E.STSI: Estimation of the Population Total under Stratified Simple...
In TeachingSampling: Selection of Samples and Parameter Estimation in Finite Population
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Computes the Horvitz-Thompson estimator of the population total according to a STSI sampling design
Usage
1
E.STSI(S, Nh, nh, y)
Arguments
S
Vector identifying the membership to the strata of each unit in the population
Nh
Vector of stratum sizes
nh
Vector of sample sizes in each stratum
y
Vector, matrix or data frame containing the recollected information of the variables of interest for every
unit in the selected sample
Details
Returns the estimation of the population total of every single variable of interest, its estimated standard error and its estimated coefficient of variation in all of the strata and finally in the entire population
Value
The function returns an array composed by several matrices representing each variable of interest. The columns of each matrix
correspond to the estimated parameters of the variables of interest in each stratum and in the entire population
Author(s)
Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com
References
Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros.
Editorial Universidad Santo Tomas.
See Also
Examples
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############
## Example 1
############
# Uses the Lucy data to draw a stratified random sample
# according to a SI design in each stratum
data(Lucy)
attach(Lucy)
# Level is the stratifying variable
summary(Level)
# Defines the size of each stratum
N1<-summary(Level)[[1]]
N2<-summary(Level)[[2]]
N3<-summary(Level)[[3]]
N1;N2;N3
Nh <- c(N1,N2,N3)
# Defines the sample size at each stratum
n1<-N1
n2<-100
n3<-200
nh<-c(n1,n2,n3)
# Draws a stratified sample
sam <- S.STSI(Level, Nh, nh)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variables of interest are: Income, Employees and Taxes
# This information is stored in a data frame called estima
estima <- data.frame(Income, Employees, Taxes)
E.STSI(Level,Nh,nh,estima)
############
## Example 2
############
# Following with Example 1. The variable SPAM is a domain of interest
Doma <- Domains(SPAM)
# This function allows to estimate the parameters of the variables of interest
# for every category in the domain SPAM
SPAM.no <- estima*Doma[,1]
SPAM.yes <- estima*Doma[,2]
E.STSI(Level, Nh, nh, Doma)
E.STSI(Level, Nh, nh, SPAM.no)
E.STSI(Level, Nh, nh, SPAM.yes)
Example output
Big Medium Small
83 737 1576
[1] 83
[1] 737
[1] 1576
The following objects are masked from Lucy:
Employees, ID, Income, Level, SPAM, Taxes, Ubication, Zone
[1] "ID" "Ubication" "Level" "Zone" "Income" "Employees"
[7] "Taxes" "SPAM"
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 103706 4.819611e+05 4.340540e+05 1.019721e+06
Standard Error 0 8.417831e+03 1.272482e+04 1.525716e+04
CVE 0 1.746579e+00 2.931622e+00 1.496209e+00
DEFF NaN 1.000000e+00 1.000000e+00 1.069284e-01
, , Employees
Big Medium Small Population
Estimation 11461 61347.880000 80998.520000 1.538074e+05
Standard Error 0 1658.872647 2712.824459 3.179823e+03
CVE 0 2.704042 3.349227 2.067406e+00
DEFF NaN 1.000000 1.000000 3.949300e-01
, , Taxes
Big Medium Small Population
Estimation 6251 15889.7200 5839.080000 2.797980e+04
Standard Error 0 607.9724 329.210403 6.913827e+02
CVE 0 3.8262 5.638053 2.471006e+00
DEFF NaN 1.0000 1.000000 3.291117e-02
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , no
Big Medium Small Population
Estimation 26 309.54000 669.800000 1005.340000
Standard Error 0 33.98791 51.604690 61.791766
CVE 0 10.98014 7.704492 6.146355
DEFF NaN 1.00000 1.000000 1.260585
, , yes
Big Medium Small Population
Estimation 57 427.460000 906.200000 1390.660000
Standard Error 0 33.987914 51.604690 61.791766
CVE 0 7.951133 5.694625 4.443341
DEFF NaN 1.000000 1.000000 1.260585
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 31914 202704.48000 1.776861e+05 4.123046e+05
Standard Error 0 22818.54867 1.603565e+04 2.788957e+04
CVE 0 11.25705 9.024707e+00 6.764313e+00
DEFF NaN 1.00000 1.000000e+00 4.881024e-01
, , Employees
Big Medium Small Population
Estimation 3587 27556.43000 32111.000000 6.325443e+04
Standard Error 0 3220.03354 3045.938095 4.432421e+03
CVE 0 11.68523 9.485653 7.007289e+00
DEFF NaN 1.00000 1.000000 7.433862e-01
, , Taxes
Big Medium Small Population
Estimation 1844 6669.85000 2320.66000 10834.510000
Standard Error 0 816.09904 279.14432 862.519103
CVE 0 12.23564 12.02866 7.960850
DEFF NaN 1.00000 1.00000 0.154529
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 71792 279256.67000 256367.92000 6.074166e+05
Standard Error 0 23207.23582 17449.91309 2.903576e+04
CVE 0 8.31036 6.80659 4.780205e+00
DEFF NaN 1.00000 1.00000 3.254941e-01
, , Employees
Big Medium Small Population
Estimation 7874 33791.450000 48887.520000 90552.970000
Standard Error 0 2939.786282 3443.349776 4527.582253
CVE 0 8.699793 7.043413 4.999927
DEFF NaN 1.000000 1.000000 0.572543
, , Taxes
Big Medium Small Population
Estimation 4407 9219.870000 3518.420000 1.714529e+04
Standard Error 0 881.688040 319.538714 9.378053e+02
CVE 0 9.562912 9.081881 5.469755e+00
DEFF NaN 1.000000 1.000000 6.813259e-02
TeachingSampling documentation built on April 22, 2020, 1:05 a.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Computes the Horvitz-Thompson estimator of the population total according to a STSI sampling design
Usage
1 | E.STSI(S, Nh, nh, y)
|
Arguments
S |
Vector identifying the membership to the strata of each unit in the population |
Nh |
Vector of stratum sizes |
nh |
Vector of sample sizes in each stratum |
y |
Vector, matrix or data frame containing the recollected information of the variables of interest for every unit in the selected sample |
Details
Returns the estimation of the population total of every single variable of interest, its estimated standard error and its estimated coefficient of variation in all of the strata and finally in the entire population
Value
The function returns an array composed by several matrices representing each variable of interest. The columns of each matrix correspond to the estimated parameters of the variables of interest in each stratum and in the entire population
Author(s)
Hugo Andres Gutierrez Rojas hagutierrezro@gmail.com
References
Sarndal, C-E. and Swensson, B. and Wretman, J. (1992), Model Assisted Survey Sampling. Springer.
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros.
Editorial Universidad Santo Tomas.
See Also
Examples
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 | ############
## Example 1
############
# Uses the Lucy data to draw a stratified random sample
# according to a SI design in each stratum
data(Lucy)
attach(Lucy)
# Level is the stratifying variable
summary(Level)
# Defines the size of each stratum
N1<-summary(Level)[[1]]
N2<-summary(Level)[[2]]
N3<-summary(Level)[[3]]
N1;N2;N3
Nh <- c(N1,N2,N3)
# Defines the sample size at each stratum
n1<-N1
n2<-100
n3<-200
nh<-c(n1,n2,n3)
# Draws a stratified sample
sam <- S.STSI(Level, Nh, nh)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variables of interest are: Income, Employees and Taxes
# This information is stored in a data frame called estima
estima <- data.frame(Income, Employees, Taxes)
E.STSI(Level,Nh,nh,estima)
############
## Example 2
############
# Following with Example 1. The variable SPAM is a domain of interest
Doma <- Domains(SPAM)
# This function allows to estimate the parameters of the variables of interest
# for every category in the domain SPAM
SPAM.no <- estima*Doma[,1]
SPAM.yes <- estima*Doma[,2]
E.STSI(Level, Nh, nh, Doma)
E.STSI(Level, Nh, nh, SPAM.no)
E.STSI(Level, Nh, nh, SPAM.yes)
|
Example output
Big Medium Small
83 737 1576
[1] 83
[1] 737
[1] 1576
The following objects are masked from Lucy:
Employees, ID, Income, Level, SPAM, Taxes, Ubication, Zone
[1] "ID" "Ubication" "Level" "Zone" "Income" "Employees"
[7] "Taxes" "SPAM"
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 103706 4.819611e+05 4.340540e+05 1.019721e+06
Standard Error 0 8.417831e+03 1.272482e+04 1.525716e+04
CVE 0 1.746579e+00 2.931622e+00 1.496209e+00
DEFF NaN 1.000000e+00 1.000000e+00 1.069284e-01
, , Employees
Big Medium Small Population
Estimation 11461 61347.880000 80998.520000 1.538074e+05
Standard Error 0 1658.872647 2712.824459 3.179823e+03
CVE 0 2.704042 3.349227 2.067406e+00
DEFF NaN 1.000000 1.000000 3.949300e-01
, , Taxes
Big Medium Small Population
Estimation 6251 15889.7200 5839.080000 2.797980e+04
Standard Error 0 607.9724 329.210403 6.913827e+02
CVE 0 3.8262 5.638053 2.471006e+00
DEFF NaN 1.0000 1.000000 3.291117e-02
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , no
Big Medium Small Population
Estimation 26 309.54000 669.800000 1005.340000
Standard Error 0 33.98791 51.604690 61.791766
CVE 0 10.98014 7.704492 6.146355
DEFF NaN 1.00000 1.000000 1.260585
, , yes
Big Medium Small Population
Estimation 57 427.460000 906.200000 1390.660000
Standard Error 0 33.987914 51.604690 61.791766
CVE 0 7.951133 5.694625 4.443341
DEFF NaN 1.000000 1.000000 1.260585
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 31914 202704.48000 1.776861e+05 4.123046e+05
Standard Error 0 22818.54867 1.603565e+04 2.788957e+04
CVE 0 11.25705 9.024707e+00 6.764313e+00
DEFF NaN 1.00000 1.000000e+00 4.881024e-01
, , Employees
Big Medium Small Population
Estimation 3587 27556.43000 32111.000000 6.325443e+04
Standard Error 0 3220.03354 3045.938095 4.432421e+03
CVE 0 11.68523 9.485653 7.007289e+00
DEFF NaN 1.00000 1.000000 7.433862e-01
, , Taxes
Big Medium Small Population
Estimation 1844 6669.85000 2320.66000 10834.510000
Standard Error 0 816.09904 279.14432 862.519103
CVE 0 12.23564 12.02866 7.960850
DEFF NaN 1.00000 1.00000 0.154529
, , N
Big Medium Small Population
Estimation 83 737 1576 2396
Standard Error 0 0 0 0
CVE 0 0 0 0
DEFF NaN NaN NaN NaN
, , Income
Big Medium Small Population
Estimation 71792 279256.67000 256367.92000 6.074166e+05
Standard Error 0 23207.23582 17449.91309 2.903576e+04
CVE 0 8.31036 6.80659 4.780205e+00
DEFF NaN 1.00000 1.00000 3.254941e-01
, , Employees
Big Medium Small Population
Estimation 7874 33791.450000 48887.520000 90552.970000
Standard Error 0 2939.786282 3443.349776 4527.582253
CVE 0 8.699793 7.043413 4.999927
DEFF NaN 1.000000 1.000000 0.572543
, , Taxes
Big Medium Small Population
Estimation 4407 9219.870000 3518.420000 1.714529e+04
Standard Error 0 881.688040 319.538714 9.378053e+02
CVE 0 9.562912 9.081881 5.469755e+00
DEFF NaN 1.000000 1.000000 6.813259e-02
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