vignettes/all_women.md

In FPRgroup/FPEMcountry: fpemlocal

Estimating family planning indicators for all women

Introduction

In this vignette we obtain estimates for all women with package datasets. By default, functions utilize UNPD datasets.

library(fpemlocal)

1. Fit models and obtain samples for all women fit_fp_c
2. Calculate point estimates for indicators fpet_calculate_indicaotrs
3. Plot the point estimates against the survey data plot_fp_c

1. Fit models and obtain samples for all women

fit_fp_c is a wrapper function to run the one-country implementation of the family planning estimation model. Specify the division numeric code, the union status of women (denote all women with “ALL”), and the time frame.

fitlist <- fit_fp_c(
  is_in_union = "ALL",
  division_numeric_code = 4,
  first_year = 1970,
  last_year = 2030
)

Obtaining results for all women entails running the in-union and not-in-union model. In this case, fit_fp_c returns a named list of fits.

fitlist %>% names

## [1] "Y"   "N"   "ALL"

2. Calculate point estimates for indicators

Calculate point estimates for family planning indicators with the function calc_fp_c.

calc_fp_c utilizes pmap from the tidyverse package purr allowing it to act on any number of fits. We will supply the entire list of fits from fit_fp_c.

resultlist <- calc_fp_c(fitlist)

Like the previous function, calc_fp_c returns a list. Since we supplied three fits the function returns three sets of calculated family planning indicators.

resultlist %>% names

## [1] "Y"   "N"   "ALL"

A set of results here consist of the following family planning indicators

resultlist$ALL %>% names

##  [1] "contraceptive_use_any"                     
##  [2] "contraceptive_use_modern"                  
##  [3] "contraceptive_use_traditional"             
##  [4] "non_use"                                   
##  [5] "unmet_need_any"                            
##  [6] "unmet_need_modern"                         
##  [7] "demand"                                    
##  [8] "demand_modern"                             
##  [9] "demand_satisfied"                          
## [10] "demand_satisfied_modern"                   
## [11] "no_need"                                   
## [12] "contraceptive_use_any_population_counts"   
## [13] "contraceptive_use_modern_population_counts"
## [14] "traditional_cpr_population_counts"         
## [15] "non_use_population_counts"                 
## [16] "unmet_need_population_counts"              
## [17] "unmet_need_modern_population_counts"       
## [18] "demand_modern_population_counts"           
## [19] "demand_population_counts"                  
## [20] "demand_satisfied_population_counts"        
## [21] "demand_satisfied_modern_population_counts" 
## [22] "no_need_population_counts"

The point estimates for each indicator are long-format tibbles. Let’s take a look at the tibble for the indicator contraceptive_use_modern

resultlist$ALL$contraceptive_use_modern

## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0116
##  2  1971 mean       0.0123
##  3  1972 mean       0.0130
##  4  1973 mean       0.0138
##  5  1974 mean       0.0147
##  6  1975 mean       0.0156
##  7  1976 mean       0.0167
##  8  1977 mean       0.0179
##  9  1978 mean       0.0191
## 10  1979 mean       0.0205
## # ... with 478 more rows

3. Plot the point estimates and the survey data

fpemlocal also includes a function named plot_fp_c to plot the calculated point estimates against the survey data. The arguments to this function are, the fit object from step 1, the results from step 2, and a vector of indicator names. The vector of indicator names corresponds to the names which appear in the results from step 2. This function also handles lists.

plot_fp_c(
  fitlist,
  resultlist,
  indicators = c(
    "unmet_need_any",
    "contraceptive_use_modern",
    "contraceptive_use_traditional",
    "contraceptive_use_any"
    )
  )

## $Y
## $Y$unmet_need_any

## 
## $Y$contraceptive_use_modern

## 
## $Y$contraceptive_use_traditional

## 
## $Y$contraceptive_use_any

## 
## 
## $N
## $N$unmet_need_any

## 
## $N$contraceptive_use_modern

## 
## $N$contraceptive_use_traditional

## 
## $N$contraceptive_use_any

## 
## 
## $ALL
## $ALL$unmet_need_any

## 
## $ALL$contraceptive_use_modern

## 
## $ALL$contraceptive_use_traditional

## 
## $ALL$contraceptive_use_any

FPRgroup/FPEMcountry documentation built on April 24, 2023, 4:32 p.m.