{ "cells": [ { "cell_type": "markdown", "id": "caf2a04b-6684-499d-9a7e-41d86bbdef9d", "metadata": {}, "source": [ "# Workflow example with R2BEAT \n", "#### Scenario 2\n", "Together with a sampling frame containing the units of the population \n", "of reference, also a previous round of the sampling survey to be \n", "planned is available" ] }, { "cell_type": "code", "execution_count": 1, "id": "8447983d-253c-416a-9dc4-798c53ee2d07", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Caricamento del pacchetto richiesto: plyr\n", "\n", "Caricamento del pacchetto richiesto: devtools\n", "\n", "Caricamento del pacchetto richiesto: usethis\n", "\n", "Caricamento del pacchetto richiesto: sampling\n", "\n", "Caricamento del pacchetto richiesto: SamplingStrata\n", "\n", "Caricamento del pacchetto richiesto: memoise\n", "\n", "Caricamento del pacchetto richiesto: doParallel\n", "\n", "Caricamento del pacchetto richiesto: foreach\n", "\n", "Caricamento del pacchetto richiesto: iterators\n", "\n", "Caricamento del pacchetto richiesto: parallel\n", "\n", "Caricamento del pacchetto richiesto: pbapply\n", "\n", "Caricamento del pacchetto richiesto: formattable\n", "\n", "Caricamento del pacchetto richiesto: SamplingBigData\n", "\n", "\n", "\n", "Report issues at https://github.com/barcaroli/SamplingStrata/issues\n", "\n", "\n", "Get a complete documentation on https://barcaroli.github.io/SamplingStrata\n", "\n", "\n" ] } ], "source": [ "# Install last version of R2BEAT\n", "#devtools::install_github(\"barcaroli/R2BEAT\",dependencies = FALSE)\n", "library(R2BEAT)" ] }, { "cell_type": "code", "execution_count": 2, "id": "88e6a364-4d30-4752-81b2-2ee6d54df81a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1] '1.0.4'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "packageVersion(\"R2BEAT\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "faa91fdb-69a8-4c59-82f2-7c7749df2154", "metadata": {}, "outputs": [], "source": [ "## Sampling frame\n", "load(\"pop.RData\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "e55471c0-38cf-4f8f-a1e5-109bbf22733b", "metadata": {}, "outputs": [], "source": [ "## Sample data\n", "load(\"sample.RData\")" ] }, { "cell_type": "markdown", "id": "f1f603ae-5224-4350-ad0a-98aec36d9158", "metadata": {}, "source": [ "### Analysis of sampled data" ] }, { "cell_type": "code", "execution_count": 5, "id": "61171287-2d0d-4de6-9f46-52c1698b3a9a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "\n", "--------------------------------------------------------\n", "\n", "> The ReGenesees package has been successfully loaded. <\n", "\n", "--------------------------------------------------------\n", "\n", "\n", "\n", "\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Package: ReGenesees\n", "Type: Package\n", "Title: R Evolved Generalized Software for Sampling Estimates and Errors\n", " in Surveys\n", "Description: Design-Based and Model-Assisted analysis of complex\n", " sampling surveys. Multistage, stratified, clustered, unequally\n", " weighted survey designs. Horvitz-Thompson and Calibration\n", " Estimators. Variance Estimation for nonlinear smooth estimators\n", " by Taylor-series linearization. Estimates, standard errors,\n", " confidence intervals and design effects for: Totals, Means,\n", " absolute and relative Frequency Distributions (marginal,\n", " conditional and joint), Ratios, Shares and Ratios of Shares,\n", " Multiple Regression Coefficients and Quantiles. Automated\n", " Linearization of Complex Analytic Estimators. Design Covariance\n", " and Correlation. Estimates, standard errors, confidence\n", " intervals and design effects for user-defined analytic\n", " estimators. Estimates and sampling errors for subpopulations.\n", " Consistent trimming of calibration weights. Calibration on\n", " complex population parameters, e.g. multiple regression\n", " coefficients. Generalized Variance Functions (GVF) method for\n", " predicting variance estimates.\n", "Version: 2.1\n", "Author: Diego Zardetto [aut, cre]\n", "Maintainer: Diego Zardetto \n", "Authors@R: person(\"Diego\", \"Zardetto\", role = c(\"aut\", \"cre\"), email =\n", " \"zardetto@istat.it\")\n", "License: EUPL\n", "URL: https://diegozardetto.github.io/ReGenesees/,\n", " https://github.com/DiegoZardetto/ReGenesees/\n", "BugReports: https://github.com/DiegoZardetto/ReGenesees/issues/\n", "Imports: stats, MASS\n", "Depends: R (>= 2.14.0)\n", "ByteCompile: TRUE\n", "RemoteType: github\n", "RemoteHost: api.github.com\n", "RemoteRepo: ReGenesees\n", "RemoteUsername: DiegoZardetto\n", "RemoteRef: HEAD\n", "RemoteSha: 054413befcf905cd6ab06611b819a1295f7a5b20\n", "GithubRepo: ReGenesees\n", "GithubUsername: DiegoZardetto\n", "GithubRef: HEAD\n", "GithubSHA1: 054413befcf905cd6ab06611b819a1295f7a5b20\n", "NeedsCompilation: no\n", "Packaged: 2021-08-11 17:21:59 UTC; UTENTE\n", "Built: R 4.1.0; ; 2021-08-11 17:22:03 UTC; windows\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "# Install ReGenesees\n", "#devtools::install_github(\"DiegoZardetto/ReGenesees\")\n", "library(ReGenesees)" ] }, { "cell_type": "code", "execution_count": 6, "id": "459690c3-107c-48a0-96fe-e48a2d59692a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Empty levels found in factors: id_hh\n", "# Empty levels have been dropped!\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in e.svydesign(sample, ids = ~municipality + id_hh, strata = ~stratum_2, :\n", "\"Sampling variance estimation for this design will take into account only leading contributions, i.e. PSUs in not-SR strata and SSUs in SR strata (see ?e.svydesign and ?ReGenesees.options for details)\"\n" ] } ], "source": [ "## Sample design description\n", "sample$stratum_2 <- as.factor(sample$stratum_2)\n", "sample.des <- e.svydesign(sample, \n", " ids= ~ municipality + id_hh, \n", " strata = ~ stratum_2, \n", " weights = ~ weight,\n", " self.rep.str = ~ SR,\n", " check.data = TRUE)" ] }, { "cell_type": "code", "execution_count": 7, "id": "9b4a5ab2-b574-48f8-9bf2-0f12926ca0e0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# All lonely strata (112) successfully collapsed!\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning message in collapse.strata(sample.des):\n", "\"No similarity score specified: achieved strata aggregation depends on the ordering of sample data\"\n" ] } ], "source": [ "## Find and collapse lonely strata\n", "ls <- find.lon.strata(sample.des)\n", "sample.des <- collapse.strata(sample.des)" ] }, { "cell_type": "code", "execution_count": 8, "id": "71e04c72-9272-4f3e-9ea9-0d695696ee06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Coherence check between 'universe' and 'template': OK\n", "\n" ] } ], "source": [ "## Calibration with known totals\n", "totals <- pop.template(sample.des,\n", " calmodel = ~ sex : cl_age, \n", " partition = ~ region)\n", "totals <- fill.template(pop, totals, mem.frac = 10)\n", "sample.cal <- e.calibrate(sample.des, \n", " totals,\n", " calmodel = ~ sex : cl_age, \n", " partition = ~ region,\n", " calfun = \"logit\",\n", " bounds = c(0.3, 2.6), \n", " aggregate.stage = 2,\n", " force = FALSE)" ] }, { "cell_type": "markdown", "id": "76496a7a-8dee-45d2-9f0e-e037ae92c0cb", "metadata": {}, "source": [ "### Preparation of inputs for allocation steps" ] }, { "cell_type": "code", "execution_count": 9, "id": "3a1c235a-fce7-4bb6-9908-495e3e27d9b2", "metadata": {}, "outputs": [], "source": [ "## Preparation of inputs for allocation steps\n", "samp_frame <- pop\n", "RGdes <- sample.des\n", "RGcal <- sample.cal\n", "strata_vars <- c(\"stratum\") \n", "target_vars <- c(\"income_hh\",\n", " \"active\",\n", " \"inactive\",\n", " \"unemployed\") \n", "weight_var <- \"weight\"\n", "deff_vars <- \"stratum\" \n", "id_PSU <- c(\"municipality\") \n", "id_SSU <- c(\"id_hh\") \n", "domain_vars <- c(\"region\") \n", "delta <- 1 \n", "minimum <- 50 \n", "\n", "inp <- prepareInputToAllocation2(\n", " samp_frame, # sampling frame\n", " RGdes, # ReGenesees design object\n", " RGcal, # ReGenesees calibrated object\n", " id_PSU, # identification variable of PSUs\n", " id_SSU, # identification variable of SSUs\n", " strata_vars, # strata variables\n", " target_vars, # target variables\n", " deff_vars, # deff variables\n", " domain_vars, # domain variables\n", " delta, # Average number of SSUs for each selection unit\n", " minimum # Minimum number of SSUs to be selected in each PSU\n", " )" ] }, { "cell_type": "code", "execution_count": 10, "id": "184e48ce-f23e-477d-9d01-61158305bdfa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 15
stratumSTRATUMNM1M2M3M4S1S2S3S4COSTCENSDOM1DOM2
<fct><chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><fct>
11000 1000 19676923339.700.68016790.21275960.1070724716543.720.46641130.40925900.3092054101center
2100001000010605729340.380.77933180.20474300.0159252425031.440.41469720.40351370.1251864101north
3110001100020583927822.700.78142280.20295220.0156249326050.400.41328100.40219720.1240193101north
41200012000 5760623110.900.76325220.20795300.0287948515405.510.42508620.40584300.1672295101north
5130001300010280128185.380.75166700.21422380.0341092024393.710.43204600.41028280.1815097101north
61400014000 8407724787.120.75372320.21315300.0331238517403.580.43084170.40953480.1789599101north
\n" ], "text/latex": [ "A data.frame: 6 × 15\n", "\\begin{tabular}{r|lllllllllllllll}\n", " & stratum & STRATUM & N & M1 & M2 & M3 & M4 & S1 & S2 & S3 & S4 & COST & CENS & DOM1 & DOM2\\\\\n", " & & & & & & & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1000 & 1000 & 196769 & 23339.70 & 0.6801679 & 0.2127596 & 0.10707247 & 16543.72 & 0.4664113 & 0.4092590 & 0.3092054 & 1 & 0 & 1 & center\\\\\n", "\t2 & 10000 & 10000 & 106057 & 29340.38 & 0.7793318 & 0.2047430 & 0.01592524 & 25031.44 & 0.4146972 & 0.4035137 & 0.1251864 & 1 & 0 & 1 & north \\\\\n", "\t3 & 11000 & 11000 & 205839 & 27822.70 & 0.7814228 & 0.2029522 & 0.01562493 & 26050.40 & 0.4132810 & 0.4021972 & 0.1240193 & 1 & 0 & 1 & north \\\\\n", "\t4 & 12000 & 12000 & 57606 & 23110.90 & 0.7632522 & 0.2079530 & 0.02879485 & 15405.51 & 0.4250862 & 0.4058430 & 0.1672295 & 1 & 0 & 1 & north \\\\\n", "\t5 & 13000 & 13000 & 102801 & 28185.38 & 0.7516670 & 0.2142238 & 0.03410920 & 24393.71 & 0.4320460 & 0.4102828 & 0.1815097 & 1 & 0 & 1 & north \\\\\n", "\t6 & 14000 & 14000 & 84077 & 24787.12 & 0.7537232 & 0.2131530 & 0.03312385 & 17403.58 & 0.4308417 & 0.4095348 & 0.1789599 & 1 & 0 & 1 & north \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 15\n", "\n", "| | stratum <fct> | STRATUM <chr> | N <dbl> | M1 <dbl> | M2 <dbl> | M3 <dbl> | M4 <dbl> | S1 <dbl> | S2 <dbl> | S3 <dbl> | S4 <dbl> | COST <dbl> | CENS <dbl> | DOM1 <dbl> | DOM2 <fct> |\n", "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1000 | 1000 | 196769 | 23339.70 | 0.6801679 | 0.2127596 | 0.10707247 | 16543.72 | 0.4664113 | 0.4092590 | 0.3092054 | 1 | 0 | 1 | center |\n", "| 2 | 10000 | 10000 | 106057 | 29340.38 | 0.7793318 | 0.2047430 | 0.01592524 | 25031.44 | 0.4146972 | 0.4035137 | 0.1251864 | 1 | 0 | 1 | north |\n", "| 3 | 11000 | 11000 | 205839 | 27822.70 | 0.7814228 | 0.2029522 | 0.01562493 | 26050.40 | 0.4132810 | 0.4021972 | 0.1240193 | 1 | 0 | 1 | north |\n", "| 4 | 12000 | 12000 | 57606 | 23110.90 | 0.7632522 | 0.2079530 | 0.02879485 | 15405.51 | 0.4250862 | 0.4058430 | 0.1672295 | 1 | 0 | 1 | north |\n", "| 5 | 13000 | 13000 | 102801 | 28185.38 | 0.7516670 | 0.2142238 | 0.03410920 | 24393.71 | 0.4320460 | 0.4102828 | 0.1815097 | 1 | 0 | 1 | north |\n", "| 6 | 14000 | 14000 | 84077 | 24787.12 | 0.7537232 | 0.2131530 | 0.03312385 | 17403.58 | 0.4308417 | 0.4095348 | 0.1789599 | 1 | 0 | 1 | north |\n", "\n" ], "text/plain": [ " stratum STRATUM N M1 M2 M3 M4 S1 \n", "1 1000 1000 196769 23339.70 0.6801679 0.2127596 0.10707247 16543.72\n", "2 10000 10000 106057 29340.38 0.7793318 0.2047430 0.01592524 25031.44\n", "3 11000 11000 205839 27822.70 0.7814228 0.2029522 0.01562493 26050.40\n", "4 12000 12000 57606 23110.90 0.7632522 0.2079530 0.02879485 15405.51\n", "5 13000 13000 102801 28185.38 0.7516670 0.2142238 0.03410920 24393.71\n", "6 14000 14000 84077 24787.12 0.7537232 0.2131530 0.03312385 17403.58\n", " S2 S3 S4 COST CENS DOM1 DOM2 \n", "1 0.4664113 0.4092590 0.3092054 1 0 1 center\n", "2 0.4146972 0.4035137 0.1251864 1 0 1 north \n", "3 0.4132810 0.4021972 0.1240193 1 0 1 north \n", "4 0.4250862 0.4058430 0.1672295 1 0 1 north \n", "5 0.4320460 0.4102828 0.1815097 1 0 1 north \n", "6 0.4308417 0.4095348 0.1789599 1 0 1 north " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$strata)" ] }, { "cell_type": "code", "execution_count": 11, "id": "48677dc4-b8e0-4a60-b5e2-1cb815a916ab", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 7
stratumSTRATUMDEFF1DEFF2DEFF3DEFF4b_nar
<fct><chr><dbl><dbl><dbl><dbl><dbl>
11000 1000 1.0021411.0034871.0185080.998091 254.50000
210000100001.0198201.0293621.0103201.000982 178.83333
311000110001.1286621.0368821.0020391.115932 52.07500
412000120003.2339420.9784191.2028420.639357 49.42857
513000130001.0633731.0568111.0157561.0489381285.00000
614000140001.0188011.0031731.0022721.013573 263.50000
\n" ], "text/latex": [ "A data.frame: 6 × 7\n", "\\begin{tabular}{r|lllllll}\n", " & stratum & STRATUM & DEFF1 & DEFF2 & DEFF3 & DEFF4 & b\\_nar\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1000 & 1000 & 1.002141 & 1.003487 & 1.018508 & 0.998091 & 254.50000\\\\\n", "\t2 & 10000 & 10000 & 1.019820 & 1.029362 & 1.010320 & 1.000982 & 178.83333\\\\\n", "\t3 & 11000 & 11000 & 1.128662 & 1.036882 & 1.002039 & 1.115932 & 52.07500\\\\\n", "\t4 & 12000 & 12000 & 3.233942 & 0.978419 & 1.202842 & 0.639357 & 49.42857\\\\\n", "\t5 & 13000 & 13000 & 1.063373 & 1.056811 & 1.015756 & 1.048938 & 1285.00000\\\\\n", "\t6 & 14000 & 14000 & 1.018801 & 1.003173 & 1.002272 & 1.013573 & 263.50000\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 7\n", "\n", "| | stratum <fct> | STRATUM <chr> | DEFF1 <dbl> | DEFF2 <dbl> | DEFF3 <dbl> | DEFF4 <dbl> | b_nar <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| 1 | 1000 | 1000 | 1.002141 | 1.003487 | 1.018508 | 0.998091 | 254.50000 |\n", "| 2 | 10000 | 10000 | 1.019820 | 1.029362 | 1.010320 | 1.000982 | 178.83333 |\n", "| 3 | 11000 | 11000 | 1.128662 | 1.036882 | 1.002039 | 1.115932 | 52.07500 |\n", "| 4 | 12000 | 12000 | 3.233942 | 0.978419 | 1.202842 | 0.639357 | 49.42857 |\n", "| 5 | 13000 | 13000 | 1.063373 | 1.056811 | 1.015756 | 1.048938 | 1285.00000 |\n", "| 6 | 14000 | 14000 | 1.018801 | 1.003173 | 1.002272 | 1.013573 | 263.50000 |\n", "\n" ], "text/plain": [ " stratum STRATUM DEFF1 DEFF2 DEFF3 DEFF4 b_nar \n", "1 1000 1000 1.002141 1.003487 1.018508 0.998091 254.50000\n", "2 10000 10000 1.019820 1.029362 1.010320 1.000982 178.83333\n", "3 11000 11000 1.128662 1.036882 1.002039 1.115932 52.07500\n", "4 12000 12000 3.233942 0.978419 1.202842 0.639357 49.42857\n", "5 13000 13000 1.063373 1.056811 1.015756 1.048938 1285.00000\n", "6 14000 14000 1.018801 1.003173 1.002272 1.013573 263.50000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$deff)" ] }, { "cell_type": "code", "execution_count": 12, "id": "9a3019d8-71d7-4d38-ac61-7f28c03884f3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 6
stratumSTRATUMEFFST1EFFST2EFFST3EFFST4
<fct><chr><dbl><dbl><dbl><dbl>
11000 1000 0.98753970.86477550.75654981.0033213
210000100000.99485990.90765450.89826991.0054137
311000110000.97654040.81360850.78352240.9925166
412000120001.01455650.91135900.91269091.0007101
513000130001.00459110.92631700.91805020.9942647
614000140001.00167450.94713180.93757880.9967146
\n" ], "text/latex": [ "A data.frame: 6 × 6\n", "\\begin{tabular}{r|llllll}\n", " & stratum & STRATUM & EFFST1 & EFFST2 & EFFST3 & EFFST4\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t1 & 1000 & 1000 & 0.9875397 & 0.8647755 & 0.7565498 & 1.0033213\\\\\n", "\t2 & 10000 & 10000 & 0.9948599 & 0.9076545 & 0.8982699 & 1.0054137\\\\\n", "\t3 & 11000 & 11000 & 0.9765404 & 0.8136085 & 0.7835224 & 0.9925166\\\\\n", "\t4 & 12000 & 12000 & 1.0145565 & 0.9113590 & 0.9126909 & 1.0007101\\\\\n", "\t5 & 13000 & 13000 & 1.0045911 & 0.9263170 & 0.9180502 & 0.9942647\\\\\n", "\t6 & 14000 & 14000 & 1.0016745 & 0.9471318 & 0.9375788 & 0.9967146\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 6\n", "\n", "| | stratum <fct> | STRATUM <chr> | EFFST1 <dbl> | EFFST2 <dbl> | EFFST3 <dbl> | EFFST4 <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| 1 | 1000 | 1000 | 0.9875397 | 0.8647755 | 0.7565498 | 1.0033213 |\n", "| 2 | 10000 | 10000 | 0.9948599 | 0.9076545 | 0.8982699 | 1.0054137 |\n", "| 3 | 11000 | 11000 | 0.9765404 | 0.8136085 | 0.7835224 | 0.9925166 |\n", "| 4 | 12000 | 12000 | 1.0145565 | 0.9113590 | 0.9126909 | 1.0007101 |\n", "| 5 | 13000 | 13000 | 1.0045911 | 0.9263170 | 0.9180502 | 0.9942647 |\n", "| 6 | 14000 | 14000 | 1.0016745 | 0.9471318 | 0.9375788 | 0.9967146 |\n", "\n" ], "text/plain": [ " stratum STRATUM EFFST1 EFFST2 EFFST3 EFFST4 \n", "1 1000 1000 0.9875397 0.8647755 0.7565498 1.0033213\n", "2 10000 10000 0.9948599 0.9076545 0.8982699 1.0054137\n", "3 11000 11000 0.9765404 0.8136085 0.7835224 0.9925166\n", "4 12000 12000 1.0145565 0.9113590 0.9126909 1.0007101\n", "5 13000 13000 1.0045911 0.9263170 0.9180502 0.9942647\n", "6 14000 14000 1.0016745 0.9471318 0.9375788 0.9967146" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$effst)" ] }, { "cell_type": "code", "execution_count": 13, "id": "19a4cd0c-7049-4be4-b61c-832b4a63c6e9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 9
STRATUMRHO_AR1RHO_NAR1RHO_AR2RHO_NAR2RHO_AR3RHO_NAR3RHO_AR4RHO_NAR4
<chr><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
11000 10.0000084457591 0.0000137554210.0000730098621-0.000007530572
21000010.0001114526711 0.0001651096510.0000580318651 0.000005522024
31100010.0025190797851 0.0007221145410.0000399216841 0.002269838473
41200010.0461285958701-0.0004456253710.0041884778761-0.007446905605
51300010.0000493559191 0.0000442453310.0000122710281 0.000038113707
61400010.0000716228571 0.0000120876210.0000086552381 0.000051706667
\n" ], "text/latex": [ "A data.frame: 6 × 9\n", "\\begin{tabular}{r|lllllllll}\n", " & STRATUM & RHO\\_AR1 & RHO\\_NAR1 & RHO\\_AR2 & RHO\\_NAR2 & RHO\\_AR3 & RHO\\_NAR3 & RHO\\_AR4 & RHO\\_NAR4\\\\\n", " & & & & & & & & & \\\\\n", "\\hline\n", "\t1 & 1000 & 1 & 0.000008445759 & 1 & 0.00001375542 & 1 & 0.000073009862 & 1 & -0.000007530572\\\\\n", "\t2 & 10000 & 1 & 0.000111452671 & 1 & 0.00016510965 & 1 & 0.000058031865 & 1 & 0.000005522024\\\\\n", "\t3 & 11000 & 1 & 0.002519079785 & 1 & 0.00072211454 & 1 & 0.000039921684 & 1 & 0.002269838473\\\\\n", "\t4 & 12000 & 1 & 0.046128595870 & 1 & -0.00044562537 & 1 & 0.004188477876 & 1 & -0.007446905605\\\\\n", "\t5 & 13000 & 1 & 0.000049355919 & 1 & 0.00004424533 & 1 & 0.000012271028 & 1 & 0.000038113707\\\\\n", "\t6 & 14000 & 1 & 0.000071622857 & 1 & 0.00001208762 & 1 & 0.000008655238 & 1 & 0.000051706667\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 9\n", "\n", "| | STRATUM <chr> | RHO_AR1 <dbl> | RHO_NAR1 <dbl> | RHO_AR2 <dbl> | RHO_NAR2 <dbl> | RHO_AR3 <dbl> | RHO_NAR3 <dbl> | RHO_AR4 <dbl> | RHO_NAR4 <dbl> |\n", "|---|---|---|---|---|---|---|---|---|---|\n", "| 1 | 1000 | 1 | 0.000008445759 | 1 | 0.00001375542 | 1 | 0.000073009862 | 1 | -0.000007530572 |\n", "| 2 | 10000 | 1 | 0.000111452671 | 1 | 0.00016510965 | 1 | 0.000058031865 | 1 | 0.000005522024 |\n", "| 3 | 11000 | 1 | 0.002519079785 | 1 | 0.00072211454 | 1 | 0.000039921684 | 1 | 0.002269838473 |\n", "| 4 | 12000 | 1 | 0.046128595870 | 1 | -0.00044562537 | 1 | 0.004188477876 | 1 | -0.007446905605 |\n", "| 5 | 13000 | 1 | 0.000049355919 | 1 | 0.00004424533 | 1 | 0.000012271028 | 1 | 0.000038113707 |\n", "| 6 | 14000 | 1 | 0.000071622857 | 1 | 0.00001208762 | 1 | 0.000008655238 | 1 | 0.000051706667 |\n", "\n" ], "text/plain": [ " STRATUM RHO_AR1 RHO_NAR1 RHO_AR2 RHO_NAR2 RHO_AR3 RHO_NAR3 \n", "1 1000 1 0.000008445759 1 0.00001375542 1 0.000073009862\n", "2 10000 1 0.000111452671 1 0.00016510965 1 0.000058031865\n", "3 11000 1 0.002519079785 1 0.00072211454 1 0.000039921684\n", "4 12000 1 0.046128595870 1 -0.00044562537 1 0.004188477876\n", "5 13000 1 0.000049355919 1 0.00004424533 1 0.000012271028\n", "6 14000 1 0.000071622857 1 0.00001208762 1 0.000008655238\n", " RHO_AR4 RHO_NAR4 \n", "1 1 -0.000007530572\n", "2 1 0.000005522024\n", "3 1 0.002269838473\n", "4 1 -0.007446905605\n", "5 1 0.000038113707\n", "6 1 0.000051706667" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$rho)" ] }, { "cell_type": "code", "execution_count": 14, "id": "6bd08c00-f0c4-4e3b-bd2a-641ac1f260d6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 3
PSU_IDSTRATUMPSU_MOS
<dbl><fct><dbl>
13091000 50845
23301000146162
32922000 24794
42932000 19609
53002000 13897
63042000 36195
\n" ], "text/latex": [ "A data.frame: 6 × 3\n", "\\begin{tabular}{r|lll}\n", " & PSU\\_ID & STRATUM & PSU\\_MOS\\\\\n", " & & & \\\\\n", "\\hline\n", "\t1 & 309 & 1000 & 50845\\\\\n", "\t2 & 330 & 1000 & 146162\\\\\n", "\t3 & 292 & 2000 & 24794\\\\\n", "\t4 & 293 & 2000 & 19609\\\\\n", "\t5 & 300 & 2000 & 13897\\\\\n", "\t6 & 304 & 2000 & 36195\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 3\n", "\n", "| | PSU_ID <dbl> | STRATUM <fct> | PSU_MOS <dbl> |\n", "|---|---|---|---|\n", "| 1 | 309 | 1000 | 50845 |\n", "| 2 | 330 | 1000 | 146162 |\n", "| 3 | 292 | 2000 | 24794 |\n", "| 4 | 293 | 2000 | 19609 |\n", "| 5 | 300 | 2000 | 13897 |\n", "| 6 | 304 | 2000 | 36195 |\n", "\n" ], "text/plain": [ " PSU_ID STRATUM PSU_MOS\n", "1 309 1000 50845 \n", "2 330 1000 146162 \n", "3 292 2000 24794 \n", "4 293 2000 19609 \n", "5 300 2000 13897 \n", "6 304 2000 36195 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$psu_file)" ] }, { "cell_type": "code", "execution_count": 15, "id": "f41c4eeb-0fb0-46d2-8d5c-ead1bfc5bb89", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 4
STRATUMSTRAT_MOSDELTAMINIMUM
<fct><dbl><dbl><dbl>
11000197007150
22000261456150
33000115813150
44000 17241150
55000101067150
66000 47218150
\n" ], "text/latex": [ "A data.frame: 6 × 4\n", "\\begin{tabular}{r|llll}\n", " & STRATUM & STRAT\\_MOS & DELTA & MINIMUM\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t1 & 1000 & 197007 & 1 & 50\\\\\n", "\t2 & 2000 & 261456 & 1 & 50\\\\\n", "\t3 & 3000 & 115813 & 1 & 50\\\\\n", "\t4 & 4000 & 17241 & 1 & 50\\\\\n", "\t5 & 5000 & 101067 & 1 & 50\\\\\n", "\t6 & 6000 & 47218 & 1 & 50\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 4\n", "\n", "| | STRATUM <fct> | STRAT_MOS <dbl> | DELTA <dbl> | MINIMUM <dbl> |\n", "|---|---|---|---|---|\n", "| 1 | 1000 | 197007 | 1 | 50 |\n", "| 2 | 2000 | 261456 | 1 | 50 |\n", "| 3 | 3000 | 115813 | 1 | 50 |\n", "| 4 | 4000 | 17241 | 1 | 50 |\n", "| 5 | 5000 | 101067 | 1 | 50 |\n", "| 6 | 6000 | 47218 | 1 | 50 |\n", "\n" ], "text/plain": [ " STRATUM STRAT_MOS DELTA MINIMUM\n", "1 1000 197007 1 50 \n", "2 2000 261456 1 50 \n", "3 3000 115813 1 50 \n", "4 4000 17241 1 50 \n", "5 5000 101067 1 50 \n", "6 6000 47218 1 50 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head(inp$des_file)" ] }, { "cell_type": "markdown", "id": "38ef19d1-4370-489c-9c91-d285a25f70b2", "metadata": {}, "source": [ "## Allocation" ] }, { "cell_type": "code", "execution_count": 16, "id": "5a084351-70c4-4515-9a70-e5a3e5092e46", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\n", "
A data.frame: 2 × 5
DOMCV1CV2CV3CV4
<chr><dbl><dbl><dbl><dbl>
DOM10.020.030.030.03
DOM20.030.060.060.06
\n" ], "text/latex": [ "A data.frame: 2 × 5\n", "\\begin{tabular}{lllll}\n", " DOM & CV1 & CV2 & CV3 & CV4\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t DOM1 & 0.02 & 0.03 & 0.03 & 0.03\\\\\n", "\t DOM2 & 0.03 & 0.06 & 0.06 & 0.06\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 5\n", "\n", "| DOM <chr> | CV1 <dbl> | CV2 <dbl> | CV3 <dbl> | CV4 <dbl> |\n", "|---|---|---|---|---|\n", "| DOM1 | 0.02 | 0.03 | 0.03 | 0.03 |\n", "| DOM2 | 0.03 | 0.06 | 0.06 | 0.06 |\n", "\n" ], "text/plain": [ " DOM CV1 CV2 CV3 CV4 \n", "1 DOM1 0.02 0.03 0.03 0.03\n", "2 DOM2 0.03 0.06 0.06 0.06" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Precision constraints\n", "cv <- as.data.frame(list(DOM=c(\"DOM1\",\"DOM2\"),\n", " CV1=c(0.02,0.03),\n", " CV2=c(0.03,0.06),\n", " CV3=c(0.03,0.06),\n", " CV4=c(0.03,0.06)))\n", "cv" ] }, { "cell_type": "code", "execution_count": 17, "id": "af3c8bf0-6d3c-4d7e-a2ad-79010a9e1f83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " iterations PSU_SR PSU NSR PSU Total SSU\n", "1 0 0 0 0 13512\n", "2 1 78 67 145 13209\n", "3 2 44 124 168 13016\n", "4 3 43 123 166 13011\n" ] } ], "source": [ "alloc <- beat.2st(stratif = inp$strata, \n", " errors = cv, \n", " des_file = inp$des_file, \n", " psu_file = inp$psu_file, \n", " rho = inp$rho, \n", " deft_start = NULL, \n", " effst = inp$effst,\n", " epsilon1 = 5, \n", " mmdiff_deft = 1,\n", " maxi = 15, \n", " epsilon = 10^(-11), \n", " minnumstrat = 2, \n", " maxiter = 200, \n", " maxiter1 = 25)" ] }, { "cell_type": "markdown", "id": "66ec790f-f0ba-4920-bf77-42e9f1a8a156", "metadata": {}, "source": [ "## Selection of PSUs (I stage)" ] }, { "cell_type": "code", "execution_count": 18, "id": "12318460-beb6-451e-8b51-6fee22562a0f", "metadata": {}, "outputs": [], "source": [ "allocat <- alloc$alloc[-nrow(alloc$alloc),]\n", "set.seed(1234)\n", "sample_2st <- StratSel(dataPop= inp$psu_file,\n", " idpsu= ~ PSU_ID, \n", " dom= ~ STRATUM, \n", " final_pop= ~ PSU_MOS, \n", " size= ~ PSU_MOS, \n", " PSUsamplestratum= 1, \n", " min_sample= minimum, \n", " min_sample_index= FALSE, \n", " dataAll=allocat,\n", " domAll= ~ factor(STRATUM), \n", " f_sample= ~ ALLOC, \n", " planned_min_sample= NULL, \n", " launch= F)" ] }, { "cell_type": "code", "execution_count": 19, "id": "506c1e9f-b636-4f3d-914b-e2ba2be406b8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 26 × 6
DomainSRdomnSRdomSRdom+nSRdomSR_PSU_final_sample_unitNSR_PSU_final_sample_unit
<chr><chr><chr><chr><chr><chr>
1000 2 0 2 423 0
2000 5 4 9 288 235
3000 0 5 5 0 247
4000 0 1 1 0 2
5000 2 0 2 281 0
6000 1 1 2 43 66
7000 0 1 1 0 56
8000 0 1 1 0 35
9000 1 0 1 911 0
100006 0 6 936 0
11000162036 761 1091
120000 1111 0 537
130001 0 1 12980
140004 0 4 10490
15000281038 1629627
160000 2727 0 1333
170001 0 1 141 0
180000 3 3 0 134
190000 6 6 0 320
200000 3 3 0 166
210001 0 1 130 0
220001 1 2 41 68
230000 3 3 0 165
240000 1 1 0 2
Total699816779315084
Mean 330 212
\n" ], "text/latex": [ "A data.frame: 26 × 6\n", "\\begin{tabular}{llllll}\n", " Domain & SRdom & nSRdom & SRdom+nSRdom & SR\\_PSU\\_final\\_sample\\_unit & NSR\\_PSU\\_final\\_sample\\_unit\\\\\n", " & & & & & \\\\\n", "\\hline\n", "\t 1000 & 2 & 0 & 2 & 423 & 0 \\\\\n", "\t 2000 & 5 & 4 & 9 & 288 & 235 \\\\\n", "\t 3000 & 0 & 5 & 5 & 0 & 247 \\\\\n", "\t 4000 & 0 & 1 & 1 & 0 & 2 \\\\\n", "\t 5000 & 2 & 0 & 2 & 281 & 0 \\\\\n", "\t 6000 & 1 & 1 & 2 & 43 & 66 \\\\\n", "\t 7000 & 0 & 1 & 1 & 0 & 56 \\\\\n", "\t 8000 & 0 & 1 & 1 & 0 & 35 \\\\\n", "\t 9000 & 1 & 0 & 1 & 911 & 0 \\\\\n", "\t 10000 & 6 & 0 & 6 & 936 & 0 \\\\\n", "\t 11000 & 16 & 20 & 36 & 761 & 1091\\\\\n", "\t 12000 & 0 & 11 & 11 & 0 & 537 \\\\\n", "\t 13000 & 1 & 0 & 1 & 1298 & 0 \\\\\n", "\t 14000 & 4 & 0 & 4 & 1049 & 0 \\\\\n", "\t 15000 & 28 & 10 & 38 & 1629 & 627 \\\\\n", "\t 16000 & 0 & 27 & 27 & 0 & 1333\\\\\n", "\t 17000 & 1 & 0 & 1 & 141 & 0 \\\\\n", "\t 18000 & 0 & 3 & 3 & 0 & 134 \\\\\n", "\t 19000 & 0 & 6 & 6 & 0 & 320 \\\\\n", "\t 20000 & 0 & 3 & 3 & 0 & 166 \\\\\n", "\t 21000 & 1 & 0 & 1 & 130 & 0 \\\\\n", "\t 22000 & 1 & 1 & 2 & 41 & 68 \\\\\n", "\t 23000 & 0 & 3 & 3 & 0 & 165 \\\\\n", "\t 24000 & 0 & 1 & 1 & 0 & 2 \\\\\n", "\t Total & 69 & 98 & 167 & 7931 & 5084\\\\\n", "\t Mean & & & & 330 & 212 \\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 26 × 6\n", "\n", "| Domain <chr> | SRdom <chr> | nSRdom <chr> | SRdom+nSRdom <chr> | SR_PSU_final_sample_unit <chr> | NSR_PSU_final_sample_unit <chr> |\n", "|---|---|---|---|---|---|\n", "| 1000 | 2 | 0 | 2 | 423 | 0 |\n", "| 2000 | 5 | 4 | 9 | 288 | 235 |\n", "| 3000 | 0 | 5 | 5 | 0 | 247 |\n", "| 4000 | 0 | 1 | 1 | 0 | 2 |\n", "| 5000 | 2 | 0 | 2 | 281 | 0 |\n", "| 6000 | 1 | 1 | 2 | 43 | 66 |\n", "| 7000 | 0 | 1 | 1 | 0 | 56 |\n", "| 8000 | 0 | 1 | 1 | 0 | 35 |\n", "| 9000 | 1 | 0 | 1 | 911 | 0 |\n", "| 10000 | 6 | 0 | 6 | 936 | 0 |\n", "| 11000 | 16 | 20 | 36 | 761 | 1091 |\n", "| 12000 | 0 | 11 | 11 | 0 | 537 |\n", "| 13000 | 1 | 0 | 1 | 1298 | 0 |\n", "| 14000 | 4 | 0 | 4 | 1049 | 0 |\n", "| 15000 | 28 | 10 | 38 | 1629 | 627 |\n", "| 16000 | 0 | 27 | 27 | 0 | 1333 |\n", "| 17000 | 1 | 0 | 1 | 141 | 0 |\n", "| 18000 | 0 | 3 | 3 | 0 | 134 |\n", "| 19000 | 0 | 6 | 6 | 0 | 320 |\n", "| 20000 | 0 | 3 | 3 | 0 | 166 |\n", "| 21000 | 1 | 0 | 1 | 130 | 0 |\n", "| 22000 | 1 | 1 | 2 | 41 | 68 |\n", "| 23000 | 0 | 3 | 3 | 0 | 165 |\n", "| 24000 | 0 | 1 | 1 | 0 | 2 |\n", "| Total | 69 | 98 | 167 | 7931 | 5084 |\n", "| Mean | | | | 330 | 212 |\n", "\n" ], "text/plain": [ " Domain SRdom nSRdom SRdom+nSRdom SR_PSU_final_sample_unit\n", "1 1000 2 0 2 423 \n", "2 2000 5 4 9 288 \n", "3 3000 0 5 5 0 \n", "4 4000 0 1 1 0 \n", "5 5000 2 0 2 281 \n", "6 6000 1 1 2 43 \n", "7 7000 0 1 1 0 \n", "8 8000 0 1 1 0 \n", "9 9000 1 0 1 911 \n", "10 10000 6 0 6 936 \n", "11 11000 16 20 36 761 \n", "12 12000 0 11 11 0 \n", "13 13000 1 0 1 1298 \n", "14 14000 4 0 4 1049 \n", "15 15000 28 10 38 1629 \n", "16 16000 0 27 27 0 \n", "17 17000 1 0 1 141 \n", "18 18000 0 3 3 0 \n", "19 19000 0 6 6 0 \n", "20 20000 0 3 3 0 \n", "21 21000 1 0 1 130 \n", "22 22000 1 1 2 41 \n", "23 23000 0 3 3 0 \n", "24 24000 0 1 1 0 \n", "25 Total 69 98 167 7931 \n", "26 Mean 330 \n", " NSR_PSU_final_sample_unit\n", "1 0 \n", "2 235 \n", "3 247 \n", "4 2 \n", "5 0 \n", "6 66 \n", "7 56 \n", "8 35 \n", "9 0 \n", "10 0 \n", "11 1091 \n", "12 537 \n", "13 0 \n", "14 0 \n", "15 627 \n", "16 1333 \n", "17 0 \n", "18 134 \n", "19 320 \n", "20 166 \n", "21 0 \n", "22 68 \n", "23 165 \n", "24 2 \n", "25 5084 \n", "26 212 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample_2st[[2]]" ] }, { "cell_type": "code", "execution_count": 20, "id": "1c30c165-2ed7-4532-8632-e15764355639", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "Plot with title \"SSUs by strata\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "## Plot of allocation (PSUs and SSUs)\n", "des <- sample_2st[[2]]\n", "des2 <- NULL\n", "des2$strata <- c(des$Domain[1:24],des$Domain[1:24])\n", "des2$SR <- c(rep(\"SR\",24),rep(\"nSR\",24))\n", "des2$PSU <- as.numeric(c(des$SRdom[1:24],des$nSRdom[1:24]))\n", "des2$SSU <- as.numeric(c(des$SR_PSU_final_sample_unit[1:24],des$NSR_PSU_final_sample_unit[1:24]))\n", "des2 <- as.data.frame(des2)\n", "des2$strata <- as.numeric(des2$strata)\n", "par(mfrow=c(2, 1))\n", "barplot(PSU~SR+strata, data=des2,\n", " main = \"PSUs by strata\",\n", " xlab = \"strata\", ylab = \"PSUs\",\n", " col = c(\"black\", \"grey\"),\n", " # beside = TRUE,\n", " las=2,\n", " cex.names=0.7)\n", "legend(\"topright\", \n", " legend = c(\"Non Self Representative\",\"Self Representative\"),cex = 0.7,\n", " fill = c(\"black\", \"grey\"))\n", "barplot(SSU~SR+strata, data=des2,\n", " main = \"SSUs by strata\",\n", " xlab = \"strata\", ylab = \"PSUs\",\n", " col = c(\"black\", \"grey\"),\n", " # beside = TRUE,\n", " las=2,\n", " cex.names=0.7)\n", "legend(\"topright\", \n", " legend = c(\"Non Self Representative\",\"Self Representative\"),cex = 0.7,\n", " fill = c(\"black\", \"grey\"))\n" ] }, { "cell_type": "markdown", "id": "4cb05ba2-3872-4886-a99f-ec1a62908790", "metadata": {}, "source": [ "## Selection of SSUs (II stage)" ] }, { "cell_type": "code", "execution_count": 21, "id": "2454296e-ea81-413f-9e3f-a605efa24412", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "PSU = 1 *** Selected SSU = 48\n", "PSU = 2 *** Selected SSU = 115\n", "PSU = 3 *** Selected SSU = 58\n", "PSU = 4 *** Selected SSU = 43\n", "PSU = 5 *** Selected SSU = 911\n", "PSU = 6 *** Selected SSU = 52\n", "PSU = 7 *** Selected SSU = 167\n", "PSU = 8 *** Selected SSU = 126\n", "PSU = 9 *** Selected SSU = 63\n", "PSU = 10 *** Selected SSU = 66\n", "PSU = 11 *** Selected SSU = 44\n", "PSU = 12 *** Selected SSU = 56\n", "PSU = 13 *** Selected SSU = 55\n", "PSU = 14 *** Selected SSU = 45\n", "PSU = 15 *** Selected SSU = 42\n", "PSU = 16 *** Selected SSU = 60\n", "PSU = 17 *** Selected SSU = 55\n", "PSU = 18 *** Selected SSU = 42\n", "PSU = 19 *** Selected SSU = 53\n", "PSU = 20 *** Selected SSU = 55\n", "PSU = 21 *** Selected SSU = 42\n", "PSU = 22 *** Selected SSU = 41\n", "PSU = 23 *** Selected SSU = 43\n", "PSU = 24 *** Selected SSU = 138\n", "PSU = 25 *** Selected SSU = 93\n", "PSU = 26 *** Selected SSU = 41\n", "PSU = 27 *** Selected SSU = 49\n", "PSU = 28 *** Selected SSU = 54\n", "PSU = 29 *** Selected SSU = 297\n", "PSU = 30 *** Selected SSU = 47\n", "PSU = 31 *** Selected SSU = 49\n", "PSU = 32 *** Selected SSU = 47\n", "PSU = 33 *** Selected SSU = 50\n", "PSU = 34 *** Selected SSU = 39\n", "PSU = 35 *** Selected SSU = 62\n", "PSU = 36 *** Selected SSU = 49\n", "PSU = 37 *** Selected SSU = 47\n", "PSU = 38 *** Selected SSU = 56\n", "PSU = 39 *** Selected SSU = 36\n", "PSU = 40 *** Selected SSU = 63\n", "PSU = 41 *** Selected SSU = 58\n", "PSU = 42 *** Selected SSU = 64\n", "PSU = 43 *** Selected SSU = 51\n", "PSU = 44 *** Selected SSU = 57\n", "PSU = 45 *** Selected SSU = 51\n", "PSU = 46 *** Selected SSU = 48\n", "PSU = 47 *** Selected SSU = 44\n", "PSU = 48 *** Selected SSU = 71\n", "PSU = 49 *** Selected SSU = 47\n", "PSU = 50 *** Selected SSU = 49\n", "PSU = 51 *** Selected SSU = 54\n", "PSU = 52 *** Selected SSU = 52\n", "PSU = 53 *** Selected SSU = 46\n", "PSU = 54 *** Selected SSU = 45\n", "PSU = 55 *** Selected SSU = 97\n", "PSU = 56 *** Selected SSU = 51\n", "PSU = 57 *** Selected SSU = 195\n", "PSU = 58 *** Selected SSU = 45\n", "PSU = 59 *** Selected SSU = 55\n", "PSU = 60 *** Selected SSU = 57\n", "PSU = 61 *** Selected SSU = 76\n", "PSU = 62 *** Selected SSU = 43\n", "PSU = 63 *** Selected SSU = 49\n", "PSU = 64 *** Selected SSU = 51\n", "PSU = 65 *** Selected SSU = 51\n", "PSU = 66 *** Selected SSU = 40\n", "PSU = 67 *** Selected SSU = 51\n", "PSU = 68 *** Selected SSU = 51\n", "PSU = 69 *** Selected SSU = 49\n", "PSU = 70 *** Selected SSU = 53\n", "PSU = 71 *** Selected SSU = 51\n", "PSU = 72 *** Selected SSU = 47\n", "PSU = 73 *** Selected SSU = 45\n", "PSU = 74 *** Selected SSU = 44\n", "PSU = 75 *** Selected SSU = 75\n", "PSU = 76 *** Selected SSU = 53\n", "PSU = 77 *** Selected SSU = 47\n", "PSU = 78 *** Selected SSU = 50\n", "PSU = 79 *** Selected SSU = 96\n", "PSU = 80 *** Selected SSU = 85\n", "PSU = 81 *** Selected SSU = 58\n", "PSU = 82 *** Selected SSU = 76\n", "PSU = 83 *** Selected SSU = 106\n", "PSU = 84 *** Selected SSU = 61\n", "PSU = 85 *** Selected SSU = 46\n", "PSU = 86 *** Selected SSU = 41\n", "PSU = 87 *** Selected SSU = 236\n", "PSU = 88 *** Selected SSU = 51\n", "PSU = 89 *** Selected SSU = 70\n", "PSU = 90 *** Selected SSU = 53\n", "PSU = 91 *** Selected SSU = 50\n", "PSU = 92 *** Selected SSU = 188\n", "PSU = 93 *** Selected SSU = 64\n", "PSU = 94 *** Selected SSU = 55\n", "PSU = 95 *** Selected SSU = 430\n", "PSU = 96 *** Selected SSU = 65\n", "PSU = 97 *** Selected SSU = 49\n", "PSU = 98 *** Selected SSU = 57\n", "PSU = 99 *** Selected SSU = 48\n", "PSU = 100 *** Selected SSU = 52\n", "PSU = 101 *** Selected SSU = 44\n", "PSU = 102 *** Selected SSU = 40\n", "PSU = 103 *** Selected SSU = 45\n", "PSU = 104 *** Selected SSU = 1298\n", "PSU = 105 *** Selected SSU = 43\n", "PSU = 106 *** Selected SSU = 42\n", "PSU = 107 *** Selected SSU = 32\n", "PSU = 108 *** Selected SSU = 60\n", "PSU = 109 *** Selected SSU = 72\n", "PSU = 110 *** Selected SSU = 55\n", "PSU = 111 *** Selected SSU = 48\n", "PSU = 112 *** Selected SSU = 58\n", "PSU = 113 *** Selected SSU = 55\n", "PSU = 114 *** Selected SSU = 45\n", "PSU = 115 *** Selected SSU = 72\n", "PSU = 116 *** Selected SSU = 25\n", "PSU = 117 *** Selected SSU = 48\n", "PSU = 118 *** Selected SSU = 59\n", "PSU = 119 *** Selected SSU = 66\n", "PSU = 120 *** Selected SSU = 49\n", "PSU = 121 *** Selected SSU = 55\n", "PSU = 122 *** Selected SSU = 55\n", "PSU = 123 *** Selected SSU = 38\n", "PSU = 124 *** Selected SSU = 69\n", "PSU = 125 *** Selected SSU = 50\n", "PSU = 126 *** Selected SSU = 39\n", "PSU = 127 *** Selected SSU = 72\n", "PSU = 128 *** Selected SSU = 72\n", "PSU = 129 *** Selected SSU = 51\n", "PSU = 130 *** Selected SSU = 109\n", "PSU = 131 *** Selected SSU = 46\n", "PSU = 132 *** Selected SSU = 51\n", "PSU = 133 *** Selected SSU = 55\n", "PSU = 134 *** Selected SSU = 59\n", "PSU = 135 *** Selected SSU = 52\n", "PSU = 136 *** Selected SSU = 314\n", "PSU = 137 *** Selected SSU = 48\n", "PSU = 138 *** Selected SSU = 2\n", "PSU = 139 *** Selected SSU = 68\n", "PSU = 140 *** Selected SSU = 60\n", "PSU = 141 *** Selected SSU = 47\n", "PSU = 142 *** Selected SSU = 35\n", "PSU = 143 *** Selected SSU = 66\n", "PSU = 144 *** Selected SSU = 43\n", "PSU = 145 *** Selected SSU = 56\n", "PSU = 146 *** Selected SSU = 6\n", "PSU = 147 *** Selected SSU = 275\n", "PSU = 148 *** Selected SSU = 54\n", "PSU = 149 *** Selected SSU = 52\n", "PSU = 150 *** Selected SSU = 56\n", "PSU = 151 *** Selected SSU = 56\n", "PSU = 152 *** Selected SSU = 51\n", "PSU = 153 *** Selected SSU = 48\n", "PSU = 154 *** Selected SSU = 56\n", "PSU = 155 *** Selected SSU = 51\n", "PSU = 156 *** Selected SSU = 141\n", "PSU = 157 *** Selected SSU = 44\n", "PSU = 158 *** Selected SSU = 57\n", "PSU = 159 *** Selected SSU = 39\n", "PSU = 160 *** Selected SSU = 56\n", "PSU = 161 *** Selected SSU = 2\n", "PSU = 162 *** Selected SSU = 54\n", "PSU = 163 *** Selected SSU = 60\n", "PSU = 164 *** Selected SSU = 130\n", "PSU = 165 *** Selected SSU = 41\n", "PSU = 166 *** Selected SSU = 68\n", "PSU = 167 *** Selected SSU = 51\n", "--------------------------------\n", "Total PSU = 167\n", "Total SSU = 13015\n", "--------------------------------" ] } ], "source": [ "selected_PSU <- sample_2st[[4]]\n", "selected_PSU <- selected_PSU[selected_PSU$PSU_final_sample_unit > 0,]\n", "samp <- select_SSU(df=pop,\n", " PSU_code=\"municipality\",\n", " SSU_code=\"id_ind\",\n", " PSU_sampled=selected_PSU[selected_PSU$Sampled_PSU==1,],\n", " verbose=TRUE)\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "49d7066e-e5b3-44ed-a6d8-c86e9f31c672", "metadata": {}, "outputs": [ { "data": { "text/html": [ "13015" ], "text/latex": [ "13015" ], "text/markdown": [ "13015" ], "text/plain": [ "[1] 13015" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "13011" ], "text/latex": [ "13011" ], "text/markdown": [ "13011" ], "text/plain": [ "[1] 13011" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nrow(samp)\n", "sum(allocat$ALLOC)" ] }, { "cell_type": "code", "execution_count": 23, "id": "38676fa0-815a-41df-a8a0-8aa260accdf8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "2258507" ], "text/latex": [ "2258507" ], "text/markdown": [ "2258507" ], 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Maue8EifaXF4fTL1/60Xl/yp1b/ZpG8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\nBGRAkYAM/gHy5xBkBage5QAAAABJRU5ErkJggg==", "text/plain": [ "Plot with title \"Weights distribution by region\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "Plot with title \"Weights distribution by stratum\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "## Plot of weights distribution\n", "par(mfrow=c(1, 2))\n", "boxplot(samp$weight,col=\"grey\")\n", "title(\"Weights distribution (total sample)\",cex.main=0.7)\n", "boxplot(weight ~ region, data=samp,col=\"grey\")\n", "title(\"Weights distribution by region\",cex.main=0.7)\n", "par(mfrow=c(1, 2))\n", "boxplot(weight ~ province, data=samp,col=\"grey\")\n", "title(\"Weights distribution by province\",cex.main=0.7)\n", "boxplot(weight ~ stratum, data=samp,col=\"grey\")\n", "title(\"Weights distribution by stratum\",cex.main=0.7)" ] }, { "cell_type": "markdown", "id": "edf54291-9d4e-47be-9a92-aad6b1b24fc1", "metadata": {}, "source": [ "## Precision constraints compliance control (by simulation)" ] }, { "cell_type": "code", "execution_count": 25, "id": "bde1b28c-cc52-48df-809e-7194f806a2fe", "metadata": {}, "outputs": [], "source": [ "selected_PSU <- sample_2st[[4]]\n", "df=pop\n", "df$one <- 1\n", "PSU_code=\"municipality\"\n", "SSU_code=\"id_ind\"\n", "PSU_sampled=selected_PSU[selected_PSU$Sampled_PSU==1,]\n", "target_vars <- c(\"income_hh\",\n", " \"active\",\n", " \"inactive\",\n", " \"unemployed\") \n", "PSU_sampled <- selected_PSU[selected_PSU$PSU_final_sample_unit > 0,]" ] }, { "cell_type": "code", "execution_count": 26, "id": "c3d35837-196f-444f-8583-e5dc715b3978", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A data.frame: 1 × 5
CV1CV2CV3CV4dom
<dbl><dbl><dbl><dbl><chr>
0.00910.00940.02440.0378DOM1
\n" ], "text/latex": [ "A data.frame: 1 × 5\n", "\\begin{tabular}{lllll}\n", " CV1 & CV2 & CV3 & CV4 & dom\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 0.0091 & 0.0094 & 0.0244 & 0.0378 & DOM1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 1 × 5\n", "\n", "| CV1 <dbl> | CV2 <dbl> | CV3 <dbl> | CV4 <dbl> | dom <chr> |\n", "|---|---|---|---|---|\n", "| 0.0091 | 0.0094 | 0.0244 | 0.0378 | DOM1 |\n", "\n" ], "text/plain": [ " CV1 CV2 CV3 CV4 dom \n", "1 0.0091 0.0094 0.0244 0.0378 DOM1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Domain level = national\n", "domain_var <- \"one\"\n", "eval <- eval_2stage(df,\n", " PSU_code,\n", " SSU_code,\n", " domain_var,\n", " target_vars,\n", " PSU_sampled,\n", " nsampl=100, \n", " writeFiles=FALSE,\n", " progress=TRUE) \n", "eval$coeff_var" ] }, { "cell_type": "code", "execution_count": 27, "id": "08cb71f0-535f-401e-ae82-355941cd4a55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "data": { "image/png": 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"text/plain": [ "Plot with title \"Distribution of CV's in the domains\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 3 × 5
CV1CV2CV3CV4dom
<dbl><dbl><dbl><dbl><chr>
0.00780.00480.01600.0640DOM1
0.02090.02050.04960.0805DOM2
0.02620.03560.05990.0471DOM3
\n" ], "text/latex": [ "A data.frame: 3 × 5\n", "\\begin{tabular}{lllll}\n", " CV1 & CV2 & CV3 & CV4 & dom\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 0.0078 & 0.0048 & 0.0160 & 0.0640 & DOM1\\\\\n", "\t 0.0209 & 0.0205 & 0.0496 & 0.0805 & DOM2\\\\\n", "\t 0.0262 & 0.0356 & 0.0599 & 0.0471 & DOM3\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 5\n", "\n", "| CV1 <dbl> | CV2 <dbl> | CV3 <dbl> | CV4 <dbl> | dom <chr> |\n", "|---|---|---|---|---|\n", "| 0.0078 | 0.0048 | 0.0160 | 0.0640 | DOM1 |\n", "| 0.0209 | 0.0205 | 0.0496 | 0.0805 | DOM2 |\n", "| 0.0262 | 0.0356 | 0.0599 | 0.0471 | DOM3 |\n", "\n" ], "text/plain": [ " CV1 CV2 CV3 CV4 dom \n", "1 0.0078 0.0048 0.0160 0.0640 DOM1\n", "2 0.0209 0.0205 0.0496 0.0805 DOM2\n", "3 0.0262 0.0356 0.0599 0.0471 DOM3" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "Plot with title \"Distribution of relative bias in the domains\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# Domain level = regional\n", "domain_var <- \"region\"\n", "set.seed(1234)\n", "eval <- eval_2stage(df,\n", " PSU_code,\n", " SSU_code,\n", " domain_var,\n", " target_vars,\n", " PSU_sampled,\n", " nsampl=100, \n", " writeFiles=FALSE,\n", " progress=TRUE) \n", "eval$coeff_var" ] }, { "cell_type": "code", "execution_count": 28, "id": "2960a7c5-abdf-46c4-bbda-e1c12b9e28eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " |======================================================================| 100%\n" ] }, { "data": { "image/png": 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"text/plain": [ "Plot with title \"Distribution of CV's in the domains\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 6 × 5
CV1CV2CV3CV4dom
<dbl><dbl><dbl><dbl><chr>
0.01250.00700.02570.0959DOM1
0.00990.00730.02460.0762DOM2
0.02590.02410.05960.0980DOM3
0.03190.03430.08150.1185DOM4
0.03170.03940.06120.0560DOM5
0.03760.06370.11660.0753DOM6
\n" ], "text/latex": [ "A data.frame: 6 × 5\n", "\\begin{tabular}{lllll}\n", " CV1 & CV2 & CV3 & CV4 & dom\\\\\n", " & & & & \\\\\n", "\\hline\n", "\t 0.0125 & 0.0070 & 0.0257 & 0.0959 & DOM1\\\\\n", "\t 0.0099 & 0.0073 & 0.0246 & 0.0762 & DOM2\\\\\n", "\t 0.0259 & 0.0241 & 0.0596 & 0.0980 & DOM3\\\\\n", "\t 0.0319 & 0.0343 & 0.0815 & 0.1185 & DOM4\\\\\n", "\t 0.0317 & 0.0394 & 0.0612 & 0.0560 & DOM5\\\\\n", "\t 0.0376 & 0.0637 & 0.1166 & 0.0753 & DOM6\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 5\n", "\n", "| CV1 <dbl> | CV2 <dbl> | CV3 <dbl> | CV4 <dbl> | dom <chr> |\n", "|---|---|---|---|---|\n", "| 0.0125 | 0.0070 | 0.0257 | 0.0959 | DOM1 |\n", "| 0.0099 | 0.0073 | 0.0246 | 0.0762 | DOM2 |\n", "| 0.0259 | 0.0241 | 0.0596 | 0.0980 | DOM3 |\n", "| 0.0319 | 0.0343 | 0.0815 | 0.1185 | DOM4 |\n", "| 0.0317 | 0.0394 | 0.0612 | 0.0560 | DOM5 |\n", "| 0.0376 | 0.0637 | 0.1166 | 0.0753 | DOM6 |\n", "\n" ], "text/plain": [ " CV1 CV2 CV3 CV4 dom \n", "1 0.0125 0.0070 0.0257 0.0959 DOM1\n", "2 0.0099 0.0073 0.0246 0.0762 DOM2\n", "3 0.0259 0.0241 0.0596 0.0980 DOM3\n", "4 0.0319 0.0343 0.0815 0.1185 DOM4\n", "5 0.0317 0.0394 0.0612 0.0560 DOM5\n", "6 0.0376 0.0637 0.1166 0.0753 DOM6" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "Plot with title \"Distribution of relative bias in the domains\"" ] }, "metadata": { "image/png": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "# Domain level = provincial\n", "domain_var <- \"province\"\n", "set.seed(1234)\n", "eval <- eval_2stage(df,\n", " PSU_code,\n", " SSU_code,\n", " domain_var,\n", " target_vars,\n", " PSU_sampled,\n", " nsampl=100, \n", " writeFiles=FALSE,\n", " progress=TRUE) \n", "eval$coeff_var" ] }, { "cell_type": "code", "execution_count": 29, "id": "f395ae16", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
A data.frame: 4 × 6
TypeDomV1V2V3V4
<chr><chr><dbl><dbl><dbl><dbl>
1DOM1111 1 442
5DOM2110 12022
9DOM221116 123
13DOM2311 1 1
\n" ], "text/latex": [ "A data.frame: 4 × 6\n", "\\begin{tabular}{r|llllll}\n", " & Type & Dom & V1 & V2 & V3 & V4\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t1 & DOM1 & 1 & 1 & 1 & 1 & 442\\\\\n", "\t5 & DOM2 & 1 & 1 & 0 & 1 & 2022\\\\\n", "\t9 & DOM2 & 2 & 1 & 1 & 16 & 123\\\\\n", "\t13 & DOM2 & 3 & 1 & 1 & 1 & 1\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 4 × 6\n", "\n", "| | Type <chr> | Dom <chr> | V1 <dbl> | V2 <dbl> | V3 <dbl> | V4 <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| 1 | DOM1 | 1 | 1 | 1 | 1 | 442 |\n", "| 5 | DOM2 | 1 | 1 | 0 | 1 | 2022 |\n", "| 9 | DOM2 | 2 | 1 | 1 | 16 | 123 |\n", "| 13 | DOM2 | 3 | 1 | 1 | 1 | 1 |\n", "\n" ], "text/plain": [ " Type Dom V1 V2 V3 V4 \n", "1 DOM1 1 1 1 1 442\n", "5 DOM2 1 1 0 1 2022\n", "9 DOM2 2 1 1 16 123\n", "13 DOM2 3 1 1 1 1" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "alloc$sensitivity" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.1.1" } }, "nbformat": 4, "nbformat_minor": 5 }