Estimate kin counts in a one-sex framework.

Source: R/kin.R

Implementation of Goodman-Keyfitz-Pullum equations in a matrix framework. This produce a matrilineal (or patrilineal) kin count distribution by kin and age.

kin(
  p = NULL,
  f = NULL,
  time_invariant = TRUE,
  pi = NULL,
  n = NULL,
  output_cohort = NULL,
  output_period = NULL,
  output_kin = NULL,
  output_age_focal = NULL,
  birth_female = 1/2.04,
  summary_kin = TRUE
)

Arguments

p

numeric. A vector (atomic) or matrix with probabilities (or survival ratios, or transition between age class in a more general perspective) with rows as ages (and columns as years in case of matrix, being the name of each col the year).

f

numeric. Same as p but for fertility rates.

time_invariant

logical. Constant assumption for a given year rates. Default TRUE.

pi

numeric. Same as U but for childbearing distribution (sum to 1). Optional.

n

numeric. Only for time_invariant = FALSE. Same as p but for population distribution (counts or %). Optional.

output_cohort

integer. Vector of year cohorts for returning results. Should be within input data years range.

output_period

integer. Vector of period years for returning results. Should be within input data years range.

output_kin

character. kin types to return: "m" for mother, "d" for daughter,...

output_age_focal

integer. Vector of ages to select (and make faster the run).

birth_female

numeric. Female portion at birth. This multiplies f argument. If f is already for female offspring,

summary_kin

logical. Whether or not include kin_summary table (see output details). Default TRUE. this needs to be set as 1.

Value

A list with:

  • kin_full a data frame with year, cohort, Focal´s age, related ages and type of kin (for example d is daughter, oa is older aunts, etc.), including living and dead kin at that age.

  • kin_summary a data frame with Focal´s age, related ages and type of kin, with indicators obtained processing kin_full, grouping by cohort or period (depending on the given arguments):

    • count_living: count of living kin at actual age of Focal

    • mean_age: mean age of each type of living kin.

    • sd_age: standard deviation of age of each type of living kin.

    • count_death: count of dead kin at specific age of Focal.

    • count_cum_death: cumulated count of dead kin until specific age of Focal.

    • mean_age_lost: mean age where Focal lost her relative.

Details

See Caswell (2019) and Caswell (2021) for details on formulas. One sex only (female by default).

Examples

# Kin expected matrilineal count for a Swedish female based on 2015 rates.
swe_surv_2015 <- swe_px[,"2015"]
swe_asfr_2015 <- swe_asfr[,"2015"]
# Run kinship models
swe_2015 <- kin(p = swe_surv_2015, f = swe_asfr_2015)
head(swe_2015$kin_summary)
#> # A tibble: 6 × 10
#>   age_focal kin   year  cohort count_living mean_age sd_age count_dead
#>       <int> <chr> <lgl> <lgl>         <dbl>    <dbl>  <dbl>      <dbl>
#> 1         0 coa   NA    NA           0.275      8.32   6.14  0.0000633
#> 2         0 cya   NA    NA           0.0898     4.05   3.68  0.0000370
#> 3         0 d     NA    NA           0        NaN    NaN     0        
#> 4         0 gd    NA    NA           0        NaN    NaN     0        
#> 5         0 ggd   NA    NA           0        NaN    NaN     0        
#> 6         0 ggm   NA    NA           0.320     84.4    6.43  0.0287   
#> # ℹ 2 more variables: count_cum_dead <dbl>, mean_age_lost <dbl>