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Specify Miscount Data Model

Source: R/bage_mod-functions.R
set_datamod_miscount.Rd

Specify a data model for the outcome in a Poisson model, where the outcome is subject to undercount and overcount.

Usage

set_datamod_miscount(mod, prob, rate)

Arguments

mod

An object of class "bage_mod_pois", created with mod_pois().

prob

The prior for the probability that a person or event in the target population will correctly enumerated. A data frame with a variable called "mean", a variable called "disp", and, optionally, one or more 'by' variables.

rate

The prior for the overcoverage rate. A data frame with a variable called "mean", a variable called "disp", and, optionally, one or more 'by' variables.

Value

A revised version of mod.

Details

The miscount data model is essentially a combination of the undercount and overcount data models. It assumes that reported outcome is the sum of two quantities:

  1. Units from target population, undercounted People or events belonging to the target population, in which each unit's inclusion probability is less than 1.

  2. Overcount People or events that do not belong to target population, or that are counted more than once.

If, for instance, a census enumerates 91 people from a true population of 100, but also mistakenly enumerates a further 6 people, then

  • the true value for the outcome variable is 100

  • the value for the undercounted target population is 91,

  • the value for the overcount is 6, and

  • the observed value for the outcome variable is 91 + 6 = 97.

The prob argument

The prob argument specifies a prior distribution for the probability that a person or event in the target population is included in the reported outcome. prob is a data frame with a variable called "mean", a variable called "disp", and, optionally, one or more 'by' variables. For instance, a prob of

data.frame(sex = c("Female", "Male"),
           mean = c(0.95, 0.92),
           disp = c(0.02, 0.015))

implies that the expected value for the inclusion probability is 0.95 for females and 0.92 for males, with slightly more uncertainty for females than for males.

The rate argument

The rate argument specifies a prior distribution for the overcoverage rate. rate is a data frame with a variable called "mean", a variable called "disp", and, optionally, one or more 'by' variables. For instance, a rate of

data.frame(mean = 0.03, disp = 0.1)

implies that the expected value for the overcoverage rate is 0.03, with a dispersion of 0.1. Since no 'by' variables are included, the same mean and dispersion values are applied to all cells.

Mathematical details

The model for the observed outcome is

$$y_i^{\text{obs}} = u_i + v_i$$ $$u_i \sim \text{Binomial}(y_i^{\text{true}}, \pi_{g[i]})$$ $$v_i \sim \text{Poisson}(\kappa_{h[i]} \gamma_i w_i)$$ $$\pi_g \sim \text{Beta}(m_g^{(\pi)} / d_g^{(\pi)}, (1-m_g^{(\pi)}) / d_g^{(\pi)})$$ $$\kappa_h \sim \text{Gamma}(1/d_h^{(\kappa)}, 1/(d_h^{(\kappa)} m_h^{(\kappa)}))$$

where

  • \(y_i^{\text{obs}}\) is the observed outcome for cell \(i\);

  • \(y_i^{\text{true}}\) is the true outcome for cell \(i\);

  • \(\gamma_i\) is the rate for cell \(i\);

  • \(w_i\) is exposure for cell \(i\);

  • \(\pi_{g[i]}\) is the probability that a member of the target population in cell \(i\) is correctly enumerated in that cell;

  • \(\kappa_{h[i]}\) is the overcoverage rate for cell \(i\);

  • \(m_g^{(\pi)}\) is the expected value for \(\pi_g\) (specified via prob);

  • \(d_g^{(\pi)}\) is disperson for \(\pi_g\) (specified via prob);

  • \(m_h^{(\kappa)}\) is the expected value for \(\kappa_h\) (specified via rate); and

  • \(d_h^{(\kappa)}\) is disperson for \(\kappa_h\) (specified via rate).

See also

Examples

## specify 'prob' and 'rate'
prob <- data.frame(sex = c("Female", "Male"),
                   mean = c(0.95, 0.97),
                   disp = c(0.05, 0.05))
rate <- data.frame(mean = 0.03, disp = 0.15)

## specify model
mod <- mod_pois(divorces ~ age * sex + time,
                data = nzl_divorces,
                exposure = population) |>
  set_datamod_miscount(prob = prob, rate = rate)
mod
#> 
#>     ------ Unfitted Poisson model ------
#> 
#>    divorces ~ age * sex + time
#> 
#>                  exposure: population
#>                data model: miscount
#> 
#>         term  prior along n_par n_par_free
#>  (Intercept) NFix()     -     1          1
#>          age   RW()   age    11         11
#>          sex NFix()     -     2          2
#>         time   RW()  time    11         11
#>      age:sex   RW()   age    22         22
#> 
#>  disp: mean = 1
#> 
#>  n_draw var_time var_age var_sexgender
#>    1000     time     age           sex
#> 

## fit model
mod <- mod |>
  fit()
#> Building log-posterior function...
#> Finding maximum...
#> Drawing values for hyper-parameters...
mod
#> 
#>     ------ Fitted Poisson model ------
#> 
#>    divorces ~ age * sex + time
#> 
#>                  exposure: population
#>                data model: miscount
#> 
#>         term  prior along n_par n_par_free std_dev
#>  (Intercept) NFix()     -     1          1       -
#>          age   RW()   age    11         11    2.00
#>          sex NFix()     -     2          2    0.36
#>         time   RW()  time    11         11    0.13
#>      age:sex   RW()   age    22         22    0.33
#> 
#>  disp: mean = 1
#> 
#>  n_draw var_time var_age var_sexgender optimizer
#>    1000     time     age           sex    nlminb
#> 
#>  time_total time_max time_draw iter converged                    message
#>        0.36     0.19      0.14   17      TRUE   relative convergence (4)
#> 

## original data, plus imputed values for outcome
mod |>
  augment()
#> ℹ Adding variable `.divorces` with true values for `divorces`.
#> # A tibble: 242 × 9
#>    age   sex     time divorces    .divorces population .observed
#>    <fct> <chr>  <int>    <dbl> <rdbl<1000>>      <dbl>     <dbl>
#>  1 15-19 Female  2011        0     0 (0, 1)     154460 0        
#>  2 15-19 Female  2012        6     6 (5, 7)     153060 0.0000392
#>  3 15-19 Female  2013        3     3 (2, 4)     152250 0.0000197
#>  4 15-19 Female  2014        3     3 (2, 4)     152020 0.0000197
#>  5 15-19 Female  2015        3     3 (2, 4)     152970 0.0000196
#>  6 15-19 Female  2016        3     3 (2, 4)     154170 0.0000195
#>  7 15-19 Female  2017        6     6 (5, 7)     154450 0.0000388
#>  8 15-19 Female  2018        0     0 (0, 1)     154170 0        
#>  9 15-19 Female  2019        3     3 (2, 4)     154760 0.0000194
#> 10 15-19 Female  2020        0     0 (0, 1)     154480 0        
#> # ℹ 232 more rows
#> # ℹ 2 more variables: .fitted <rdbl<1000>>, .expected <rdbl<1000>>

## parameter estimates
library(dplyr)
mod |>
  components() |>
  filter(term == "datamod")
#> # A tibble: 3 × 4
#>   term    component level               .fitted
#>   <chr>   <chr>     <chr>          <rdbl<1000>>
#> 1 datamod prob      Female       0.96 (0.82, 1)
#> 2 datamod prob      Male         0.98 (0.86, 1)
#> 3 datamod rate      rate   0.027 (0.011, 0.055)

## the data have in fact been confidentialized,
## so we account for that, in addition
## to accounting for undercoverage and
## overcoverage
mod <- mod |>
 set_confidential_rr3() |>
 fit()
#> Building log-posterior function...
#> Finding maximum...
#> Drawing values for hyper-parameters...
mod
#> 
#>     ------ Fitted Poisson model ------
#> 
#>    divorces ~ age * sex + time
#> 
#>                  exposure: population
#>                data model: miscount
#>       confidentialization: rr3
#> 
#>         term  prior along n_par n_par_free std_dev
#>  (Intercept) NFix()     -     1          1       -
#>          age   RW()   age    11         11    1.94
#>          sex NFix()     -     2          2    0.32
#>         time   RW()  time    11         11    0.13
#>      age:sex   RW()   age    22         22    0.30
#> 
#>  disp: mean = 1
#> 
#>  n_draw var_time var_age var_sexgender optimizer
#>    1000     time     age           sex    nlminb
#> 
#>  time_total time_max time_draw iter converged                    message
#>        0.81     0.43      0.33   17      TRUE   relative convergence (4)
#>