Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
actxps:::set_actxps_plot_theme()
## ----packages-----------------------------------------------------------------
library(actxps)
library(clock)
## ----agg-exp-1----------------------------------------------------------------
agg_sim_exp_df <- agg_sim_dat |>
as_exp_df(col_exposure = "exposure_n", col_claims = "claims_n",
conf_int = TRUE,
start_date = 2005, end_date = 2019, target_status = "Surrender")
## ----agg-exp-2----------------------------------------------------------------
summary(agg_sim_exp_df, pol_yr)
## ----agg-exp-3----------------------------------------------------------------
summary(agg_sim_exp_df, inc_guar, product)
## ----pol-dur1-----------------------------------------------------------------
dates <- date_build(2022 + 0:10, 12, 31)
# policy years
pol_yr(dates, "2022-05-10")
# policy quarters
pol_qtr(dates, "2022-05-10")
# policy months
pol_mth(dates, "2022-05-10")
# policy weeks
pol_wk(dates, "2022-05-10")
## ----add-preds, fig.height=4, fig.width=5-------------------------------------
# create exposure records
exposed_data <- expose(census_dat, end_date = "2019-12-31",
target_status = "Surrender") |>
filter(pol_yr <= 10) |>
# add a response column for surrenders
mutate(surrendered = status == "Surrender")
# create a simple logistic model
mod <- glm(surrendered ~ pol_yr, data = exposed_data,
family = "binomial", weights = exposure)
exp_res <- exposed_data |>
# attach predictions
add_predictions(mod, type = "response", col_expected = "logistic") |>
# summarize results
group_by(pol_yr) |>
exp_stats(expected = "logistic")
# create a plot
plot_termination_rates(exp_res)
## ----recipe, warning=FALSE----------------------------------------------------
library(recipes)
recipe(~ ., data = census_dat) |>
step_expose(end_date = "2019-12-31", target_status = "Surrender")
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