The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Title: Estimate Rogers-Castro Migration Age Schedules with Bayesian Models
Version: 0.2.0
Description: A collection of functions to estimate Rogers-Castro migration age schedules using 'Stan'. This model which describes the fundamental relationship between migration and age in the form of a flexible multi-exponential migration model was most notably proposed in Rogers and Castro (1978) <doi:10.1068/a100475>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.1.2
Biarch: true
Depends: R (≥ 3.4.0)
Imports: methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.1.1), Rdpack, dplyr, rlang, stats, tibble, tidybayes, magrittr, shiny, shinythemes
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Suggests: knitr, rmarkdown, ggplot2
SystemRequirements: GNU make
VignetteBuilder: knitr
RdMacros: Rdpack
NeedsCompilation: yes
Packaged: 2022-01-16 02:28:09 UTC; jessieyeung
Author: Jessie Yeung ORCID iD [aut, cre], Monica Alexander ORCID iD [aut], Tim Riffe ORCID iD [aut]
Maintainer: Jessie Yeung <jessieyeung1@gmail.com>
Repository: CRAN
Date/Publication: 2022-01-17 08:22:42 UTC

The 'rcbayes' package.

Description

A DESCRIPTION OF THE PACKAGE

References

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org

Set initial values for Rogers-Castro migration model

Description

Choose initial values for parameters in the Rogers-Castro model in a strategic way based on your data. Provide these initial values to improve convergence of model. Intended to be used with rcbayes::mig_estimate_rc as an additional input into 'Stan'.

Usage

init_rc(
  ages,
  migrants,
  pop,
  mx,
  pre_working_age,
  working_age,
  retirement,
  post_retirement,
  nchains = 4,
  net_mig
)

Arguments

ages

numeric. A vector of integers for ages.

migrants

numeric. A vector of integers for observed age-specific migrants.

pop

numeric. A vector of integers for age-specific population or sample sizes, of which "migrants" experienced a migration event.

mx

numeric. A vector of age-specific migration rates.

pre_working_age

logical (TRUE/FALSE). Whether or not you are including pre working age component.

working_age

logical (TRUE/FALSE). Whether or not you are including working age component.

retirement

logical (TRUE/FALSE). Whether or not you are including retirement age component.

post_retirement

logical (TRUE/FALSE). Whether or not you are including post retirement age component.

nchains

numeric. A positive integer specifying the number of Markov chains. Should be 4 unless changed otherwise.

net_mig

numeric. Deprecated argument, use migrants instead.

Value

A list of length nchains. Each element of the list is a list of numeric values. Within the inner lists, there is one element for every model parameter.

Examples

# Ex. 1: Using ages, migrants, and population
ages <- 0:80
migrants <- c(202,215,167,188,206,189,164,
            158,197,185,176,173,167,198,
            203,237,249,274,319,345,487,
            491,521,505,529,527,521,529,
            507,484,467,439,399,399,380,
            368,310,324,289,292,270,269,
            285,254,245,265,257,258,263,
            253,346,293,332,346,349,355,
            386,346,344,352,331,320,307,
            320,310,258,254,243,256,263,
            183,169,172,160,166,113,132,
            111,130,110,113)
pop <- c(105505,105505,105505,105505,105505,
        106126,106126,106126,106126,106126,
        100104,100104,100104,100104,100104,
        114880,114880,114880,114880,114880,
        136845,136845,136845,136845,136845,
        136582,136582,136582,136582,136582,
        141935,141935,141935,141935,141935,
        134097,134097,134097,134097,134097,
        130769,130769,130769,130769,130769,
        133718,133718,133718,133718,133718,
        154178,154178,154178,154178,154178,
        145386,145386,145386,145386,145386,
        126270,126270,126270,126270,126270,
        108314,108314,108314,108314,108314,
        79827,79827,79827,79827,79827,59556,
        59556,59556,59556,59556,59556)

#compute initial values
iv <- init_rc(ages=ages, migrants=migrants, pop=pop,
             pre_working_age=TRUE,
             working_age=TRUE,
             retirement=TRUE,
             post_retirement=TRUE)

# Ex 2: Using ages and mx
ages <- 0:80
mx <- c(0.001914601, 0.002037818, 0.001582863, 0.001781906,
        0.001952514, 0.001780902, 0.001545333, 0.001488796,
        0.001856284, 0.001743211, 0.001758172, 0.001728203,
        0.001668265, 0.001977943, 0.002027891, 0.002063022,
        0.002167479, 0.002385097, 0.002776811, 0.003003134,
        0.003558771, 0.003588001, 0.003807227, 0.003690307,
        0.003865687, 0.003858488, 0.003814558, 0.003873131,
        0.003712056, 0.003543659, 0.003290238, 0.003092965,
        0.002811146, 0.002811146, 0.002677282, 0.002744282,
        0.002311759, 0.002416161, 0.002155156, 0.002177528,
        0.002064710, 0.002057062, 0.002179416, 0.001942356,
        0.001873533, 0.001981783, 0.001921955, 0.001929434,
        0.001966826, 0.001892041, 0.002244159, 0.001900401,
        0.002153355, 0.002244159, 0.002263617, 0.002441776,
        0.002655001, 0.002379872, 0.002366115, 0.002421141,
        0.002621367, 0.002534252, 0.002431298, 0.002534252,
        0.002455057, 0.002381964, 0.002345034, 0.002243477,
        0.002363499, 0.002428126, 0.002292457, 0.002117078,
        0.002154659, 0.002004334, 0.002079497, 0.001897374,
        0.002216401, 0.001863792, 0.002182820, 0.001847001,
        0.001897374)

# compute initial values
iv <- init_rc(ages=ages, mx=mx,
             pre_working_age=TRUE,
             working_age=TRUE,
             retirement=TRUE,
             post_retirement=TRUE)

Run Interactive Rogers-Castro App

Description

Run an interactive Rogers-Castro app. Use interactive sliders to see how parameters affect the Rogers-Castro age schedules.

Usage

interact_rc()

Value

No return value, called for interactive widget

Examples

## Not run: 
interact_rc()

## End(Not run)

Calculate Rogers-Castro migration age schedule

Description

Given a set of ages and parameters, calculate the migration age schedule based on the Rogers and Castro formula. Choose between a 7, 9, 11 or 13 parameter model.

Usage

mig_calculate_rc(ages, pars)

Arguments

ages

numeric. A vector of ages for migration rates to be calculated.

pars

numeric. A named list of parameters. See below for details.

Details

In the full 13 parameter model, the migration rate at age x, m(x) is defined as

m(x) = a1*exp(-1*alpha1*x) + a2*exp(-1*alpha2*(x - mu2) - exp(-1*lambda2*(x - mu2))) + a3*exp(-1*alpha3*(x - 3) - exp(-1*lambda3*(x - mu3))) + a4*exp(lambda4*x) + c

The first, second, third and fourth pieces of the equation represent pre-working age, working age, retirement and post-retirement age patterns, respectively. Models with less parameters gradually remove terms at the older ages. Parameters in each family are:

For a specific family to be included, values for all parameters in that family must be specified.

Value

A vector the same length as ages. Values represent migration rate for each age in ages.

References

Rogers A, Castro LJ (1981). Model migration schedules. RR-81-030.

Examples

pars <- c(a1= 0.09, alpha1= 0.1, a2= 0.2,
alpha2= 0.1, mu2= 21, lambda2= 0.39, a3= 0.001,
alpha3= 1, mu3= 67, lambda3= 0.6, c= 0.01)
ages <- 0:75
mx <- mig_calculate_rc(ages = ages, pars = pars)

plot(ages, mx, type = 'l')

Estimate Rogers-Castro migration age schedule

Description

Given a set of ages and observed age-specific migrants, estimate the parameters of a Roger-Castro model migration schedule. Choose between a 7, 9, 11 or 13 parameter model.

Usage

mig_estimate_rc(
  ages,
  migrants,
  pop,
  mx,
  sigma,
  pre_working_age,
  working_age,
  retirement,
  post_retirement,
  net_mig,
  ...
)

Arguments

ages

numeric. A vector of integers for ages.

migrants

numeric. A vector of integers for observed age-specific migrants.

pop

numeric. A vector of integers for age-specific population or sample sizes, of which "migrants" experienced a migration event.

mx

numeric. A vector of age-specific migration rates.

sigma

numeric. Standard deviation of migration rates for Normal model. Argument is option, standard deviation is estimated if Normal model is run without being specified.

pre_working_age

logical (TRUE/FALSE). Whether or not to include pre working age component.

working_age

logical (TRUE/FALSE). Whether or not to include working age component.

retirement

logical (TRUE/FALSE). Whether or not to include retirement age component.

post_retirement

logical (TRUE/FALSE). Whether or not to include post retirement age component.

net_mig

numeric. Deprecated argument, use migrants instead.

...

additional inputs to stan, see ?rstan::stan for details.

Value

A list of length 3. The first element, pars_df, is a data frame that provides parameter estimates with 95% credible intervals. The second element, fit_df, is a data frame that shows the data and estimated migration rates at each age. The third element, check_converge, is a data frame that provides the R-hat values and effective sample sizes.

Examples

# Ex 1: Run poisson model using ages, migrants, and population
ages <- 0:80
migrants <- c(202,215,167,188,206,189,164,
            158,197,185,176,173,167,198,
            203,237,249,274,319,345,487,
            491,521,505,529,527,521,529,
            507,484,467,439,399,399,380,
            368,310,324,289,292,270,269,
            285,254,245,265,257,258,263,
            253,346,293,332,346,349,355,
            386,346,344,352,331,320,307,
            320,310,258,254,243,256,263,
            183,169,172,160,166,113,132,
            111,130,110,113)
pop <- c(105505,105505,105505,105505,105505,
        106126,106126,106126,106126,106126,
        100104,100104,100104,100104,100104,
        114880,114880,114880,114880,114880,
        136845,136845,136845,136845,136845,
        136582,136582,136582,136582,136582,
        141935,141935,141935,141935,141935,
        134097,134097,134097,134097,134097,
        130769,130769,130769,130769,130769,
        133718,133718,133718,133718,133718,
        154178,154178,154178,154178,154178,
        145386,145386,145386,145386,145386,
        126270,126270,126270,126270,126270,
        108314,108314,108314,108314,108314,
        79827,79827,79827,79827,79827,59556,
        59556,59556,59556,59556,59556)


# fit the model
res <- mig_estimate_rc(ages = ages, migrants = migrants, pop = pop,
                       pre_working_age = TRUE,
                       working_age = TRUE,
                       retirement = TRUE,
                       post_retirement = FALSE,
                       #optional inputs into stan
                       control = list(adapt_delta = 0.95, max_treedepth = 10),
                       iter = 10, chains = 1 #to speed up example
                       )
# plot the results and data
plot(ages, migrants/pop, ylab = "migration rate", xlab = "age")
lines(ages, res[["fit_df"]]$median, col = "red")
legend("topright", legend=c("data", "fit"), col=c("black", "red"), lty=1, pch = 1)

# Ex 2: Run normal model using ages and mx
ages <- 0:80
mx <- c(0.001914601, 0.002037818, 0.001582863, 0.001781906,
        0.001952514, 0.001780902, 0.001545333, 0.001488796,
        0.001856284, 0.001743211, 0.001758172, 0.001728203,
        0.001668265, 0.001977943, 0.002027891, 0.002063022,
        0.002167479, 0.002385097, 0.002776811, 0.003003134,
        0.003558771, 0.003588001, 0.003807227, 0.003690307,
        0.003865687, 0.003858488, 0.003814558, 0.003873131,
        0.003712056, 0.003543659, 0.003290238, 0.003092965,
        0.002811146, 0.002811146, 0.002677282, 0.002744282,
        0.002311759, 0.002416161, 0.002155156, 0.002177528,
        0.002064710, 0.002057062, 0.002179416, 0.001942356,
        0.001873533, 0.001981783, 0.001921955, 0.001929434,
        0.001966826, 0.001892041, 0.002244159, 0.001900401,
        0.002153355, 0.002244159, 0.002263617, 0.002441776,
        0.002655001, 0.002379872, 0.002366115, 0.002421141,
        0.002621367, 0.002534252, 0.002431298, 0.002534252,
        0.002455057, 0.002381964, 0.002345034, 0.002243477,
        0.002363499, 0.002428126, 0.002292457, 0.002117078,
        0.002154659, 0.002004334, 0.002079497, 0.001897374,
        0.002216401, 0.001863792, 0.002182820, 0.001847001,
        0.001897374)

# fit the model
res <- mig_estimate_rc(ages = ages, mx = mx,
                       pre_working_age = TRUE,
                       working_age = TRUE,
                       retirement = TRUE,
                       post_retirement = FALSE,
                       #optional inputs into stan
                       control = list(adapt_delta = 0.95, max_treedepth = 10),
                       iter = 10, chains = 1 #to speed up example
                       )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.