Domains: Domains Indicator Matrix
In TeachingSampling: Selection of Samples and Parameter Estimation in Finite Population
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Creates a matrix of domain indicator variables for every single unit in the selected sample or in the entire population
Usage
1
Domains(y)
Arguments
y
Vector of the domain of interest containing the membership of each unit to a specified category of the domain
Details
Each value of y represents the domain which a specified unit belongs
Value
The function returns a n\times p matrix, where n is the number of units in the selected
sample and p is the number of categories of the domain of interest. The values of this matrix are zero, if the unit does not
belongs to a specified category and one, otherwise.
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
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8
9
10
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12
13
14
15
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18
19
20
21
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41
############
## Example 1
############
# This domain contains only two categories: "yes" and "no"
x <- as.factor(c("yes","yes","yes","no","no","no","no","yes","yes"))
Domains(x)
############
## Example 2
############
# Uses the Lucy data to draw a random sample of units according
# to a SI design
data(Lucy)
attach(Lucy)
N <- dim(Lucy)[1]
n <- 400
sam <- sample(N,n)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variable SPAM is a domain of interest
Doma <- Domains(SPAM)
Doma
# HT estimation of the absolute domain size for every category in the domain
# of interest
E.SI(N,n,Doma)
############
## Example 3
############
# Following with Example 2...
# The variables of interest are: Income, Employees and Taxes
# This function allows to estimate the population total of this variables for every
# category in the domain of interest SPAM
estima <- data.frame(Income, Employees, Taxes)
SPAM.no <- estima*Doma[,1]
SPAM.yes <- estima*Doma[,2]
E.SI(N,n,SPAM.no)
E.SI(N,n,SPAM.yes)
Example output
no yes
[1,] 0 1
[2,] 0 1
[3,] 0 1
[4,] 1 0
[5,] 1 0
[6,] 1 0
[7,] 1 0
[8,] 0 1
[9,] 0 1
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"
no yes
[1,] 1 0
[2,] 0 1
[3,] 0 1
[4,] 0 1
[5,] 0 1
[6,] 0 1
[7,] 0 1
[8,] 1 0
[9,] 0 1
[10,] 0 1
[11,] 0 1
[12,] 1 0
[13,] 1 0
[14,] 1 0
[15,] 0 1
[16,] 0 1
[17,] 0 1
[18,] 0 1
[19,] 0 1
[20,] 0 1
[21,] 1 0
[22,] 0 1
[23,] 0 1
[24,] 1 0
[25,] 0 1
[26,] 0 1
[27,] 0 1
[28,] 1 0
[29,] 0 1
[30,] 0 1
[31,] 1 0
[32,] 1 0
[33,] 1 0
[34,] 0 1
[35,] 0 1
[36,] 0 1
[37,] 1 0
[38,] 1 0
[39,] 1 0
[40,] 1 0
[41,] 0 1
[42,] 0 1
[43,] 1 0
[44,] 1 0
[45,] 1 0
[46,] 1 0
[47,] 1 0
[48,] 1 0
[49,] 1 0
[50,] 1 0
[51,] 0 1
[52,] 1 0
[53,] 0 1
[54,] 1 0
[55,] 0 1
[56,] 1 0
[57,] 1 0
[58,] 1 0
[59,] 1 0
[60,] 1 0
[61,] 1 0
[62,] 0 1
[63,] 0 1
[64,] 0 1
[65,] 1 0
[66,] 0 1
[67,] 0 1
[68,] 0 1
[69,] 1 0
[70,] 1 0
[71,] 0 1
[72,] 0 1
[73,] 0 1
[74,] 0 1
[75,] 0 1
[76,] 1 0
[77,] 1 0
[78,] 1 0
[79,] 1 0
[80,] 0 1
[81,] 1 0
[82,] 0 1
[83,] 1 0
[84,] 1 0
[85,] 0 1
[86,] 1 0
[87,] 1 0
[88,] 1 0
[89,] 1 0
[90,] 0 1
[91,] 1 0
[92,] 1 0
[93,] 1 0
[94,] 1 0
[95,] 1 0
[96,] 1 0
[97,] 0 1
[98,] 1 0
[99,] 0 1
[100,] 1 0
[101,] 1 0
[102,] 0 1
[103,] 1 0
[104,] 1 0
[105,] 0 1
[106,] 0 1
[107,] 1 0
[108,] 1 0
[109,] 1 0
[110,] 0 1
[111,] 0 1
[112,] 1 0
[113,] 0 1
[114,] 0 1
[115,] 0 1
[116,] 0 1
[117,] 0 1
[118,] 0 1
[119,] 1 0
[120,] 0 1
[121,] 1 0
[122,] 0 1
[123,] 1 0
[124,] 0 1
[125,] 0 1
[126,] 0 1
[127,] 1 0
[128,] 0 1
[129,] 0 1
[130,] 0 1
[131,] 0 1
[132,] 0 1
[133,] 0 1
[134,] 1 0
[135,] 1 0
[136,] 1 0
[137,] 1 0
[138,] 1 0
[139,] 1 0
[140,] 1 0
[141,] 0 1
[142,] 0 1
[143,] 0 1
[144,] 1 0
[145,] 0 1
[146,] 0 1
[147,] 0 1
[148,] 0 1
[149,] 0 1
[150,] 0 1
[151,] 1 0
[152,] 1 0
[153,] 0 1
[154,] 0 1
[155,] 1 0
[156,] 1 0
[157,] 0 1
[158,] 1 0
[159,] 0 1
[160,] 0 1
[161,] 1 0
[162,] 0 1
[163,] 0 1
[164,] 0 1
[165,] 0 1
[166,] 1 0
[167,] 1 0
[168,] 1 0
[169,] 0 1
[170,] 1 0
[171,] 0 1
[172,] 1 0
[173,] 1 0
[174,] 0 1
[175,] 0 1
[176,] 0 1
[177,] 0 1
[178,] 1 0
[179,] 0 1
[180,] 0 1
[181,] 0 1
[182,] 1 0
[183,] 0 1
[184,] 1 0
[185,] 1 0
[186,] 1 0
[187,] 0 1
[188,] 0 1
[189,] 1 0
[190,] 1 0
[191,] 0 1
[192,] 1 0
[193,] 0 1
[194,] 0 1
[195,] 0 1
[196,] 1 0
[197,] 1 0
[198,] 0 1
[199,] 0 1
[200,] 1 0
[201,] 0 1
[202,] 1 0
[203,] 0 1
[204,] 1 0
[205,] 0 1
[206,] 1 0
[207,] 0 1
[208,] 0 1
[209,] 1 0
[210,] 0 1
[211,] 1 0
[212,] 0 1
[213,] 0 1
[214,] 0 1
[215,] 1 0
[216,] 1 0
[217,] 1 0
[218,] 0 1
[219,] 0 1
[220,] 0 1
[221,] 1 0
[222,] 0 1
[223,] 1 0
[224,] 0 1
[225,] 1 0
[226,] 0 1
[227,] 0 1
[228,] 1 0
[229,] 1 0
[230,] 1 0
[231,] 1 0
[232,] 0 1
[233,] 1 0
[234,] 0 1
[235,] 0 1
[236,] 0 1
[237,] 1 0
[238,] 0 1
[239,] 0 1
[240,] 0 1
[241,] 1 0
[242,] 0 1
[243,] 0 1
[244,] 1 0
[245,] 1 0
[246,] 1 0
[247,] 0 1
[248,] 0 1
[249,] 1 0
[250,] 0 1
[251,] 1 0
[252,] 1 0
[253,] 1 0
[254,] 0 1
[255,] 1 0
[256,] 1 0
[257,] 1 0
[258,] 1 0
[259,] 0 1
[260,] 0 1
[261,] 0 1
[262,] 0 1
[263,] 1 0
[264,] 1 0
[265,] 1 0
[266,] 1 0
[267,] 1 0
[268,] 1 0
[269,] 1 0
[270,] 1 0
[271,] 0 1
[272,] 0 1
[273,] 0 1
[274,] 0 1
[275,] 1 0
[276,] 0 1
[277,] 0 1
[278,] 1 0
[279,] 0 1
[280,] 1 0
[281,] 0 1
[282,] 1 0
[283,] 0 1
[284,] 0 1
[285,] 0 1
[286,] 0 1
[287,] 0 1
[288,] 1 0
[289,] 0 1
[290,] 0 1
[291,] 0 1
[292,] 1 0
[293,] 0 1
[294,] 0 1
[295,] 0 1
[296,] 0 1
[297,] 1 0
[298,] 1 0
[299,] 1 0
[300,] 1 0
[301,] 0 1
[302,] 1 0
[303,] 0 1
[304,] 0 1
[305,] 0 1
[306,] 0 1
[307,] 0 1
[308,] 0 1
[309,] 1 0
[310,] 0 1
[311,] 1 0
[312,] 0 1
[313,] 0 1
[314,] 1 0
[315,] 0 1
[316,] 1 0
[317,] 0 1
[318,] 1 0
[319,] 0 1
[320,] 0 1
[321,] 1 0
[322,] 0 1
[323,] 0 1
[324,] 0 1
[325,] 0 1
[326,] 1 0
[327,] 0 1
[328,] 0 1
[329,] 1 0
[330,] 0 1
[331,] 0 1
[332,] 0 1
[333,] 0 1
[334,] 1 0
[335,] 0 1
[336,] 0 1
[337,] 1 0
[338,] 1 0
[339,] 0 1
[340,] 0 1
[341,] 0 1
[342,] 0 1
[343,] 0 1
[344,] 0 1
[345,] 1 0
[346,] 0 1
[347,] 1 0
[348,] 0 1
[349,] 0 1
[350,] 1 0
[351,] 0 1
[352,] 1 0
[353,] 1 0
[354,] 1 0
[355,] 0 1
[356,] 1 0
[357,] 0 1
[358,] 0 1
[359,] 0 1
[360,] 0 1
[361,] 0 1
[362,] 1 0
[363,] 0 1
[364,] 0 1
[365,] 1 0
[366,] 1 0
[367,] 1 0
[368,] 0 1
[369,] 0 1
[370,] 0 1
[371,] 0 1
[372,] 0 1
[373,] 0 1
[374,] 1 0
[375,] 0 1
[376,] 0 1
[377,] 0 1
[378,] 0 1
[379,] 1 0
[380,] 0 1
[381,] 0 1
[382,] 1 0
[383,] 0 1
[384,] 0 1
[385,] 0 1
[386,] 1 0
[387,] 0 1
[388,] 0 1
[389,] 0 1
[390,] 0 1
[391,] 0 1
[392,] 0 1
[393,] 0 1
[394,] 0 1
[395,] 0 1
[396,] 0 1
[397,] 1 0
[398,] 0 1
[399,] 1 0
[400,] 0 1
N no yes
Estimation 2396 1024.29000 1371.710000
Standard Error 0 54.16179 54.161793
CVE 0 5.28774 3.948487
DEFF NaN 1.00000 1.000000
N Income Employees Taxes
Estimation 2396 4.310823e+05 60792.51000 11114.445000
Standard Error 0 2.861642e+04 3847.71602 1090.885873
CVE 0 6.638273e+00 6.32926 9.815028
DEFF NaN 1.000000e+00 1.00000 1.000000
N Income Employees Taxes
Estimation 2396 6.210672e+05 88687.940000 17619.585000
Standard Error 0 3.276007e+04 4470.242148 1632.874048
CVE 0 5.274802e+00 5.040417 9.267381
DEFF NaN 1.000000e+00 1.000000 1.000000
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
Creates a matrix of domain indicator variables for every single unit in the selected sample or in the entire population
Usage
1 | Domains(y)
|
Arguments
y |
Vector of the domain of interest containing the membership of each unit to a specified category of the domain |
Details
Each value of y represents the domain which a specified unit belongs
Value
The function returns a n\times p matrix, where n is the number of units in the selected sample and p is the number of categories of the domain of interest. The values of this matrix are zero, if the unit does not belongs to a specified category and one, otherwise.
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 | ############
## Example 1
############
# This domain contains only two categories: "yes" and "no"
x <- as.factor(c("yes","yes","yes","no","no","no","no","yes","yes"))
Domains(x)
############
## Example 2
############
# Uses the Lucy data to draw a random sample of units according
# to a SI design
data(Lucy)
attach(Lucy)
N <- dim(Lucy)[1]
n <- 400
sam <- sample(N,n)
# The information about the units in the sample is stored in an object called data
data <- Lucy[sam,]
attach(data)
names(data)
# The variable SPAM is a domain of interest
Doma <- Domains(SPAM)
Doma
# HT estimation of the absolute domain size for every category in the domain
# of interest
E.SI(N,n,Doma)
############
## Example 3
############
# Following with Example 2...
# The variables of interest are: Income, Employees and Taxes
# This function allows to estimate the population total of this variables for every
# category in the domain of interest SPAM
estima <- data.frame(Income, Employees, Taxes)
SPAM.no <- estima*Doma[,1]
SPAM.yes <- estima*Doma[,2]
E.SI(N,n,SPAM.no)
E.SI(N,n,SPAM.yes)
|
Example output
no yes
[1,] 0 1
[2,] 0 1
[3,] 0 1
[4,] 1 0
[5,] 1 0
[6,] 1 0
[7,] 1 0
[8,] 0 1
[9,] 0 1
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"
no yes
[1,] 1 0
[2,] 0 1
[3,] 0 1
[4,] 0 1
[5,] 0 1
[6,] 0 1
[7,] 0 1
[8,] 1 0
[9,] 0 1
[10,] 0 1
[11,] 0 1
[12,] 1 0
[13,] 1 0
[14,] 1 0
[15,] 0 1
[16,] 0 1
[17,] 0 1
[18,] 0 1
[19,] 0 1
[20,] 0 1
[21,] 1 0
[22,] 0 1
[23,] 0 1
[24,] 1 0
[25,] 0 1
[26,] 0 1
[27,] 0 1
[28,] 1 0
[29,] 0 1
[30,] 0 1
[31,] 1 0
[32,] 1 0
[33,] 1 0
[34,] 0 1
[35,] 0 1
[36,] 0 1
[37,] 1 0
[38,] 1 0
[39,] 1 0
[40,] 1 0
[41,] 0 1
[42,] 0 1
[43,] 1 0
[44,] 1 0
[45,] 1 0
[46,] 1 0
[47,] 1 0
[48,] 1 0
[49,] 1 0
[50,] 1 0
[51,] 0 1
[52,] 1 0
[53,] 0 1
[54,] 1 0
[55,] 0 1
[56,] 1 0
[57,] 1 0
[58,] 1 0
[59,] 1 0
[60,] 1 0
[61,] 1 0
[62,] 0 1
[63,] 0 1
[64,] 0 1
[65,] 1 0
[66,] 0 1
[67,] 0 1
[68,] 0 1
[69,] 1 0
[70,] 1 0
[71,] 0 1
[72,] 0 1
[73,] 0 1
[74,] 0 1
[75,] 0 1
[76,] 1 0
[77,] 1 0
[78,] 1 0
[79,] 1 0
[80,] 0 1
[81,] 1 0
[82,] 0 1
[83,] 1 0
[84,] 1 0
[85,] 0 1
[86,] 1 0
[87,] 1 0
[88,] 1 0
[89,] 1 0
[90,] 0 1
[91,] 1 0
[92,] 1 0
[93,] 1 0
[94,] 1 0
[95,] 1 0
[96,] 1 0
[97,] 0 1
[98,] 1 0
[99,] 0 1
[100,] 1 0
[101,] 1 0
[102,] 0 1
[103,] 1 0
[104,] 1 0
[105,] 0 1
[106,] 0 1
[107,] 1 0
[108,] 1 0
[109,] 1 0
[110,] 0 1
[111,] 0 1
[112,] 1 0
[113,] 0 1
[114,] 0 1
[115,] 0 1
[116,] 0 1
[117,] 0 1
[118,] 0 1
[119,] 1 0
[120,] 0 1
[121,] 1 0
[122,] 0 1
[123,] 1 0
[124,] 0 1
[125,] 0 1
[126,] 0 1
[127,] 1 0
[128,] 0 1
[129,] 0 1
[130,] 0 1
[131,] 0 1
[132,] 0 1
[133,] 0 1
[134,] 1 0
[135,] 1 0
[136,] 1 0
[137,] 1 0
[138,] 1 0
[139,] 1 0
[140,] 1 0
[141,] 0 1
[142,] 0 1
[143,] 0 1
[144,] 1 0
[145,] 0 1
[146,] 0 1
[147,] 0 1
[148,] 0 1
[149,] 0 1
[150,] 0 1
[151,] 1 0
[152,] 1 0
[153,] 0 1
[154,] 0 1
[155,] 1 0
[156,] 1 0
[157,] 0 1
[158,] 1 0
[159,] 0 1
[160,] 0 1
[161,] 1 0
[162,] 0 1
[163,] 0 1
[164,] 0 1
[165,] 0 1
[166,] 1 0
[167,] 1 0
[168,] 1 0
[169,] 0 1
[170,] 1 0
[171,] 0 1
[172,] 1 0
[173,] 1 0
[174,] 0 1
[175,] 0 1
[176,] 0 1
[177,] 0 1
[178,] 1 0
[179,] 0 1
[180,] 0 1
[181,] 0 1
[182,] 1 0
[183,] 0 1
[184,] 1 0
[185,] 1 0
[186,] 1 0
[187,] 0 1
[188,] 0 1
[189,] 1 0
[190,] 1 0
[191,] 0 1
[192,] 1 0
[193,] 0 1
[194,] 0 1
[195,] 0 1
[196,] 1 0
[197,] 1 0
[198,] 0 1
[199,] 0 1
[200,] 1 0
[201,] 0 1
[202,] 1 0
[203,] 0 1
[204,] 1 0
[205,] 0 1
[206,] 1 0
[207,] 0 1
[208,] 0 1
[209,] 1 0
[210,] 0 1
[211,] 1 0
[212,] 0 1
[213,] 0 1
[214,] 0 1
[215,] 1 0
[216,] 1 0
[217,] 1 0
[218,] 0 1
[219,] 0 1
[220,] 0 1
[221,] 1 0
[222,] 0 1
[223,] 1 0
[224,] 0 1
[225,] 1 0
[226,] 0 1
[227,] 0 1
[228,] 1 0
[229,] 1 0
[230,] 1 0
[231,] 1 0
[232,] 0 1
[233,] 1 0
[234,] 0 1
[235,] 0 1
[236,] 0 1
[237,] 1 0
[238,] 0 1
[239,] 0 1
[240,] 0 1
[241,] 1 0
[242,] 0 1
[243,] 0 1
[244,] 1 0
[245,] 1 0
[246,] 1 0
[247,] 0 1
[248,] 0 1
[249,] 1 0
[250,] 0 1
[251,] 1 0
[252,] 1 0
[253,] 1 0
[254,] 0 1
[255,] 1 0
[256,] 1 0
[257,] 1 0
[258,] 1 0
[259,] 0 1
[260,] 0 1
[261,] 0 1
[262,] 0 1
[263,] 1 0
[264,] 1 0
[265,] 1 0
[266,] 1 0
[267,] 1 0
[268,] 1 0
[269,] 1 0
[270,] 1 0
[271,] 0 1
[272,] 0 1
[273,] 0 1
[274,] 0 1
[275,] 1 0
[276,] 0 1
[277,] 0 1
[278,] 1 0
[279,] 0 1
[280,] 1 0
[281,] 0 1
[282,] 1 0
[283,] 0 1
[284,] 0 1
[285,] 0 1
[286,] 0 1
[287,] 0 1
[288,] 1 0
[289,] 0 1
[290,] 0 1
[291,] 0 1
[292,] 1 0
[293,] 0 1
[294,] 0 1
[295,] 0 1
[296,] 0 1
[297,] 1 0
[298,] 1 0
[299,] 1 0
[300,] 1 0
[301,] 0 1
[302,] 1 0
[303,] 0 1
[304,] 0 1
[305,] 0 1
[306,] 0 1
[307,] 0 1
[308,] 0 1
[309,] 1 0
[310,] 0 1
[311,] 1 0
[312,] 0 1
[313,] 0 1
[314,] 1 0
[315,] 0 1
[316,] 1 0
[317,] 0 1
[318,] 1 0
[319,] 0 1
[320,] 0 1
[321,] 1 0
[322,] 0 1
[323,] 0 1
[324,] 0 1
[325,] 0 1
[326,] 1 0
[327,] 0 1
[328,] 0 1
[329,] 1 0
[330,] 0 1
[331,] 0 1
[332,] 0 1
[333,] 0 1
[334,] 1 0
[335,] 0 1
[336,] 0 1
[337,] 1 0
[338,] 1 0
[339,] 0 1
[340,] 0 1
[341,] 0 1
[342,] 0 1
[343,] 0 1
[344,] 0 1
[345,] 1 0
[346,] 0 1
[347,] 1 0
[348,] 0 1
[349,] 0 1
[350,] 1 0
[351,] 0 1
[352,] 1 0
[353,] 1 0
[354,] 1 0
[355,] 0 1
[356,] 1 0
[357,] 0 1
[358,] 0 1
[359,] 0 1
[360,] 0 1
[361,] 0 1
[362,] 1 0
[363,] 0 1
[364,] 0 1
[365,] 1 0
[366,] 1 0
[367,] 1 0
[368,] 0 1
[369,] 0 1
[370,] 0 1
[371,] 0 1
[372,] 0 1
[373,] 0 1
[374,] 1 0
[375,] 0 1
[376,] 0 1
[377,] 0 1
[378,] 0 1
[379,] 1 0
[380,] 0 1
[381,] 0 1
[382,] 1 0
[383,] 0 1
[384,] 0 1
[385,] 0 1
[386,] 1 0
[387,] 0 1
[388,] 0 1
[389,] 0 1
[390,] 0 1
[391,] 0 1
[392,] 0 1
[393,] 0 1
[394,] 0 1
[395,] 0 1
[396,] 0 1
[397,] 1 0
[398,] 0 1
[399,] 1 0
[400,] 0 1
N no yes
Estimation 2396 1024.29000 1371.710000
Standard Error 0 54.16179 54.161793
CVE 0 5.28774 3.948487
DEFF NaN 1.00000 1.000000
N Income Employees Taxes
Estimation 2396 4.310823e+05 60792.51000 11114.445000
Standard Error 0 2.861642e+04 3847.71602 1090.885873
CVE 0 6.638273e+00 6.32926 9.815028
DEFF NaN 1.000000e+00 1.00000 1.000000
N Income Employees Taxes
Estimation 2396 6.210672e+05 88687.940000 17619.585000
Standard Error 0 3.276007e+04 4470.242148 1632.874048
CVE 0 5.274802e+00 5.040417 9.267381
DEFF NaN 1.000000e+00 1.000000 1.000000
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