fccp_open0: Cohort Component Population Projection Model based on a...
In gjabel/demoproj: Demographic Projection Models

Description Usage Arguments Value Examples

A cohort component projection model based on a closed female population,

N(t+5) = L[t,t+5] ≤ft( N(t) + \frac{1}{2} I[t,t+5] \right) + \frac{1}{2} I[t,t+5]

where the Leslie matrix, eqnL, is created given user defined age specific fertility and survivorship rates.

fccp_open0(n = NULL, x = NULL, p = NULL, Nx = NULL, sx = NULL,
  fx = NULL, sn = NULL, sex_ratio = 1/(1 + 1.05), ex = NULL,
  Ix = NULL, tidy_output = TRUE, age_lab = x, gender_lab = "Female",
  ...)

fccp_open(n = NULL, x = NULL, p = NULL, Nx = NULL, sx = NULL,
  fx = NULL, sn = NULL, sex_ratio = 1/(1 + 1.05), ex = NULL,
  Ix = NULL, tidy_output = TRUE, age_lab = x, gender_lab = "Female",
  ...)

`n`	Numeric value for the number of projection steps.
`x`	Vector containing a character string of age group labels.
`p`	Numeric value for step size of the population projection.
`Nx`	Vector containing numeric values of the initial female population size in each age group (`x`).
`sx, fx, ex, Ix`	Vectors containing numeric values of the age specific female survival, fertility and emigration rates and immigration counts. If `fccp_open0` is used a single vector is required. If `fccp_open` is used a matrix of period specific rates (or counts for immigration) is required, where rows represent age groups and columns future periods. If a single vector is passed to `fccp_open` a matrix based on constant assumptions in all future rates and immigration counts will be constructed.
`sn, sex_ratio`	Numeric value of the survivorship of new-born female babies from birth to the end of the interval and the sex ratio at birth of new-born babies. If `fccp_open0` is used a single value is required. If `fccp_open` is used a vector of period specific values is required. If a single value is passed to `fccp_open` a vector based on constant assumptions in all future rates will be constructed.
`tidy_output`	Logical value to indicate if projection output should be in a tidy data format (`TRUE`, the default) or as a `matrix` where rows represent age and gender groups and columns the projection year.
`age_lab, gender_lab`	Vector containing a character string of age and gender group labels. Only used if projection output is in a tidy data format. See `tidy_pp` for more details.
`...`	Additional arguments passed to `tidy_pp` to build a tidy data frame (if chosen from `tidy_output`).

Projected populations by age and gender for n future steps, given the age specific fertility, survivorship and emigration rates and immigration counts. Depending on the tidy_output value the projections will be returned as either a matrix or a tibble. Both versions contain the initial population sizes given in Nx.

fccp_open0 produces population projections based strictly on constant future rates.

fccp_open produces population projections based non-constant future rates.

library(dplyr)
df0 <- sweden1993 %>%
    #immigration from estimated count and net age pattern
    mutate(Ix_f = 138000 * Mx_f/sum(Mx_f),
    Ix_f = round(Ix_f),
    #emigration as remainder
    Ex_f = Ix_f - Mx_f ,
    #emigration rate for projection model
    ex_f = Ex_f/Nx_f)

# matrix output
fccp_open0(n = 5, x = df0$x, p = 5, Nx = df0$Nx_f,
           sx = df0$sx_f,
           fx = df0$fx, sn = df0$Lx_f[1]/(5*100000),
           ex = df0$ex_f, Ix = df0$Ix_f,
           tidy_output = FALSE)

# tidy data frame output
fccp_open0(n = 5, x = df0$x, p = 5, Nx = df0$Nx_f,
           sx = df0$sx_f,
           fx = df0$fx, sn = df0$Lx_f[1]/(5*100000),
           ex = df0$ex_f, Ix = df0$Ix_f,
           year0 = 1993, age_lab = df0$age)

# setting up non-constant future immigrant counts
II <- matrix(df0$Ix_f, nrow = length(df0$Ix_f), ncol = 5)
II <- sweep(II, 2, seq(from = 1, to = 1.5, length = 5), "*")
# immigration increase
colSums(II)
# run projection with increasing immigration, fx, sx and ex remains constant
fccp_open(n = 5, x = df0$x, p = 5, Nx = df0$Nx_f,
          sx = df0$sx_f,
          fx = df0$fx, sn = df0$Lx_f[1]/(5*100000),
          ex = df0$ex_f, Ix = II,
          tidy_output = FALSE)