vignettes/aggregating_estimates.md

In FPRgroup/FPEMcountry: fpemlocal

Aggregating estimates from multiple fits with custom user data

Introduction

In this vignette we will fit FPET to multiple countries and aggregate the samples to obtain results for aggregate levels. We will fit models for Botswana and Lesotho, country codes 72 and 426 respectively.

Table of Contents

1. Fit models
2. Read in population data4
3. Calculate results

Fit models

First, fit the models with the function fit_fp_c.

fit_botswana <- fit_fp_c(
  surveydata_filepath = "data-raw/manuscript_example_data/Botswana_72_married_example.csv",
  division_numeric_code = 72,
  is_in_union = "Y",
  first_year = 1970,
  last_year = 2030
)

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_character(),
##   division_numeric_code = col_double(),
##   start_date = col_double(),
##   end_date = col_double(),
##   contraceptive_use_modern = col_double(),
##   contraceptive_use_traditional = col_double(),
##   contraceptive_use_any = col_double(),
##   unmet_need_modern = col_logical(),
##   unmet_need_any = col_double(),
##   pertaining_to_methods_used_since_last_pregnancy_reason = col_logical(),
##   geographical_region_bias_reason = col_logical(),
##   non_pregnant_and_other_positive_biases_reason = col_logical(),
##   traditional_method_bias_reason = col_logical(),
##   se_modern = col_logical(),
##   se_traditional = col_logical(),
##   se_unmet_need = col_logical(),
##   se_log_r_modern_no_use = col_logical(),
##   se_log_r_traditional_no_use = col_logical(),
##   se_log_r_unmet_no_need = col_logical(),
##   source_id = col_double(),
##   se_log_r_unmet_no_need_imputed = col_double()
##   # ... with 2 more columns
## )
## i Use `spec()` for the full column specifications.

fit_lesotho <- fit_fp_c(
  surveydata_filepath = "data-raw/manuscript_example_data/Lesotho_426_married_example.csv",
  division_numeric_code = 426,
  is_in_union = "Y",
  first_year = 1970,
  last_year = 2030
)

## 
## -- Column specification --------------------------------------------------------
## cols(
##   .default = col_double(),
##   is_in_union = col_character(),
##   age_range = col_character(),
##   data_series_type = col_character(),
##   group_type_relative_to_baseline = col_character(),
##   unmet_need_modern = col_logical(),
##   is_pertaining_to_methods_used_since_last_pregnancy = col_character(),
##   pertaining_to_methods_used_since_last_pregnancy_reason = col_logical(),
##   has_geographical_region_bias = col_character(),
##   geographical_region_bias_reason = col_logical(),
##   has_non_pregnant_and_other_positive_biases = col_character(),
##   non_pregnant_and_other_positive_biases_reason = col_logical(),
##   age_group_bias = col_character(),
##   modern_method_bias = col_character(),
##   has_traditional_method_bias = col_character(),
##   traditional_method_bias_reason = col_logical(),
##   has_absence_of_probing_questions_bias = col_character(),
##   record_id = col_character()
## )
## i Use `spec()` for the full column specifications.

Read in population data

Read in population data for the populations of interest. Create a single dataset with the function rbind.

popdata_botswana <- read.csv("data-raw/manuscript_example_data/Botswana_72_married_popdata_example.csv")
popdata_lesotho <- read.csv("data-raw/manuscript_example_data/Lesotho_426_married_popdata_example.csv")
popdata <- rbind(popdata_botswana, popdata_lesotho)

Calculate results

Supply the fits in a list and the population data to the function calc_fp_aggregate. The resulting object is a list of long format tibbles with family planning estimates.

results <- calc_fp_aggregate(fits = list(fit_botswana, fit_lesotho),
                   population_data = popdata)
results %>% head()

## $contraceptive_use_any
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0998
##  2  1971 mean       0.106 
##  3  1972 mean       0.113 
##  4  1973 mean       0.121 
##  5  1974 mean       0.128 
##  6  1975 mean       0.136 
##  7  1976 mean       0.145 
##  8  1977 mean       0.154 
##  9  1978 mean       0.164 
## 10  1979 mean       0.174 
## # ... with 478 more rows
## 
## $contraceptive_use_modern
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0817
##  2  1971 mean       0.0887
##  3  1972 mean       0.0963
##  4  1973 mean       0.104 
##  5  1974 mean       0.112 
##  6  1975 mean       0.121 
##  7  1976 mean       0.130 
##  8  1977 mean       0.139 
##  9  1978 mean       0.150 
## 10  1979 mean       0.160 
## # ... with 478 more rows
## 
## $contraceptive_use_traditional
## # A tibble: 488 x 3
##     year percentile  value
##    <int> <chr>       <dbl>
##  1  1970 mean       0.0181
##  2  1971 mean       0.0177
##  3  1972 mean       0.0171
##  4  1973 mean       0.0165
##  5  1974 mean       0.0160
##  6  1975 mean       0.0154
##  7  1976 mean       0.0148
##  8  1977 mean       0.0143
##  9  1978 mean       0.0138
## 10  1979 mean       0.0133
## # ... with 478 more rows
## 
## $non_use
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.900
##  2  1971 mean       0.894
##  3  1972 mean       0.887
##  4  1973 mean       0.879
##  5  1974 mean       0.872
##  6  1975 mean       0.864
##  7  1976 mean       0.855
##  8  1977 mean       0.846
##  9  1978 mean       0.836
## 10  1979 mean       0.826
## # ... with 478 more rows
## 
## $unmet_need_any
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.326
##  2  1971 mean       0.326
##  3  1972 mean       0.325
##  4  1973 mean       0.324
##  5  1974 mean       0.323
##  6  1975 mean       0.321
##  7  1976 mean       0.319
##  8  1977 mean       0.317
##  9  1978 mean       0.315
## 10  1979 mean       0.313
## # ... with 478 more rows
## 
## $unmet_need_modern
## # A tibble: 488 x 3
##     year percentile value
##    <int> <chr>      <dbl>
##  1  1970 mean       0.344
##  2  1971 mean       0.343
##  3  1972 mean       0.342
##  4  1973 mean       0.340
##  5  1974 mean       0.339
##  6  1975 mean       0.336
##  7  1976 mean       0.334
##  8  1977 mean       0.332
##  9  1978 mean       0.329
## 10  1979 mean       0.327
## # ... with 478 more rows

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