Package data

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.

  1. Family planning source data national_FPsource_data and subnat_FPsource_data
  2. Country and area classification Country_and_area_classification_inclFP2020
  3. Country names country_names
  4. Estimated national correlations national_estimated_correlations_logitnormal
  5. Estimated subnational correlations subnational_estimated_correlations
  6. Estimated model parameters for national one-country runs national_theta_rms_hat_logitnormal, national_tau_alpha_cms_hat_logitnormal , and national_sigma_delta_hat_logitnormal,
  7. Estimated model parameters for subnational one-country runs subnational_alpha_cms_hat, subnational_tau_alpha_pms_hat, and subnational_inv.sigma_delta_hat.

Load your library

library(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 × 15
## # Rowwise: 
##   Country     Method   average_year  year country_code Public se.Public Public_n
##   <chr>       <chr>           <dbl> <dbl> <chr>         <dbl>     <dbl>    <dbl>
## 1 Afghanistan Injecta…        2016.  2015 AF            0.626    0.0238      931
## 2 Afghanistan IUD             2016.  2015 AF            0.603    0.0497      211
## 3 Afghanistan Pill            2016.  2015 AF            0.363    0.0265      642
## 4 Afghanistan Female …        2016.  2015 AF            0.709    0.0311      270
## 5 Benin       Injecta…        1996.  1996 BJ            0.732    0.0933       22
## 6 Benin       Pill            1996.  1996 BJ            0.304    0.0751       15
## # ℹ 7 more variables: Commercial_medical <dbl>, se.Commercial_medical <dbl>,
## #   Commercial_medical_n <dbl>, Other <dbl>, se.Other <dbl>, Other_n <dbl>,
## #   check_sum <dbl>
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  3

Country 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 region`
##    <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 t… Carib… No                
##  8 Antigua and Barbuda         28 Latin America and t… Carib… No                
##  9 Argentina                   32 Latin America and t… South… No                
## 10 Armenia                     51 Asia                 Weste… No                
## # ℹ 221 more rows
## # ℹ 3 more variables: `Least developed country` <chr>,
## #   `Sub-Saharan Africa` <chr>, FP2020 <chr>
??Country_and_area_classification

Country 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_names

Estimated 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.

mcmsupply::national_estimated_correlations_bivarlogitnormal
## # 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           0

Estimated 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           0

Estimated 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 (\(\delta_{k}\)).

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  1.4333294

Estimated 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