The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Azka Ubaidillah, Ridson Al Farizal P
Ridson Al Farizal P ridsonap@bps.go.id
The sae.projection package provides a robust tool for small area estimation using a projection-based approach. This method is particularly beneficial in scenarios involving two surveys, the first survey collects data solely on auxiliary variables, while the second, typically smaller survey, collects both the variables of interest and the auxiliary variables. The package constructs a working model to predict the variables of interest for each sample in the first survey. These predictions are then used to estimate relevant indicators for the desired domains. This condition overcomes the problem of estimation in a small area when only using the second survey data.
You can install the development version of sae.projection from GitHub with:
# install.packages("devtools")
devtools::install_github("Alfrzlp/sae.projection")This is a basic example which shows you how to solve a common problem:
library(sae.projection)
#> Loading required package: tidymodels
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom 1.0.7 ✔ recipes 1.1.0
#> ✔ dials 1.3.0 ✔ rsample 1.2.1
#> ✔ dplyr 1.1.4 ✔ tibble 3.2.1
#> ✔ ggplot2 3.5.1 ✔ tidyr 1.3.1
#> ✔ infer 1.0.7 ✔ tune 1.2.1
#> ✔ modeldata 1.4.0 ✔ workflows 1.1.4
#> ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
#> ✔ purrr 1.0.2 ✔ yardstick 1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
#> • Use suppressPackageStartupMessages() to eliminate package startup messages
library(dplyr)df_svy22_income <- df_svy22 %>% filter(!is.na(income))
df_svy23_income <- df_svy23 %>% filter(!is.na(income))lm_proj <- projection(
income ~ age + sex + edu + disability,
id = 'PSU', weight = 'WEIGHT', strata = 'STRATA',
domain = c('PROV', 'REGENCY'),
model = linear_reg(),
data_model = df_svy22_income,
data_proj = df_svy23_income,
)
lm_proj$df_result
#> PROV REGENCY ypr var_ypr rse_ypr
#> 1 35 1 1301174 10618881790 7.919617
#> 2 35 2 1575921 8089890094 5.707383
#> 3 35 3 1515173 8851908817 6.209496
#> 4 35 4 2016174 13493709449 5.761527
#> 5 35 5 1630143 6715811111 5.027170
#> 6 35 6 1737991 7666303467 5.037853
#> 7 35 7 2214592 20119175179 6.404887
#> 8 35 8 1903982 18080432329 7.062225
#> 9 35 9 1773319 4428235969 3.752567
#> 10 35 10 2034861 4602423996 3.333946
#> 11 35 11 1500750 26703134922 10.888621
#> 12 35 12 1722463 10352480692 5.907074
#> 13 35 13 2053568 24669804362 7.648456
#> 14 35 14 2105472 10160099561 4.787397
#> 15 35 15 3737909 144213198696 10.159535
#> 16 35 16 2412471 8240112740 3.762743
#> 17 35 17 2187732 7891875275 4.060655
#> 18 35 18 2117781 21478193260 6.920187
#> 19 35 19 1797575 7877467222 4.937490
#> 20 35 20 2053399 7851002103 4.315084
#> 21 35 21 1691834 4192575324 3.827214
#> 22 35 22 1905703 7757174634 4.621646
#> 23 35 23 2090962 6458363757 3.843395
#> 24 35 24 2223585 9689490994 4.426869
#> 25 35 25 3172802 11755846028 3.417307
#> 26 35 26 1824850 15006851832 6.713016
#> 27 35 27 1558080 25660267931 10.281133
#> 28 35 28 1525220 9180681192 6.282101
#> 29 35 29 1834459 19430440166 7.598595
#> 30 35 71 2541951 43342967547 8.190156
#> 31 35 72 2284458 10360590216 4.455630
#> 32 35 73 2835409 20677060812 5.071410
#> 33 35 74 2602627 56026780853 9.094647
#> 34 35 75 2585689 22048185343 5.742620
#> 35 35 76 2888925 14002070555 4.095999
#> 36 35 77 2554860 21995898018 5.805021
#> 37 35 78 4594493 300406206504 11.929351
#> 38 35 79 2562641 10071273136 3.916106rf_proj <- projection(
income ~ age + sex + edu + disability,
id = 'PSU', weight = 'WEIGHT', strata = 'STRATA',
domain = c('PROV', 'REGENCY'),
model = rand_forest(mtry = tune(), trees = tune(), min_n = tune()),
data_model = df_svy22_income,
data_proj = df_svy23_income,
kfold = 3,
grid = 20
)
rf_proj$df_resultdf_svy22_neet <- df_svy22 %>%
filter(between(age, 15, 24))
df_svy23_neet <- df_svy23 %>%
filter(between(age, 15, 24))lr_proj <- projection(
formula = neet ~ sex + edu + disability,
id = 'PSU',
weight = 'WEIGHT',
strata = 'STRATA',
domain = c('PROV', 'REGENCY'),
model = logistic_reg(),
data_model = df_svy22_neet,
data_proj = df_svy23_neet
)
lr_proj$df_result
#> PROV REGENCY ypr var_ypr rse_ypr
#> 1 35 1 0.1883741 0.0009101754 16.015535
#> 2 35 2 0.1761453 0.0005962890 13.863006
#> 3 35 3 0.2627529 0.0008963487 11.394387
#> 4 35 4 0.1501824 0.0005408503 15.485294
#> 5 35 5 0.1970353 0.0007401344 13.807382
#> 6 35 6 0.2236771 0.0004727475 9.720599
#> 7 35 7 0.2339609 0.0004360528 8.925373
#> 8 35 8 0.3226574 0.0008149866 8.847767
#> 9 35 9 0.2947512 0.0005243011 7.768458
#> 10 35 10 0.2439578 0.0006033091 10.068281
#> 11 35 11 0.2767459 0.0010785238 11.866799
#> 12 35 12 0.2453357 0.0006228644 10.172695
#> 13 35 13 0.3123293 0.0005482030 7.496490
#> 14 35 14 0.3277912 0.0008762413 9.030557
#> 15 35 15 0.1347073 0.0002041638 10.607138
#> 16 35 16 0.2359613 0.0006320838 10.654830
#> 17 35 17 0.2293048 0.0003759636 8.455897
#> 18 35 18 0.1947305 0.0006598068 13.190899
#> 19 35 19 0.2014381 0.0008690814 14.634864
#> 20 35 20 0.1471380 0.0006121382 16.815121
#> 21 35 21 0.1766580 0.0010066641 17.960110
#> 22 35 22 0.2105672 0.0006411531 12.025142
#> 23 35 23 0.2568354 0.0007179900 10.432882
#> 24 35 24 0.2224894 0.0004518400 9.553950
#> 25 35 25 0.2033393 0.0006942281 12.957752
#> 26 35 26 0.2489649 0.0011293225 13.498045
#> 27 35 27 0.3110092 0.0018619150 13.874160
#> 28 35 28 0.1703990 0.0004611499 12.602423
#> 29 35 29 0.2939050 0.0012088401 11.829802
#> 30 35 71 0.1395280 0.0006020211 17.585090
#> 31 35 72 0.1424188 0.0005480416 16.437644
#> 32 35 73 0.1783830 0.0004321919 11.654264
#> 33 35 74 0.2186362 0.0006895557 12.010541
#> 34 35 75 0.1700568 0.0005773959 14.130020
#> 35 35 76 0.1880439 0.0006208718 13.250790
#> 36 35 77 0.1415133 0.0005442635 16.485696
#> 37 35 78 0.1759323 0.0002766692 9.454418
#> 38 35 79 0.1786988 0.0004931984 12.427652library(bonsai)
show_engines('boost_tree')
#> # A tibble: 7 × 2
#> engine mode
#> <chr> <chr>
#> 1 xgboost classification
#> 2 xgboost regression
#> 3 C5.0 classification
#> 4 spark classification
#> 5 spark regression
#> 6 lightgbm regression
#> 7 lightgbm classification
lgbm_model <- boost_tree(
mtry = tune(), trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
engine = 'lightgbm'
)lgbm_proj <- projection(
formula = neet ~ sex + edu + disability,
id = 'PSU',
weight = 'WEIGHT',
strata = 'STRATA',
domain = c('PROV', 'REGENCY'),
model = lgbm_model,
data_model = df_svy22_neet,
data_proj = df_svy23_neet,
kfold = 3,
grid = 20
)
lgbm_proj$df_resultThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.