Extract Components used by SVD Summary

Extract the matrix and offset used by a scaled SVD summary of a demographic database.

Usage

# S3 method for class 'bage_ssvd'
components(
  object,
  v = NULL,
  n_comp = NULL,
  indep = NULL,
  age_labels = NULL,
  ...
)

Arguments

object

An object of class "bage_ssvd".

v

Version of scaled SVD components to use. If no value is suppled, the most recent version is used.

n_comp

The number of components. The default is half the total number of components of object.

indep

Whether to use independent or joint SVDs for each sex/gender, if the data contains a sex/gender variable. The default is to use independent SVDs. To obtain results for the total population when the data contains a sex/gender variable, set indep to NA.

age_labels

Age labels for the desired age or age-sex profile. If no labels are supplied, the most detailed profile available is used.

...

Not currently used.

Value

A tibble with the offset and components.

Scaled SVDs of demographic databases in bage

• HMD Mortality rates from the Human Mortality Database.

• HFD Fertility rates from the Human Fertility Database.

• LFP Labor forcce participation rates from the OECD.

See also

• generate() Randomly generate age-profiles, or age-sex profiles, based on a scaled SVD summary.

• SVD() SVD prior for terms involving age.

• SVD_AR1(), SVD_AR(), SVD_RW(), SVD_RW2() Dynamic SVD priors for terms involving age and time.

• poputils::age_labels() Generate age labels.

Examples

## females and males modeled independently
components(LFP, n_comp = 3)
#> # A tibble: 104 × 4
#>    component sex    age     value
#>    <fct>     <chr>  <chr>   <dbl>
#>  1 Offset    Female 15-19 -1.02  
#>  2 Offset    Female 20-24  0.470 
#>  3 Offset    Female 25-29  0.949 
#>  4 Offset    Female 30-34  0.924 
#>  5 Offset    Female 35-39  1.02  
#>  6 Offset    Female 40-44  1.12  
#>  7 Offset    Female 45-49  1.05  
#>  8 Offset    Female 50-54  0.729 
#>  9 Offset    Female 55-59  0.0967
#> 10 Offset    Female 60-64 -0.947 
#> # ℹ 94 more rows

## joint model for females and males
components(LFP, indep = FALSE, n_comp = 3)
#> # A tibble: 104 × 4
#>    component sex    age    value
#>    <fct>     <chr>  <chr>  <dbl>
#>  1 Offset    Female 15-19 -1.03 
#>  2 Offset    Female 20-24  0.496
#>  3 Offset    Female 25-29  0.936
#>  4 Offset    Female 30-34  0.895
#>  5 Offset    Female 35-39  1.00 
#>  6 Offset    Female 40-44  1.12 
#>  7 Offset    Female 45-49  1.06 
#>  8 Offset    Female 50-54  0.740
#>  9 Offset    Female 55-59  0.108
#> 10 Offset    Female 60-64 -0.949
#> # ℹ 94 more rows

## females and males combined
components(LFP, indep = NA, n_comp = 3)
#> # A tibble: 52 × 3
#>    component age    value
#>    <fct>     <chr>  <dbl>
#>  1 Offset    15-19 -0.839
#>  2 Offset    20-24  0.774
#>  3 Offset    25-29  1.48 
#>  4 Offset    30-34  1.58 
#>  5 Offset    35-39  1.66 
#>  6 Offset    40-44  1.70 
#>  7 Offset    45-49  1.59 
#>  8 Offset    50-54  1.26 
#>  9 Offset    55-59  0.632
#> 10 Offset    60-64 -0.434
#> # ℹ 42 more rows

## specify age groups
labels <- poputils::age_labels(type = "five", min = 15, max = 60)
components(LFP, age_labels = labels)
#> # A tibble: 72 × 4
#>    component sex    age    value
#>    <fct>     <chr>  <chr>  <dbl>
#>  1 Offset    Female 15-19 -1.16 
#>  2 Offset    Female 20-24  0.475
#>  3 Offset    Female 25-29  0.986
#>  4 Offset    Female 30-34  1.00 
#>  5 Offset    Female 35-39  1.10 
#>  6 Offset    Female 40-44  1.18 
#>  7 Offset    Female 45-49  1.09 
#>  8 Offset    Female 50-54  0.765
#>  9 Offset    Female 55-59  0.122
#> 10 Offset    Male   15-19 -0.888
#> # ℹ 62 more rows