frs.des,LONEP==1)
> svymean(~HHINC,frs.des.lonep,deff=T)
          mean       SE   DEff
HHINC 274.4804   8.1958 0.9732
>