This vignette covers package data. Central to all vignettes are data inputs in the form of, country classifications, estimated correlations, estimated national and subnational model parameters for one-country runs, and national and subnational family planning source data.
-
Family planning source data
national_FPsource_dataandsubnat_FPsource_data -
Country and area classification
Country_and_area_classification_inclFP2020 -
Country names
country_names -
Estimated national correlations
national_estimated_correlations_logitnormal -
Estimated subnational correlations
subnational_estimated_correlations -
Estimated model parameters for national one-country
runs
national_theta_rms_hat_logitnormal,national_tau_alpha_cms_hat_logitnormal, andnational_sigma_delta_hat_logitnormal, -
Estimated model parameters for subnational one-country
runs
subnational_alpha_cms_hat,subnational_tau_alpha_pms_hat, andsubnational_inv.sigma_delta_hat.
Load your library
## Warning: replacing previous import 'stats::filter' by 'dplyr::filter' when
## loading 'mcmsupply'Family planning source data
These are are two family planning commodity source datasets provided
in this package - one for the national level observations,
national_FPsource_data and one for the subnational level
data subnat_FPsource_data. For the national level data,
there is a vignette
calculate_FPsource_national_data_from_DHSmicrodata in the
inst/data-raw folder that explains how the national level
data was calculated using the DHS micro-data. A similar approach was
used for the subnational data using IPUMS data.
head(national_FPsource_data)## # A tibble: 6 × 21
## # Rowwise:
## Country Region Method average_year Commercial_medical Other Public
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Southern As… Femal… 2016. 0.256 3.54e-2 0.709
## 2 Afghanistan Southern As… IUD 2016. 0.385 1.25e-2 0.602
## 3 Afghanistan Southern As… Injec… 2016. 0.364 1.05e-2 0.626
## 4 Afghanistan Southern As… OC Pi… 2016. 0.600 3.70e-2 0.363
## 5 Albania Southern Eu… Femal… 2008. 0.000307 3.07e-4 0.999
## 6 Albania Southern Eu… Femal… 2018. 0.0446 2.34e-2 0.932
## # ℹ 14 more variables: Commercial_medical.SE <dbl>, Other.SE <dbl>,
## # Public.SE <dbl>, Other_n <dbl>, Public_n <dbl>, Commercial_medical_n <dbl>,
## # check_sum <dbl>, count_SE.NA <dbl>, DEFT <dbl>, row_id <int>,
## # index_country <int>, index_region <int>, index_method <int>,
## # index_year <int>
head(subnat_FPsource_data)## Country Region Method average_year sector_categories
## 1 Afghanistan Badakhshan Female Sterilization 2015.5 Commercial_medical
## 2 Afghanistan Badakhshan Female Sterilization 2015.5 Other
## 3 Afghanistan Badakhshan Female Sterilization 2015.5 Public
## 4 Afghanistan Badakhshan IUD 2015.5 Commercial_medical
## 5 Afghanistan Badakhshan IUD 2015.5 Other
## 6 Afghanistan Badakhshan IUD 2015.5 Public
## proportion SE.proportion n
## 1 5.757770e-11 0 NA
## 2 5.757770e-11 0 NA
## 3 1.000000e+00 0 5
## 4 5.181116e-11 0 NA
## 5 5.181116e-11 0 NA
## 6 1.000000e+00 0 3Country and area classification
Country and area classification data is used as the a link between
low-level divisions (country) and higher-level divisions (sub-regions,
regions). After loading the package, enter
Country and area classification into the console to access
this data.
Country_and_area_classification## # A tibble: 231 × 8
## `Country or area` `ISO Code` `Major area` Region `Developed\nregion`
## <chr> <dbl> <chr> <chr> <chr>
## 1 Afghanistan 4 Asia South… No
## 2 Albania 8 Europe South… Yes
## 3 Algeria 12 Africa North… No
## 4 American Samoa 16 Oceania Polyn… No
## 5 Andorra 20 Europe South… Yes
## 6 Angola 24 Africa Middl… No
## 7 Anguilla 660 Latin America and … Carib… No
## 8 Antigua and Barbuda 28 Latin America and … Carib… No
## 9 Argentina 32 Latin America and … South… No
## 10 Armenia 51 Asia Weste… No
## # ℹ 221 more rows
## # ℹ 3 more variables: `Least developed\ncountry` <chr>,
## # `Sub-Saharan\nAfrica` <chr>, FP2020 <chr>
??Country_and_area_classificationCountry names
Country names is to inform users of what countries are available at
the national and subnational administrative division in the preloaded
data of the mcmsupply package. After loading the package, enter
country_names into the console to access this data.
country_names## # A tibble: 30 × 3
## `Country names` National level data ava…¹ Subnational level da…²
## <chr> <chr> <chr>
## 1 Afghanistan Yes Yes
## 2 Benin Yes Yes
## 3 Burkina Faso Yes Yes
## 4 Cameroon Yes Yes
## 5 Congo Yes No
## 6 Democratic Republic of Congo Yes Yes
## 7 Cote d’Ivoire Yes Yes
## 8 Ethiopia Yes Yes
## 9 Ghana Yes Yes
## 10 Guinea Yes Yes
## # ℹ 20 more rows
## # ℹ abbreviated names: ¹`National level data available`,
## # ²`Subnational level data available`
??country_namesEstimated national correlations
This is the estimated correlations for the rates of change between
methods in the global national model. The approach for estimating
correlations at the national level is very similar to that at the
subnational level. For an example of how to calculate the subnational
correlations, please review the
inst/data-raw/estimated_global_subnational_correlations.R
script.
## # A tibble: 10 × 4
## row column public_cor private_cor
## <chr> <chr> <dbl> <dbl>
## 1 Implants Female Sterilization 0 0
## 2 Injectables Female Sterilization 0 0.1
## 3 IUD Female Sterilization 0 0
## 4 OC Pills Female Sterilization 0 0.1
## 5 Injectables Implants 0 0
## 6 IUD Implants 0 0
## 7 OC Pills Implants 0 0
## 8 IUD Injectables 0 0
## 9 OC Pills Injectables 0.2 0.1
## 10 OC Pills IUD 0 0Estimated subnational correlations
This is the estimated correlations for the rates of change between
methods in the global national model. There is a vignette to describe
how we calculated these correlations at the subnational level, please
review the
inst/data-raw/estimated_global_subnational_correlations.R
script.
subnational_estimated_correlations## # A tibble: 10 × 4
## row column public_cor private_cor
## <chr> <chr> <dbl> <dbl>
## 1 Implants Female Sterilization -0.1 0.2
## 2 Injectables Female Sterilization 0.1 0.3
## 3 IUD Female Sterilization 0.2 -0.1
## 4 OC Pills Female Sterilization 0 0.5
## 5 Injectables Implants 0.1 0.1
## 6 IUD Implants 0 0.1
## 7 OC Pills Implants 0 0.1
## 8 IUD Injectables 0.3 0
## 9 OC Pills Injectables 0.3 0.6
## 10 OC Pills IUD 0 0Estimated model parameters for national one-country runs
These are the estimated parameters used in a one-country national
model run. national_theta_rms_hat_logitnormal are the
regional intercepts used to inform the country-specific intercept of the
model, the national_tau_alpha_cms_hat_logitnormal are the
associated variance with these country-specific intercepts.
national_sigma_delta_hat_logitnormal is the
variance-covariance matrix used to inform the multivariate normal prior
describing the first-order differences of the spline coefficients
().
national_theta_rms_hat_bivarlogitnorm## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.124690 1.879019 0.6123944 0.7029649 -0.3923574
## [2,] 4.925907 6.186027 3.3938891 6.1469855 2.1172546
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.888573 2.519485 1.724739 1.702931 -0.1325017
## [2,] 5.123504 6.185247 3.381383 6.205612 2.1114136
##
## , , 3
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.123264 0.9757107 1.403772 0.7431322 -0.6460643
## [2,] 5.081845 6.1901683 3.300275 6.1561532 2.0779721
##
## , , 4
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.266304 1.579957 0.9993598 0.8297971 -0.5330631
## [2,] 5.102983 6.167419 3.3558101 6.1656484 2.0286927
##
## , , 5
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.375822 2.057794 1.426649 1.157994 0.2966173
## [2,] 5.125126 6.187705 3.211676 6.203302 2.0952665
##
## , , 6
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.103074 1.788250 1.904318 0.4001253 0.1891328
## [2,] 5.068090 6.166153 3.337933 6.1742769 2.1654082
national_tau_alpha_cms_hat_bivarlogitnorm## [1] 1.5156756 0.4571958
national_inv_sigma_delta_hat_bivarlogitnorm## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 9.823609 0.000000 0.0000000 0.000000 0.0000000
## [2,] 0.000000 3.093259 0.0000000 0.000000 0.0000000
## [3,] 0.000000 0.000000 5.0010706 0.000000 -0.9655567
## [4,] 0.000000 0.000000 0.0000000 7.818821 0.0000000
## [5,] 0.000000 0.000000 -0.9655567 0.000000 4.7434306
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 190.8352767 0.000 -0.9206719 0.000 -0.8301243
## [2,] 0.0000000 1338.708 0.0000000 0.000 0.0000000
## [3,] -0.9206719 0.000 1.8037400 0.000 -0.1361239
## [4,] 0.0000000 0.000 0.0000000 1742.649 0.0000000
## [5,] -0.8301243 0.000 -0.1361239 0.000 1.4333294Estimated model parameters for subnational one-country runs
These are the estimated parameters used in a one-country subnational
model run. subnational_alpha_cms_hat are the
country-specific intercepts used to inform the subnational
province-specific intercepts of the model, the
subnational_tau_alpha_pms_hat are the associated variance
with these province-specific intercepts.
subnational_inv.sigma_delta_hat is a precision of the
variance-covariance matrix used to inform the multi-variate normal prior
on first-order differences of the spline coefficients for the
one-country subnational model.
subnational_alpha_cms_hat## , , Benin
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.490061 2.315202 1.468489 0.965467 -0.867741
## [2,] 3.709024 4.913419 3.559578 4.705832 1.417933
##
## , , Burkina Faso
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.624638 4.401430 4.091642 1.856639 1.673468
## [2,] 3.627078 4.916308 3.542609 4.695024 1.332808
##
## , , Cameroon
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.414227 1.851670 1.186021 1.618966 0.02014994
## [2,] 3.900294 4.974344 3.297583 4.689463 2.05336892
##
## , , Congo Democratic Republic
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.492296 1.517491 0.5847138 1.595690 -1.365295
## [2,] 3.718868 4.924430 3.5579679 4.707582 3.153638
##
## , , Cote d'Ivoire
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.593154 2.777196 1.708129 1.815302 -1.004267
## [2,] 3.634471 4.930963 3.507759 4.734519 1.576795
##
## , , Ethiopia
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.747417 3.253701 1.232114 2.182444 -0.004640113
## [2,] 3.722831 5.066009 3.872909 4.693914 3.030750423
##
## , , Ghana
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.024306 3.090449 2.375181 1.850325 -1.455411
## [2,] 3.723255 4.907766 3.500096 4.610203 3.382613
##
## , , Guinea
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.569021 2.579824 1.529904 1.812646 -0.3854365
## [2,] 3.672309 4.972549 3.411387 4.726776 1.0253375
##
## , , India
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.727972 2.319194 -0.6089675 0.9337197 -0.7976565
## [2,] 4.603336 4.930379 3.7498797 4.5598335 1.7521702
##
## , , Kenya
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.380276 1.475382 0.6217132 1.003297 -0.1438469
## [2,] 4.009730 5.036432 4.1340056 4.739624 2.8696055
##
## , , Liberia
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.584706 2.537414 1.092415 1.821980 0.7096074
## [2,] 3.664674 4.891488 3.318765 4.775019 1.9086049
##
## , , Madagascar
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.452349 2.007100 1.926210 0.2240422 0.5095557
## [2,] 3.795371 5.001309 3.348336 4.7504325 1.4172713
##
## , , Malawi
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9599796 1.814213 1.873269 1.112080 1.331949
## [2,] 3.6146381 5.017234 2.575305 4.812123 2.198249
##
## , , Mali
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.793507 2.378469 0.8042072 3.805264 -0.4974907
## [2,] 3.665678 4.949069 3.2119981 4.773084 1.8144717
##
## , , Mozambique
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.835659 2.321865 3.195910 3.149991 1.197336
## [2,] 3.684628 4.949439 3.332752 4.652611 1.160038
##
## , , Nepal
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.291945 2.086874 1.028583 1.066474 0.3402941
## [2,] 1.304060 4.968606 3.771652 4.698728 2.6481706
##
## , , Niger
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.632563 3.088681 3.726582 1.548546 1.5793860
## [2,] 3.696786 5.006807 4.037084 4.761696 0.9871326
##
## , , Pakistan
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.2688487 2.250390 0.6232042 1.526340 -0.3606401
## [2,] 4.0865275 4.985413 3.7905436 4.797376 1.7001215
##
## , , Rwanda
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.193200 3.382272 3.251083 1.502788 2.272553
## [2,] 3.677578 4.903894 3.615488 4.691452 2.633087
##
## , , Senegal
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.716900 4.204814 2.855192 2.388533 1.196907
## [2,] 3.668796 4.988311 3.516041 4.706682 2.730969
##
## , , Tanzania
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9650422 2.369480 1.4544593 1.442722 1.262963
## [2,] 3.1060103 4.697833 0.4194487 4.674467 1.199514
##
## , , Uganda
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.769091 1.798986 0.5185503 1.264740 -0.7620542
## [2,] 3.831614 4.944204 4.0255467 4.697859 3.1368453
##
## , , Zimbabwe
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.317705 1.758903 2.014544 1.552914 0.9099968
## [2,] 3.732717 4.927628 3.146861 4.686576 1.7962100
subnational_tau_alpha_pms_hat## [1] 2.035571 1.507170
subnational_inv.sigma_delta_hat## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 6.1465996 -5.081301 -0.8678454 0.3733307 0.5105131
## [2,] -5.0813014 3095.437441 -19.9096569 8.0479535 12.1046314
## [3,] -0.8678454 -19.909657 2.7844919 0.0286086 -0.6903623
## [4,] 0.3733307 8.047954 0.0286086 4.5158420 -0.6462109
## [5,] 0.5105131 12.104631 -0.6903623 -0.6462109 2.8511850
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 25527.97 0.00 0.0 0.00 0.00
## [2,] 0.00 49990.84 0.0 0.00 0.00
## [3,] 0.00 0.00 471914.4 0.00 0.00
## [4,] 0.00 0.00 0.0 20922.88 0.00
## [5,] 0.00 0.00 0.0 0.00 45692.51