Package data
package_data.RmdThis 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.
-
Country and area classification
Country_and_area_classification_inclFP2020 -
Country names
country_names -
Estimated national correlations
estimated_national_correlations -
Estimated subnational correlations
estimated_global_subnational_correlationsandestimated_global_spatial_subnational_correlations -
Estimated model parameters for national one-country
runs
median_alpha_region_intercepts,precision_alpha_country_intercepts, andBspline_sigma_matrix_median, -
Estimated model parameters for subnational one-country
runs
median_alphacms,tau_alpha_pms_hat,sigma_delta_hat,spatial_sigma_delta_hat,global_provincial_neighbouradj, and the country-specific neighbourhood adjacency matrices in thedata/local_neighboursfolder -
Family planning source data
national_FPsource_dataandsubnat_FPsource_data
1. 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_classification1. 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_names3. 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.
estimated_national_correlations## # A tibble: 10 × 4
## row column public_cor private_cor
## <chr> <chr> <dbl> <dbl>
## 1 Implants Female Sterilization 0 0
## 2 Injectables Female Sterilization 0.2 0.7
## 3 IUD Female Sterilization 0.1 0.2
## 4 OC Pills Female Sterilization 0 0.7
## 5 Injectables Implants 0.1 0.1
## 6 IUD Implants 0.2 0
## 7 OC Pills Implants 0 0.1
## 8 IUD Injectables 0.2 0.1
## 9 OC Pills Injectables 0.5 0.8
## 10 OC Pills IUD 0.1 0.14. 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.
estimated_global_subnational_correlations## # A tibble: 10 × 4
## row column public_cor private_cor
## <chr> <chr> <dbl> <dbl>
## 1 Implants Female Sterilization 0.1 0
## 2 Injectables Female Sterilization 0.2 0
## 3 IUD Female Sterilization 0 0
## 4 OC Pills Female Sterilization 0.2 0
## 5 Injectables Implants 0 0
## 6 IUD Implants 0 0
## 7 OC Pills Implants 0 0
## 8 IUD Injectables 0.1 0
## 9 OC Pills Injectables 0.3 0
## 10 OC Pills IUD 0.2 05. Estimated model parameters for national one-country runs
These are the estimated parameters used in a one-country national
model run. median_alpha_region_intercepts are the regional
intercepts used to inform the country-specific intercept of the model,
the precision_alpha_country_intercepts are the associated
variance with these country-specific intercepts.
Bspline_sigma_matrix_median is the variance-covariance
matrix used to inform the Wishart prior on the first-order difference of
the spline coefficients.
median_alpha_region_intercepts## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.301550 1.833818 0.5270685 0.7537049 -0.5164741
## [2,] 3.690803 4.764637 3.1455633 4.5460172 1.7931031
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.583934 2.683237 1.794515 1.790627 -0.1707711
## [2,] 3.748790 4.751290 3.117329 4.546589 1.7916651
##
## , , 3
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.675565 1.697223 1.029494 1.005109 -0.5180739
## [2,] 3.730166 4.760698 3.077658 4.553346 1.6835570
##
## , , 4
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.735901 2.048935 1.242921 1.054806 0.1537607
## [2,] 3.688687 4.736354 2.988089 4.587235 1.8155029
##
## , , 5
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.517680 1.195353 1.517616 1.065101 -0.7044244
## [2,] 3.727607 4.780151 3.036655 4.557675 1.6806916
precision_alpha_country_intercepts## [1] 1.0338134 0.5713761
Bspline_sigma_matrix_median## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.17569510 0.00000000 0.04788189 0.02535736 0.00000000
## [2,] 0.00000000 0.59857633 0.04403929 0.09373054 0.00000000
## [3,] 0.04788189 0.04403929 0.32499002 0.06931805 0.16419734
## [4,] 0.02535736 0.09373054 0.06931805 0.37017274 0.03482467
## [5,] 0.00000000 0.00000000 0.16419734 0.03482467 0.32956170
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.96824736 0.000000000 0.63234459 0.03290151 0.650254947
## [2,] 0.00000000 0.002161332 0.00418859 0.00000000 0.004367744
## [3,] 0.63234459 0.004188590 0.85508582 0.01539978 0.693431845
## [4,] 0.03290151 0.000000000 0.01539978 0.02854580 0.015767092
## [5,] 0.65025495 0.004367744 0.69343184 0.01576709 0.9039390916. Estimated model parameters for subnational one-country runs
These are the estimated parameters used in a one-country subnational
model run. median_alphacms are the country-specific
intercepts used to inform the subnational province-specific intercepts
of the model, the tau_alpha_pms_hat are the associated
variance with these province-specific intercepts.
sigma_delta_hat is a variance-covariance matrix used to
inform the Wishart prior on the first-order difference of the spline
coefficients for the one-country subnational model.
median_alphacms## , , Afghanistan
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9375425 2.279007 0.9614377 0.7875669 -0.06685011
## [2,] 3.5584977 4.994330 3.9379771 5.1905018 3.21232620
##
## , , Benin
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.614509 2.278655 1.470588 0.6978635 -0.7425855
## [2,] 3.490346 5.012334 3.579064 5.1853863 1.4007400
##
## , , Burkina Faso
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.541036 2.815530 3.204556 1.315246 1.669336
## [2,] 3.509578 5.000577 3.201150 5.140294 1.547652
##
## , , Cameroon
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.3805835 1.926714 1.256077 1.139853 -0.3155968
## [2,] 3.6921759 4.962097 3.267732 5.101189 2.0331992
##
## , , Congo Democratic Republic
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2.054518 1.548893 0.6588179 1.141659 -1.656690
## [2,] 3.481285 4.997134 3.4840919 5.131586 3.157996
##
## , , Cote d'Ivoire
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.538612 2.586998 1.664344 1.217848 -1.097675
## [2,] 3.458898 5.019847 3.468302 5.113079 1.566580
##
## , , Ethiopia
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.234433 2.667722 1.251174 1.987835 0.1411864
## [2,] 3.463005 5.064522 3.883845 5.182309 3.1230510
##
## , , Ghana
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.502208 3.309794 2.364002 1.222324 -1.575251
## [2,] 3.496789 4.955784 3.452444 5.151519 3.398406
##
## , , Guinea
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.51673 2.645890 1.559947 1.02495 -0.4648663
## [2,] 3.44377 5.068471 3.412729 5.11952 1.2336673
##
## , , India
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.730801 2.220600 -0.6513015 0.9208466 -0.8533873
## [2,] 4.556863 5.013087 3.8707762 5.1084206 1.8644321
##
## , , Kenya
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.164999 1.626079 0.857908 1.126047 -0.0452362
## [2,] 3.728785 5.210681 4.355300 5.182808 3.5591716
##
## , , Liberia
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.513915 2.326553 1.176981 1.198190 0.725333
## [2,] 3.431356 4.983296 3.335941 5.137783 1.959348
##
## , , Madagascar
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.595684 1.751767 1.929113 0.0839175 0.5807928
## [2,] 3.508909 5.050697 3.437585 5.1815975 1.5837381
##
## , , Malawi
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.9217411 1.740507 1.890808 1.058043 1.463779
## [2,] 2.9162896 5.088991 2.976915 5.104717 2.213030
##
## , , Mali
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.515274 2.740144 1.027390 1.229250 -0.4811589
## [2,] 3.510741 5.021333 3.244636 5.178564 1.8553459
##
## , , Mozambique
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.893525 2.174765 2.563972 1.111325 1.930336
## [2,] 3.448346 5.030801 3.055654 5.109403 1.306878
##
## , , Nepal
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.290717 2.081615 1.019371 1.324007 0.3638721
## [2,] 1.417112 4.976004 3.804222 5.113763 2.7966377
##
## , , Niger
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.502122 2.642241 2.789944 1.237055 1.6032611
## [2,] 3.521154 4.970975 3.344750 5.154410 0.7229305
##
## , , Nigeria
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.788672 2.611483 1.219640 1.350439 -0.6213742
## [2,] 3.558362 4.992574 3.740164 5.191149 2.6168193
##
## , , Pakistan
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.281763 2.336591 0.8681863 1.097996 -0.171294
## [2,] 3.890569 4.975643 3.5851548 5.100859 1.376465
##
## , , Rwanda
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.686746 2.979814 3.376699 1.024101 2.326879
## [2,] 3.450294 4.972424 3.433565 5.181746 2.499687
##
## , , Senegal
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.782367 3.505220 2.929911 2.006801 1.281271
## [2,] 3.461560 4.970257 3.483374 5.122069 2.733137
##
## , , Tanzania
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.202425 2.509193 1.580458 1.358766 1.4954566
## [2,] 2.894547 4.865637 0.397618 5.056905 0.4148872
##
## , , Uganda
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.820563 1.760954 0.553650 1.240779 -0.7461476
## [2,] 3.627767 5.113125 4.000446 5.156258 3.0800601
##
## , , Zimbabwe
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.283054 1.874936 2.181961 1.102948 1.017927
## [2,] 3.556670 5.059976 3.233320 5.161617 1.947115
tau_alpha_pms_hat## [1] 1.386594 1.057686
sigma_delta_hat## , , 1
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 4.796649e-01 9.453555e-05 0.09463145 0.00000000 0.08959552
## [2,] 9.453555e-05 1.818119e-06 0.00000000 0.00000000 0.00000000
## [3,] 9.463145e-02 0.000000e+00 0.45723020 0.03885141 0.13153208
## [4,] 0.000000e+00 0.000000e+00 0.03885141 0.32953745 0.11229934
## [5,] 8.959552e-02 0.000000e+00 0.13153208 0.11229934 0.42567693
##
## , , 2
##
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.002637201 0.0000000000 0.0000000000 0.0000000000 0.00000000
## [2,] 0.000000000 0.0002575665 0.0000000000 0.0000000000 0.00000000
## [3,] 0.000000000 0.0000000000 0.0004097972 0.0000000000 0.00000000
## [4,] 0.000000000 0.0000000000 0.0000000000 0.0003940897 0.00000000
## [5,] 0.000000000 0.0000000000 0.0000000000 0.0000000000 0.038025787. 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.
national_FPsource_data## # A tibble: 2,448 × 9
## # Groups: year, Method, Country [903]
## year Country Super_region Method average_year sector_category proportion
## <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl>
## 1 2008 Sierra Leo… Western Afr… Injec… 2008. Commercial_med… 0.265
## 2 2008 Sierra Leo… Western Afr… Injec… 2008. Other 0.00540
## 3 2008 Sierra Leo… Western Afr… Injec… 2008. Public 0.730
## 4 2008 Sierra Leo… Western Afr… OC Pi… 2008. Commercial_med… 0.587
## 5 2008 Sierra Leo… Western Afr… OC Pi… 2008. Other 0.0224
## 6 2008 Sierra Leo… Western Afr… OC Pi… 2008. Public 0.390
## 7 2013 Sierra Leo… Western Afr… Impla… 2014. Commercial_med… 0.240
## 8 2013 Sierra Leo… Western Afr… Impla… 2014. Other 0.00567
## 9 2013 Sierra Leo… Western Afr… Impla… 2014. Public 0.755
## 10 2013 Sierra Leo… Western Afr… Injec… 2014. Commercial_med… 0.198
## # ℹ 2,438 more rows
## # ℹ 2 more variables: SE.proportion <dbl>, n <dbl>
subnat_FPsource_data## # A tibble: 6,940 × 8
## Country Region Method average_year sector_categories proportion SE.proportion
## <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl>
## 1 Zimbab… Bulaw… Femal… 2016. Other 2.14e-11 0
## 2 Zimbab… Harare Femal… 2016. Other 1.95e-11 0
## 3 Zimbab… Manic… Femal… 2016. Other 2.06e-11 0
## 4 Zimbab… Masho… Femal… 2016. Other 5.32e-11 0
## 5 Zimbab… Masho… Femal… 2016. Other 5.60e-11 0
## 6 Zimbab… Masho… Femal… 2016. Other 2.12e-11 0
## 7 Zimbab… Masvi… Femal… 2016. Other 5.59e-11 0
## 8 Zimbab… Matab… Femal… 2016. Other 1.93e-11 0
## 9 Zimbab… Matab… Femal… 2016. Other 2.29e- 1 0.154
## 10 Zimbab… Midla… Femal… 2016. Other 2.09e-11 0
## # ℹ 6,930 more rows
## # ℹ 1 more variable: n <int>