Description Usage Arguments Details Author(s) References Examples
Adjustment of a table on the margins
1 | IPFP(Table, Col.knw, Row.knw, tol=0.0001)
|
Table |
A contingency table |
Col.knw |
A vector containing the true totals of the columns |
Row.knw |
A vector containing the true totals of the Rows |
tol |
The control value, by default equal to 0.0001 |
Adjust a contingency table on the know margins of the population with the Raking Ratio method
Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com
Deming, W. & Stephan, F. (1940), On a least squares adjustment of a sampled frequency
table when the expected marginal totals are known. Annals of Mathematical Statistics, 11, 427-444.
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros.
Editorial Universidad Santo Tomas.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ############
## Example 1
############
# Some example of Ardilly and Tille
Table <- matrix(c(80,90,10,170,80,80,150,210,130),3,3)
rownames(Table) <- c("a1", "a2","a3")
colnames(Table) <- c("b1", "b2","b3")
# The table with labels
Table
# The known and true margins
Col.knw <- c(150,300,550)
Row.knw <- c(430,360,210)
# The adjusted table
IPFP(Table,Col.knw,Row.knw,tol=0.0001)
############
## Example 2
############
# Draws a simple random sample
data(Lucy)
attach(Lucy)
N<-dim(Lucy)[1]
n<-400
sam<-sample(N,n)
data<-Lucy[sam,]
attach(data)
dim(data)
# Two domains of interest
Doma1<-Domains(Level)
Doma2<-Domains(SPAM)
# Cross tabulate of domains
SPAM.no<-Doma2[,1]*Doma1
SPAM.yes<-Doma2[,2]*Doma1
# Estimation
E.SI(N,n,Doma1)
E.SI(N,n,Doma2)
est1 <-E.SI(N,n,SPAM.no)[,2:4]
est2 <-E.SI(N,n,SPAM.yes)[,2:4]
est1;est2
# The contingency table estimated from above
Table <- cbind(est1[1,],est2[1,])
rownames(Table) <- c("Big", "Medium","Small")
colnames(Table) <- c("SPAM.no", "SPAM.yes")
# The known and true margins
Col.knw <- colSums(Domains(Lucy$SPAM))
Row.knw<- colSums(Domains(Lucy$Level))
# The adjusted table
IPFP(Table,Col.knw,Row.knw,tol=0.0001)
|
b1 b2 b3
a1 80 170 150
a2 90 80 210
a3 10 80 130
b1 b2 b3 Row.est
a1 73.00821 167.64061 189.3512 430
a2 69.39039 66.64928 223.9603 360
a3 7.60140 65.71011 136.6885 210
Col.est 150.00000 300.00000 550.0000 1000
The following objects are masked from Lucy:
Employees, ID, Income, Level, SPAM, Taxes, Ubication, Zone
[1] 400 8
N Big Medium Small
Estimation 2396 47.92000 754.740000 1593.340000
Standard Error 0 15.32729 50.855484 51.673806
CVE 0 31.98515 6.738146 3.243112
DEFF NaN 1.00000 1.000000 1.000000
N no yes
Estimation 2396 964.390000 1431.610000
Standard Error 0 53.689471 53.689471
CVE 0 5.567195 3.750286
DEFF NaN 1.000000 1.000000
Big Medium Small
Estimation 17.970000 287.52000 658.900000
Standard Error 9.445678 35.57699 48.884658
CVE 52.563593 12.37374 7.419132
DEFF 1.000000 1.00000 1.000000
Big Medium Small
Estimation 29.95000 467.220000 934.440000
Standard Error 12.16356 43.376297 53.399163
CVE 40.61290 9.283913 5.714563
DEFF 1.00000 1.000000 1.000000
SPAM.no SPAM.yes Row.est
Big 30.22642 52.77358 83
Medium 272.73124 464.26876 737
Small 634.04234 941.95766 1576
Col.est 937.00000 1459.00000 2396
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