Workflow example with R2BEAT¶

Scenario 2¶

Together with a sampling frame containing the units of the population of reference, also a previous round of the sampling survey to be planned is available

In [1]:

# Install last version of R2BEAT
#devtools::install_github("barcaroli/R2BEAT",dependencies = FALSE)
library(R2BEAT)

Caricamento del pacchetto richiesto: plyr

Caricamento del pacchetto richiesto: devtools

Caricamento del pacchetto richiesto: usethis

Caricamento del pacchetto richiesto: sampling

Caricamento del pacchetto richiesto: SamplingStrata

Caricamento del pacchetto richiesto: memoise

Caricamento del pacchetto richiesto: doParallel

Caricamento del pacchetto richiesto: foreach

Caricamento del pacchetto richiesto: iterators

Caricamento del pacchetto richiesto: parallel

Caricamento del pacchetto richiesto: pbapply

Caricamento del pacchetto richiesto: formattable

Caricamento del pacchetto richiesto: SamplingBigData



Report issues at https://github.com/barcaroli/SamplingStrata/issues


Get a complete documentation on https://barcaroli.github.io/SamplingStrata

In [2]:

packageVersion("R2BEAT")

[1] '1.0.4'

In [3]:

## Sampling frame
load("pop.RData")

In [4]:

## Sample data
load("sample.RData")

Analysis of sampled data¶

In [5]:

# Install ReGenesees
#devtools::install_github("DiegoZardetto/ReGenesees")
library(ReGenesees)




--------------------------------------------------------

> The ReGenesees package has been successfully loaded. <

--------------------------------------------------------

Package: ReGenesees
Type: Package
Title: R Evolved Generalized Software for Sampling Estimates and Errors
        in Surveys
Description: Design-Based and Model-Assisted analysis of complex
        sampling surveys. Multistage, stratified, clustered, unequally
        weighted survey designs. Horvitz-Thompson and Calibration
        Estimators. Variance Estimation for nonlinear smooth estimators
        by Taylor-series linearization. Estimates, standard errors,
        confidence intervals and design effects for: Totals, Means,
        absolute and relative Frequency Distributions (marginal,
        conditional and joint), Ratios, Shares and Ratios of Shares,
        Multiple Regression Coefficients and Quantiles. Automated
        Linearization of Complex Analytic Estimators. Design Covariance
        and Correlation. Estimates, standard errors, confidence
        intervals and design effects for user-defined analytic
        estimators. Estimates and sampling errors for subpopulations.
        Consistent trimming of calibration weights. Calibration on
        complex population parameters, e.g. multiple regression
        coefficients. Generalized Variance Functions (GVF) method for
        predicting variance estimates.
Version: 2.1
Author: Diego Zardetto [aut, cre]
Maintainer: Diego Zardetto <zardetto@istat.it>
Authors@R: person("Diego", "Zardetto", role = c("aut", "cre"), email =
        "zardetto@istat.it")
License: EUPL
URL: https://diegozardetto.github.io/ReGenesees/,
        https://github.com/DiegoZardetto/ReGenesees/
BugReports: https://github.com/DiegoZardetto/ReGenesees/issues/
Imports: stats, MASS
Depends: R (>= 2.14.0)
ByteCompile: TRUE
RemoteType: github
RemoteHost: api.github.com
RemoteRepo: ReGenesees
RemoteUsername: DiegoZardetto
RemoteRef: HEAD
RemoteSha: 054413befcf905cd6ab06611b819a1295f7a5b20
GithubRepo: ReGenesees
GithubUsername: DiegoZardetto
GithubRef: HEAD
GithubSHA1: 054413befcf905cd6ab06611b819a1295f7a5b20
NeedsCompilation: no
Packaged: 2021-08-11 17:21:59 UTC; UTENTE
Built: R 4.1.0; ; 2021-08-11 17:22:03 UTC; windows

In [6]:

## Sample design description
sample$stratum_2 <- as.factor(sample$stratum_2)
sample.des <- e.svydesign(sample, 
                          ids= ~ municipality + id_hh, 
                          strata = ~ stratum_2, 
                          weights = ~ weight,
                          self.rep.str = ~ SR,
                          check.data = TRUE)

# Empty levels found in factors: id_hh
# Empty levels have been dropped!

Warning message in e.svydesign(sample, ids = ~municipality + id_hh, strata = ~stratum_2, :
"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)"

In [7]:

## Find and collapse lonely strata
ls <- find.lon.strata(sample.des)
sample.des <- collapse.strata(sample.des)

# All lonely strata (112) successfully collapsed!

Warning message in collapse.strata(sample.des):
"No similarity score specified: achieved strata aggregation depends on the ordering of sample data"

In [8]:

## Calibration with known totals
totals <- pop.template(sample.des,
             calmodel = ~ sex : cl_age, 
             partition = ~ region)
totals <- fill.template(pop, totals, mem.frac = 10)
sample.cal <- e.calibrate(sample.des, 
                          totals,
                          calmodel = ~ sex : cl_age, 
                          partition = ~ region,
                          calfun = "logit",
                          bounds = c(0.3, 2.6), 
                          aggregate.stage = 2,
                          force = FALSE)

# Coherence check between 'universe' and 'template': OK

Preparation of inputs for allocation steps¶

In [9]:

## Preparation of inputs for allocation steps
samp_frame <- pop
RGdes <- sample.des
RGcal <- sample.cal
strata_vars <- c("stratum")      
target_vars <- c("income_hh",
                 "active",
                 "inactive",
                 "unemployed")   
weight_var <- "weight"
deff_vars <- "stratum"            
id_PSU <- c("municipality")      
id_SSU <- c("id_hh")             
domain_vars <- c("region") 
delta <- 1                   
minimum <- 50                

inp <- prepareInputToAllocation2(
        samp_frame,  # sampling frame
        RGdes,       # ReGenesees design object
        RGcal,       # ReGenesees calibrated object
        id_PSU,      # identification variable of PSUs
        id_SSU,      # identification variable of SSUs
        strata_vars, # strata variables
        target_vars, # target variables
        deff_vars,   # deff variables
        domain_vars, # domain variables
        delta,       # Average number of SSUs for each selection unit
        minimum      # Minimum number of SSUs to be selected in each PSU
      )

In [10]:

head(inp$strata)

A data.frame: 6 × 15
	stratum	STRATUM	N	M1	M2	M3	M4	S1	S2	S3	S4	COST	CENS	DOM1	DOM2
	<fct>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<fct>
1	1000	1000	196769	23339.70	0.6801679	0.2127596	0.10707247	16543.72	0.4664113	0.4092590	0.3092054	1	0	1	center
2	10000	10000	106057	29340.38	0.7793318	0.2047430	0.01592524	25031.44	0.4146972	0.4035137	0.1251864	1	0	1	north
3	11000	11000	205839	27822.70	0.7814228	0.2029522	0.01562493	26050.40	0.4132810	0.4021972	0.1240193	1	0	1	north
4	12000	12000	57606	23110.90	0.7632522	0.2079530	0.02879485	15405.51	0.4250862	0.4058430	0.1672295	1	0	1	north
5	13000	13000	102801	28185.38	0.7516670	0.2142238	0.03410920	24393.71	0.4320460	0.4102828	0.1815097	1	0	1	north
6	14000	14000	84077	24787.12	0.7537232	0.2131530	0.03312385	17403.58	0.4308417	0.4095348	0.1789599	1	0	1	north

In [11]:

head(inp$deff)

A data.frame: 6 × 7
	stratum	STRATUM	DEFF1	DEFF2	DEFF3	DEFF4	b_nar
	<fct>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	1000	1000	1.002141	1.003487	1.018508	0.998091	254.50000
2	10000	10000	1.019820	1.029362	1.010320	1.000982	178.83333
3	11000	11000	1.128662	1.036882	1.002039	1.115932	52.07500
4	12000	12000	3.233942	0.978419	1.202842	0.639357	49.42857
5	13000	13000	1.063373	1.056811	1.015756	1.048938	1285.00000
6	14000	14000	1.018801	1.003173	1.002272	1.013573	263.50000

In [12]:

head(inp$effst)

A data.frame: 6 × 6
	stratum	STRATUM	EFFST1	EFFST2	EFFST3	EFFST4
	<fct>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
1	1000	1000	0.9875397	0.8647755	0.7565498	1.0033213
2	10000	10000	0.9948599	0.9076545	0.8982699	1.0054137
3	11000	11000	0.9765404	0.8136085	0.7835224	0.9925166
4	12000	12000	1.0145565	0.9113590	0.9126909	1.0007101
5	13000	13000	1.0045911	0.9263170	0.9180502	0.9942647
6	14000	14000	1.0016745	0.9471318	0.9375788	0.9967146

In [13]:

head(inp$rho)

A data.frame: 6 × 9
	STRATUM	RHO_AR1	RHO_NAR1	RHO_AR2	RHO_NAR2	RHO_AR3	RHO_NAR3	RHO_AR4	RHO_NAR4
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
1	1000	1	0.000008445759	1	0.00001375542	1	0.000073009862	1	-0.000007530572
2	10000	1	0.000111452671	1	0.00016510965	1	0.000058031865	1	0.000005522024
3	11000	1	0.002519079785	1	0.00072211454	1	0.000039921684	1	0.002269838473
4	12000	1	0.046128595870	1	-0.00044562537	1	0.004188477876	1	-0.007446905605
5	13000	1	0.000049355919	1	0.00004424533	1	0.000012271028	1	0.000038113707
6	14000	1	0.000071622857	1	0.00001208762	1	0.000008655238	1	0.000051706667

In [14]:

head(inp$psu_file)

A data.frame: 6 × 3
	PSU_ID	STRATUM	PSU_MOS
	<dbl>	<fct>	<dbl>
1	309	1000	50845
2	330	1000	146162
3	292	2000	24794
4	293	2000	19609
5	300	2000	13897
6	304	2000	36195

In [15]:

head(inp$des_file)

A data.frame: 6 × 4
	STRATUM	STRAT_MOS	DELTA	MINIMUM
	<fct>	<dbl>	<dbl>	<dbl>
1	1000	197007	1	50
2	2000	261456	1	50
3	3000	115813	1	50
4	4000	17241	1	50
5	5000	101067	1	50
6	6000	47218	1	50

Allocation¶

In [16]:

## Precision constraints
cv <- as.data.frame(list(DOM=c("DOM1","DOM2"),
                         CV1=c(0.02,0.03),
                         CV2=c(0.03,0.06),
                         CV3=c(0.03,0.06),
                         CV4=c(0.03,0.06)))
cv

A data.frame: 2 × 5
DOM	CV1	CV2	CV3	CV4
<chr>	<dbl>	<dbl>	<dbl>	<dbl>
DOM1	0.02	0.03	0.03	0.03
DOM2	0.03	0.06	0.06	0.06

In [17]:

alloc <- beat.2st(stratif = inp$strata, 
                  errors = cv, 
                  des_file = inp$des_file, 
                  psu_file = inp$psu_file, 
                  rho = inp$rho, 
                  deft_start = NULL, 
                  effst = inp$effst,
                  epsilon1 = 5, 
                  mmdiff_deft = 1,
                  maxi = 15, 
                  epsilon = 10^(-11), 
                  minnumstrat = 2, 
                  maxiter = 200, 
                  maxiter1 = 25)

  iterations PSU_SR PSU NSR PSU Total   SSU
1          0      0       0         0 13512
2          1     78      67       145 13209
3          2     44     124       168 13016
4          3     43     123       166 13011

Selection of PSUs (I stage)¶

In [18]:

allocat <- alloc$alloc[-nrow(alloc$alloc),]
set.seed(1234)
sample_2st <- StratSel(dataPop= inp$psu_file,
                       idpsu= ~ PSU_ID, 
                       dom= ~ STRATUM, 
                       final_pop= ~ PSU_MOS, 
                       size= ~ PSU_MOS, 
                       PSUsamplestratum= 1, 
                       min_sample= minimum, 
                       min_sample_index= FALSE, 
                       dataAll=allocat,
                       domAll= ~ factor(STRATUM), 
                       f_sample= ~ ALLOC, 
                       planned_min_sample= NULL, 
                       launch= F)

In [19]:

sample_2st[[2]]

A data.frame: 26 × 6
Domain	SRdom	nSRdom	SRdom+nSRdom	SR_PSU_final_sample_unit	NSR_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
10000	6	0	6	936	0
11000	16	20	36	761	1091
12000	0	11	11	0	537
13000	1	0	1	1298	0
14000	4	0	4	1049	0
15000	28	10	38	1629	627
16000	0	27	27	0	1333
17000	1	0	1	141	0
18000	0	3	3	0	134
19000	0	6	6	0	320
20000	0	3	3	0	166
21000	1	0	1	130	0
22000	1	1	2	41	68
23000	0	3	3	0	165
24000	0	1	1	0	2
Total	69	98	167	7931	5084
Mean				330	212

In [20]:

## Plot of allocation (PSUs and SSUs)
des <- sample_2st[[2]]
des2 <- NULL
des2$strata <- c(des$Domain[1:24],des$Domain[1:24])
des2$SR <- c(rep("SR",24),rep("nSR",24))
des2$PSU <- as.numeric(c(des$SRdom[1:24],des$nSRdom[1:24]))
des2$SSU <- as.numeric(c(des$SR_PSU_final_sample_unit[1:24],des$NSR_PSU_final_sample_unit[1:24]))
des2 <- as.data.frame(des2)
des2$strata <- as.numeric(des2$strata)
par(mfrow=c(2, 1))
barplot(PSU~SR+strata, data=des2,
        main = "PSUs by strata",
        xlab = "strata", ylab = "PSUs",
        col = c("black", "grey"),
        # beside = TRUE,
        las=2,
        cex.names=0.7)
legend("topright", 
       legend = c("Non Self Representative","Self Representative"),cex = 0.7,
       fill = c("black", "grey"))
barplot(SSU~SR+strata, data=des2,
        main = "SSUs by strata",
        xlab = "strata", ylab = "PSUs",
        col = c("black", "grey"),
        # beside = TRUE,
        las=2,
        cex.names=0.7)
legend("topright", 
       legend = c("Non Self Representative","Self Representative"),cex = 0.7,
       fill = c("black", "grey"))

Selection of SSUs (II stage)¶

In [21]:

selected_PSU <- sample_2st[[4]]
selected_PSU <- selected_PSU[selected_PSU$PSU_final_sample_unit > 0,]
samp <- select_SSU(df=pop,
                   PSU_code="municipality",
                   SSU_code="id_ind",
                   PSU_sampled=selected_PSU[selected_PSU$Sampled_PSU==1,],
                   verbose=TRUE)

PSU =  1  *** Selected SSU =  48
PSU =  2  *** Selected SSU =  115
PSU =  3  *** Selected SSU =  58
PSU =  4  *** Selected SSU =  43
PSU =  5  *** Selected SSU =  911
PSU =  6  *** Selected SSU =  52
PSU =  7  *** Selected SSU =  167
PSU =  8  *** Selected SSU =  126
PSU =  9  *** Selected SSU =  63
PSU =  10  *** Selected SSU =  66
PSU =  11  *** Selected SSU =  44
PSU =  12  *** Selected SSU =  56
PSU =  13  *** Selected SSU =  55
PSU =  14  *** Selected SSU =  45
PSU =  15  *** Selected SSU =  42
PSU =  16  *** Selected SSU =  60
PSU =  17  *** Selected SSU =  55
PSU =  18  *** Selected SSU =  42
PSU =  19  *** Selected SSU =  53
PSU =  20  *** Selected SSU =  55
PSU =  21  *** Selected SSU =  42
PSU =  22  *** Selected SSU =  41
PSU =  23  *** Selected SSU =  43
PSU =  24  *** Selected SSU =  138
PSU =  25  *** Selected SSU =  93
PSU =  26  *** Selected SSU =  41
PSU =  27  *** Selected SSU =  49
PSU =  28  *** Selected SSU =  54
PSU =  29  *** Selected SSU =  297
PSU =  30  *** Selected SSU =  47
PSU =  31  *** Selected SSU =  49
PSU =  32  *** Selected SSU =  47
PSU =  33  *** Selected SSU =  50
PSU =  34  *** Selected SSU =  39
PSU =  35  *** Selected SSU =  62
PSU =  36  *** Selected SSU =  49
PSU =  37  *** Selected SSU =  47
PSU =  38  *** Selected SSU =  56
PSU =  39  *** Selected SSU =  36
PSU =  40  *** Selected SSU =  63
PSU =  41  *** Selected SSU =  58
PSU =  42  *** Selected SSU =  64
PSU =  43  *** Selected SSU =  51
PSU =  44  *** Selected SSU =  57
PSU =  45  *** Selected SSU =  51
PSU =  46  *** Selected SSU =  48
PSU =  47  *** Selected SSU =  44
PSU =  48  *** Selected SSU =  71
PSU =  49  *** Selected SSU =  47
PSU =  50  *** Selected SSU =  49
PSU =  51  *** Selected SSU =  54
PSU =  52  *** Selected SSU =  52
PSU =  53  *** Selected SSU =  46
PSU =  54  *** Selected SSU =  45
PSU =  55  *** Selected SSU =  97
PSU =  56  *** Selected SSU =  51
PSU =  57  *** Selected SSU =  195
PSU =  58  *** Selected SSU =  45
PSU =  59  *** Selected SSU =  55
PSU =  60  *** Selected SSU =  57
PSU =  61  *** Selected SSU =  76
PSU =  62  *** Selected SSU =  43
PSU =  63  *** Selected SSU =  49
PSU =  64  *** Selected SSU =  51
PSU =  65  *** Selected SSU =  51
PSU =  66  *** Selected SSU =  40
PSU =  67  *** Selected SSU =  51
PSU =  68  *** Selected SSU =  51
PSU =  69  *** Selected SSU =  49
PSU =  70  *** Selected SSU =  53
PSU =  71  *** Selected SSU =  51
PSU =  72  *** Selected SSU =  47
PSU =  73  *** Selected SSU =  45
PSU =  74  *** Selected SSU =  44
PSU =  75  *** Selected SSU =  75
PSU =  76  *** Selected SSU =  53
PSU =  77  *** Selected SSU =  47
PSU =  78  *** Selected SSU =  50
PSU =  79  *** Selected SSU =  96
PSU =  80  *** Selected SSU =  85
PSU =  81  *** Selected SSU =  58
PSU =  82  *** Selected SSU =  76
PSU =  83  *** Selected SSU =  106
PSU =  84  *** Selected SSU =  61
PSU =  85  *** Selected SSU =  46
PSU =  86  *** Selected SSU =  41
PSU =  87  *** Selected SSU =  236
PSU =  88  *** Selected SSU =  51
PSU =  89  *** Selected SSU =  70
PSU =  90  *** Selected SSU =  53
PSU =  91  *** Selected SSU =  50
PSU =  92  *** Selected SSU =  188
PSU =  93  *** Selected SSU =  64
PSU =  94  *** Selected SSU =  55
PSU =  95  *** Selected SSU =  430
PSU =  96  *** Selected SSU =  65
PSU =  97  *** Selected SSU =  49
PSU =  98  *** Selected SSU =  57
PSU =  99  *** Selected SSU =  48
PSU =  100  *** Selected SSU =  52
PSU =  101  *** Selected SSU =  44
PSU =  102  *** Selected SSU =  40
PSU =  103  *** Selected SSU =  45
PSU =  104  *** Selected SSU =  1298
PSU =  105  *** Selected SSU =  43
PSU =  106  *** Selected SSU =  42
PSU =  107  *** Selected SSU =  32
PSU =  108  *** Selected SSU =  60
PSU =  109  *** Selected SSU =  72
PSU =  110  *** Selected SSU =  55
PSU =  111  *** Selected SSU =  48
PSU =  112  *** Selected SSU =  58
PSU =  113  *** Selected SSU =  55
PSU =  114  *** Selected SSU =  45
PSU =  115  *** Selected SSU =  72
PSU =  116  *** Selected SSU =  25
PSU =  117  *** Selected SSU =  48
PSU =  118  *** Selected SSU =  59
PSU =  119  *** Selected SSU =  66
PSU =  120  *** Selected SSU =  49
PSU =  121  *** Selected SSU =  55
PSU =  122  *** Selected SSU =  55
PSU =  123  *** Selected SSU =  38
PSU =  124  *** Selected SSU =  69
PSU =  125  *** Selected SSU =  50
PSU =  126  *** Selected SSU =  39
PSU =  127  *** Selected SSU =  72
PSU =  128  *** Selected SSU =  72
PSU =  129  *** Selected SSU =  51
PSU =  130  *** Selected SSU =  109
PSU =  131  *** Selected SSU =  46
PSU =  132  *** Selected SSU =  51
PSU =  133  *** Selected SSU =  55
PSU =  134  *** Selected SSU =  59
PSU =  135  *** Selected SSU =  52
PSU =  136  *** Selected SSU =  314
PSU =  137  *** Selected SSU =  48
PSU =  138  *** Selected SSU =  2
PSU =  139  *** Selected SSU =  68
PSU =  140  *** Selected SSU =  60
PSU =  141  *** Selected SSU =  47
PSU =  142  *** Selected SSU =  35
PSU =  143  *** Selected SSU =  66
PSU =  144  *** Selected SSU =  43
PSU =  145  *** Selected SSU =  56
PSU =  146  *** Selected SSU =  6
PSU =  147  *** Selected SSU =  275
PSU =  148  *** Selected SSU =  54
PSU =  149  *** Selected SSU =  52
PSU =  150  *** Selected SSU =  56
PSU =  151  *** Selected SSU =  56
PSU =  152  *** Selected SSU =  51
PSU =  153  *** Selected SSU =  48
PSU =  154  *** Selected SSU =  56
PSU =  155  *** Selected SSU =  51
PSU =  156  *** Selected SSU =  141
PSU =  157  *** Selected SSU =  44
PSU =  158  *** Selected SSU =  57
PSU =  159  *** Selected SSU =  39
PSU =  160  *** Selected SSU =  56
PSU =  161  *** Selected SSU =  2
PSU =  162  *** Selected SSU =  54
PSU =  163  *** Selected SSU =  60
PSU =  164  *** Selected SSU =  130
PSU =  165  *** Selected SSU =  41
PSU =  166  *** Selected SSU =  68
PSU =  167  *** Selected SSU =  51
--------------------------------
Total PSU =  167
Total SSU =  13015
--------------------------------

In [22]:

nrow(samp)
sum(allocat$ALLOC)

13015

13011

In [23]:

nrow(pop)
sum(samp$weight)

2258507

In [24]:

## Plot of weights distribution
par(mfrow=c(1, 2))
boxplot(samp$weight,col="grey")
title("Weights distribution (total sample)",cex.main=0.7)
boxplot(weight ~ region, data=samp,col="grey")
title("Weights distribution by region",cex.main=0.7)
par(mfrow=c(1, 2))
boxplot(weight ~ province, data=samp,col="grey")
title("Weights distribution by province",cex.main=0.7)
boxplot(weight ~ stratum, data=samp,col="grey")
title("Weights distribution by stratum",cex.main=0.7)

Precision constraints compliance control (by simulation)¶

In [25]:

selected_PSU <- sample_2st[[4]]
df=pop
df$one <- 1
PSU_code="municipality"
SSU_code="id_ind"
PSU_sampled=selected_PSU[selected_PSU$Sampled_PSU==1,]
target_vars <- c("income_hh",
                 "active",
                 "inactive",
                 "unemployed")  
PSU_sampled <- selected_PSU[selected_PSU$PSU_final_sample_unit > 0,]

In [26]:

# Domain level = national
domain_var <- "one"
eval <- eval_2stage(df,
                    PSU_code,
                    SSU_code,
                    domain_var,
                    target_vars,
                    PSU_sampled,
                    nsampl=100, 
                    writeFiles=FALSE,
                    progress=TRUE) 
eval$coeff_var

  |======================================================================| 100%

A data.frame: 1 × 5
CV1	CV2	CV3	CV4	dom
<dbl>	<dbl>	<dbl>	<dbl>	<chr>
0.0091	0.0094	0.0244	0.0378	DOM1

In [27]:

# Domain level = regional
domain_var <- "region"
set.seed(1234)
eval <- eval_2stage(df,
                    PSU_code,
                    SSU_code,
                    domain_var,
                    target_vars,
                    PSU_sampled,
                    nsampl=100, 
                    writeFiles=FALSE,
                    progress=TRUE) 
eval$coeff_var

  |======================================================================| 100%

A data.frame: 3 × 5
CV1	CV2	CV3	CV4	dom
<dbl>	<dbl>	<dbl>	<dbl>	<chr>
0.0078	0.0048	0.0160	0.0640	DOM1
0.0209	0.0205	0.0496	0.0805	DOM2
0.0262	0.0356	0.0599	0.0471	DOM3

In [28]:

# Domain level = provincial
domain_var <- "province"
set.seed(1234)
eval <- eval_2stage(df,
                    PSU_code,
                    SSU_code,
                    domain_var,
                    target_vars,
                    PSU_sampled,
                    nsampl=100, 
                    writeFiles=FALSE,
                    progress=TRUE) 
eval$coeff_var

  |======================================================================| 100%

A data.frame: 6 × 5
CV1	CV2	CV3	CV4	dom
<dbl>	<dbl>	<dbl>	<dbl>	<chr>
0.0125	0.0070	0.0257	0.0959	DOM1
0.0099	0.0073	0.0246	0.0762	DOM2
0.0259	0.0241	0.0596	0.0980	DOM3
0.0319	0.0343	0.0815	0.1185	DOM4
0.0317	0.0394	0.0612	0.0560	DOM5
0.0376	0.0637	0.1166	0.0753	DOM6

In [29]:

alloc$sensitivity

A data.frame: 4 × 6
	Type	Dom	V1	V2	V3	V4
	<chr>	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
1	DOM1	1	1	1	1	442
5	DOM2	1	1	0	1	2022
9	DOM2	2	1	1	16	123
13	DOM2	3	1	1	1	1