After experience data has been prepared for analysis, the next step
is to summarize results. The actxps package’s workhorse function for
summarizing termination experience is exp_stats(). This
function returns an exp_df object, which is a type of data
frame containing additional attributes about the experience study.
At a minimum, an exp_df includes:
n_claims)claims)exposure)q_obs)Optionally, an exp_df can also include:
ae_*)credibility)
and credibility-adjusted expected termination rates
(adj_*)To demonstrate this function, we’re going to use a data frame
containing simulated census data for a theoretical deferred annuity
product that has an optional guaranteed income rider. Before
exp_stats() can be used, we must convert our census data
into exposure records using the expose() function1. In
addition, let’s assume we’re interested in studying surrender rates, so
we’ll pass the argument target_status = 'Surrender' to
expose().
library(actxps)
library(dplyr)
exposed_data <- expose(census_dat, end_date = "2019-12-31",
target_status = "Surrender")exp_stats() functionTo use exp_stats(), pass it a data frame of
exposure-level records, ideally of type exposed_df (the
object class returned by the expose() family of
functions).
exp_stats(exposed_data)
#> Experience study results
#>
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 1 × 4
#> n_claims claims exposure q_obs
#> <int> <int> <dbl> <dbl>
#> 1 2869 2869 132634. 0.0216The results show us that we specified no groups, which is why the
output data is a single row. In addition, we can see that we’re looking
at surrender rates through the end of 2019, which
exp_stats() inferred from exposed_data.
The number of claims (n_claims) is equal to the number
of “Surrender” statuses in exposed_data. Since we didn’t
specify any weighting variable, the amount of claims
(claims) equals the number of claims.
(amount <- sum(exposed_data$status == "Surrender"))
#> [1] 2869The total exposure (exposure) is equal to the sum of the
exposures in exposed_data. Had we specified a weighting
variable, this would be equal to the sum of weighted exposures.
(sum_expo <- sum(exposed_data$exposure))
#> [1] 132634Lastly, the observed termination rate (q_obs) equals the
amount of claims divided by the exposures.
amount / sum_expo
#> [1] 0.02163096If the data frame passed into exp_stats() is grouped
using dplyr::group_by(), the resulting output will contain
one record for each unique group.
In the following, exposed_data is grouped by policy year
before being passed to exp_stats(). This results in one row
per policy year found in the data.
exposed_data |>
group_by(pol_yr) |>
exp_stats()
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 15 × 5
#> pol_yr n_claims claims exposure q_obs
#> <int> <int> <int> <dbl> <dbl>
#> 1 1 102 102 19252. 0.00530
#> 2 2 160 160 17715. 0.00903
#> 3 3 124 124 16097. 0.00770
#> 4 4 168 168 14536. 0.0116
#> 5 5 164 164 12916. 0.0127
#> 6 6 152 152 11376. 0.0134
#> 7 7 164 164 9917. 0.0165
#> 8 8 190 190 8448. 0.0225
#> 9 9 181 181 6960. 0.0260
#> 10 10 152 152 5604. 0.0271
#> 11 11 804 804 4390. 0.183
#> 12 12 330 330 2663. 0.124
#> 13 13 99 99 1620. 0.0611
#> 14 14 62 62 872. 0.0711
#> 15 15 17 17 268. 0.0634Multiple grouping variables are allowed. Below, the presence of an
income guarantee (inc_guar) is added as a second grouping
variable.
exposed_data |>
group_by(inc_guar, pol_yr) |>
exp_stats()
#> Experience study results
#>
#> Groups: inc_guar, pol_yr
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 30 × 6
#> inc_guar pol_yr n_claims claims exposure q_obs
#> <lgl> <int> <int> <int> <dbl> <dbl>
#> 1 FALSE 1 56 56 7720. 0.00725
#> 2 FALSE 2 92 92 7103. 0.0130
#> 3 FALSE 3 67 67 6447. 0.0104
#> 4 FALSE 4 123 123 5799. 0.0212
#> 5 FALSE 5 97 97 5106. 0.0190
#> 6 FALSE 6 96 96 4494. 0.0214
#> 7 FALSE 7 92 92 3899. 0.0236
#> 8 FALSE 8 103 103 3287. 0.0313
#> 9 FALSE 9 87 87 2684. 0.0324
#> 10 FALSE 10 60 60 2156. 0.0278
#> # ℹ 20 more rowsThe target_status argument of exp_stats()
specifies which status levels count as claims in the experience study
summary. If the data passed to exp_stats() is an
exposed_df object that already has a specified target
status (via a prior call to expose()), then this argument
is not necessary because the target status is automatically
inferred.
Even if the target status exists on the input data, it can be overridden. However care should be taken to ensure that exposure values in the data are appropriate for the new status.
Using the example data, a total termination rate can be estimated by
including both death and surrender statuses in
target_status. To ensure exposures are accurate, an
adjustment is made to fully expose deaths prior to calling
exp_stats()2.
exposed_data |>
mutate(exposure = ifelse(status == "Death", 1, status)) |>
group_by(pol_yr) |>
exp_stats(target_status = c("Surrender", "Death"))
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender, Death
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 15 × 5
#> pol_yr n_claims claims exposure q_obs
#> <int> <int> <int> <dbl> <dbl>
#> 1 1 290 290 20199 0.0144
#> 2 2 325 325 18754 0.0173
#> 3 3 292 292 17054 0.0171
#> 4 4 329 329 15602 0.0211
#> 5 5 329 329 13946 0.0236
#> 6 6 334 334 12371 0.0270
#> 7 7 297 297 10869 0.0273
#> 8 8 340 340 9510 0.0358
#> 9 9 308 308 7953 0.0387
#> 10 10 260 260 6489 0.0401
#> 11 11 894 894 6505 0.137
#> 12 12 398 398 3753 0.106
#> 13 13 131 131 2135 0.0614
#> 14 14 89 89 1306 0.0681
#> 15 15 23 23 544 0.0423Experience studies often weight output by key policy values. Examples
include account values, cash values, face amount, premiums, and more.
Weighting can be accomplished by passing the name of a weighting column
to the wt argument of exp_stats().
Our sample data contains a column called premium that we
can weight by. When weights are supplied, the claims,
exposure, and q_obs columns will be weighted.
If expected termination rates are supplied (see below), these rates and
A/E values will also be weighted.3
exposed_data |>
group_by(pol_yr) |>
exp_stats(wt = 'premium')
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Weighted by: premium
#>
#> # A tibble: 15 × 8
#> pol_yr n_claims claims exposure q_obs .weight .weight_sq .weight_n
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 102 83223 25312813. 0.00329 26301746 60742993234 19995
#> 2 2 160 170058 23352482. 0.00728 24275265 56232848027 18434
#> 3 3 124 123554 21246774. 0.00582 22201817 51746834383 16806
#> 4 4 168 176751 19270854. 0.00917 20200019 47142441689 15266
#> 5 5 164 173273 17228981. 0.0101 18134795 42887920479 13618
#> 6 6 152 163034 15246521. 0.0107 16192950 38828949428 12067
#> 7 7 164 153238 13328780. 0.0115 14159437 34291451913 10541
#> 8 8 190 174200 11476432. 0.0152 12346124 30121310640 9130
#> 9 9 181 187337 9546258. 0.0196 10420172 25781142118 7591
#> 10 10 152 157603 7707064. 0.0204 8543150 20882643976 6185
#> 11 11 804 856379 6093183. 0.141 6783273 16219955859 4897
#> 12 12 330 383055 3883534. 0.0986 4525027 11191462577 3093
#> 13 13 99 123357 2450266. 0.0503 2891573 7075934189 1937
#> 14 14 62 75534 1339240. 0.0564 1821026 4655661820 1182
#> 15 15 17 19168 401173. 0.0478 783146 1944755748 510As common metric in experience studies is the actual-to-expected, or A/E ratio.
\[ A/E\ ratio = \frac{observed\ value}{expected\ value} \]
If the data passed to exp_stats() has one or more
columns containing expected termination rates, A/E ratios can be
calculated by passing the names of these columns to the
expected argument.
Let’s assume we have two sets of expected rates. The first set is a
vector that varies by policy year. The second set is either 1.5% or 3.0%
depending on whether the policy has a guaranteed income benefit. First,
we need to attach these assumptions to our exposure data. We will use
the names expected_1 and expected_2. Then we
pass these names to the expected argument when we call
exp_stats().
In the output, 4 new columns are created for expected rates and A/E ratios.
expected_table <- c(seq(0.005, 0.03, length.out = 10), 0.2, 0.15, rep(0.05, 3))
# using 2 different expected termination assumption sets
exposed_data2 <- exposed_data |>
mutate(expected_1 = expected_table[pol_yr],
expected_2 = ifelse(exposed_data$inc_guar, 0.015, 0.03))
exp_res <- exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(expected = c("expected_1", "expected_2"))
exp_res |>
select(pol_yr, inc_guar, q_obs, expected_1, expected_2,
ae_expected_1, ae_expected_2)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1, expected_2
#>
#> # A tibble: 30 × 7
#> pol_yr inc_guar q_obs expected_1 expected_2 ae_expected_1 ae_expected_2
#> <int> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 0.00725 0.005 0.03 1.45 0.242
#> 2 1 TRUE 0.00399 0.005 0.015 0.798 0.266
#> 3 2 FALSE 0.0130 0.00778 0.03 1.67 0.432
#> 4 2 TRUE 0.00641 0.00778 0.015 0.824 0.427
#> 5 3 FALSE 0.0104 0.0106 0.03 0.985 0.346
#> 6 3 TRUE 0.00591 0.0106 0.015 0.560 0.394
#> 7 4 FALSE 0.0212 0.0133 0.03 1.59 0.707
#> 8 4 TRUE 0.00515 0.0133 0.015 0.386 0.343
#> 9 5 FALSE 0.0190 0.0161 0.03 1.18 0.633
#> 10 5 TRUE 0.00858 0.0161 0.015 0.532 0.572
#> # ℹ 20 more rowsAs noted above, if weights are passed to exp_stats()
then A/E ratios will also be weighted.
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(expected = c("expected_1", "expected_2"),
wt = "premium") |>
select(pol_yr, inc_guar, q_obs, expected_1, expected_2,
ae_expected_1, ae_expected_2)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1, expected_2
#> Weighted by: premium
#>
#> # A tibble: 30 × 7
#> pol_yr inc_guar q_obs expected_1 expected_2 ae_expected_1 ae_expected_2
#> <int> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 0.00471 0.005 0.03 0.942 0.157
#> 2 1 TRUE 0.00235 0.005 0.015 0.470 0.157
#> 3 2 FALSE 0.0105 0.00778 0.03 1.36 0.351
#> 4 2 TRUE 0.00513 0.00778 0.015 0.660 0.342
#> 5 3 FALSE 0.00737 0.0106 0.03 0.698 0.246
#> 6 3 TRUE 0.00479 0.0106 0.015 0.453 0.319
#> 7 4 FALSE 0.0174 0.0133 0.03 1.30 0.579
#> 8 4 TRUE 0.00377 0.0133 0.015 0.283 0.252
#> 9 5 FALSE 0.0146 0.0161 0.03 0.907 0.487
#> 10 5 TRUE 0.00710 0.0161 0.015 0.441 0.473
#> # ℹ 20 more rowsIf the credibility argument is set to TRUE,
exp_stats() will produce an estimate of partial credibility
under the Limited Fluctuation credibility method (also known as
Classical Credibility) assuming a binomial distribution of claims.4
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(credibility = TRUE) |>
select(pol_yr, inc_guar, claims, q_obs, credibility)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 30 × 5
#> pol_yr inc_guar claims q_obs credibility
#> <int> <lgl> <int> <dbl> <dbl>
#> 1 1 FALSE 56 0.00725 0.192
#> 2 1 TRUE 46 0.00399 0.173
#> 3 2 FALSE 92 0.0130 0.246
#> 4 2 TRUE 68 0.00641 0.211
#> 5 3 FALSE 67 0.0104 0.210
#> 6 3 TRUE 57 0.00591 0.193
#> 7 4 FALSE 123 0.0212 0.286
#> 8 4 TRUE 45 0.00515 0.172
#> 9 5 FALSE 97 0.0190 0.254
#> 10 5 TRUE 67 0.00858 0.210
#> # ℹ 20 more rowsUnder the default arguments, credibility calculations assume a 95%
confidence of being within 5% of the true value. These parameters can be
overridden using the conf_level and cred_r
arguments, respectively.
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(credibility = TRUE, conf_level = 0.98, cred_r = 0.03) |>
select(pol_yr, inc_guar, claims, q_obs, credibility)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 30 × 5
#> pol_yr inc_guar claims q_obs credibility
#> <int> <lgl> <int> <dbl> <dbl>
#> 1 1 FALSE 56 0.00725 0.0969
#> 2 1 TRUE 46 0.00399 0.0876
#> 3 2 FALSE 92 0.0130 0.125
#> 4 2 TRUE 68 0.00641 0.107
#> 5 3 FALSE 67 0.0104 0.106
#> 6 3 TRUE 57 0.00591 0.0976
#> 7 4 FALSE 123 0.0212 0.145
#> 8 4 TRUE 45 0.00515 0.0867
#> 9 5 FALSE 97 0.0190 0.128
#> 10 5 TRUE 67 0.00858 0.106
#> # ℹ 20 more rowsIf expected values are passed to exp_stats() and
credibility is set to TRUE, then the output
will also contain credibility-weighted expected values:
\[ q^{adj} = Z^{cred} \times q^{obs} + (1-Z^{cred}) \times q^{exp} \] where,
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(credibility = TRUE, expected = "expected_1") |>
select(pol_yr, inc_guar, claims, q_obs, credibility, adj_expected_1,
expected_1, ae_expected_1)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1
#>
#> # A tibble: 30 × 8
#> pol_yr inc_guar claims q_obs credibility adj_expected_1 expected_1
#> <int> <lgl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 56 0.00725 0.192 0.00543 0.005
#> 2 1 TRUE 46 0.00399 0.173 0.00482 0.005
#> 3 2 FALSE 92 0.0130 0.246 0.00905 0.00778
#> 4 2 TRUE 68 0.00641 0.211 0.00749 0.00778
#> 5 3 FALSE 67 0.0104 0.210 0.0105 0.0106
#> 6 3 TRUE 57 0.00591 0.193 0.00966 0.0106
#> 7 4 FALSE 123 0.0212 0.286 0.0156 0.0133
#> 8 4 TRUE 45 0.00515 0.172 0.0119 0.0133
#> 9 5 FALSE 97 0.0190 0.254 0.0168 0.0161
#> 10 5 TRUE 67 0.00858 0.210 0.0145 0.0161
#> # ℹ 20 more rows
#> # ℹ 1 more variable: ae_expected_1 <dbl>If conf_int is set to TRUE,
exp_stats() will produce lower and upper confidence
interval limits for the observed termination rate.
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(conf_int = TRUE) |>
select(pol_yr, inc_guar, q_obs, q_obs_lower, q_obs_upper)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 30 × 5
#> pol_yr inc_guar q_obs q_obs_lower q_obs_upper
#> <int> <lgl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 0.00725 0.00544 0.00920
#> 2 1 TRUE 0.00399 0.00286 0.00520
#> 3 2 FALSE 0.0130 0.0104 0.0156
#> 4 2 TRUE 0.00641 0.00490 0.00801
#> 5 3 FALSE 0.0104 0.00807 0.0129
#> 6 3 TRUE 0.00591 0.00446 0.00746
#> 7 4 FALSE 0.0212 0.0176 0.0250
#> 8 4 TRUE 0.00515 0.00366 0.00675
#> 9 5 FALSE 0.0190 0.0153 0.0229
#> 10 5 TRUE 0.00858 0.00666 0.0106
#> # ℹ 20 more rowsIf no weighting variable is passed to wt, confidence
intervals will be constructed assuming a binomial distribution of
claims. However, if a weighting variable is supplied, a normal
distribution for aggregate claims will be assumed with a mean equal to
observed claims and a variance equal to:
\[ Var(S) = E(N) \times Var(X) + E(X)^2 \times Var(N) \]
Where S is the aggregate claim random variable,
X is the weighting variable assumed to follow a normal
distribution, and N is a binomial random variable for the
number of claims.
The default confidence level is 95%. This can be changed using the
conf_level argument. Below, tighter confidence intervals
are constructed by decreasing the confidence level to 90%.
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(conf_int = TRUE, conf_level = 0.9) |>
select(pol_yr, inc_guar, q_obs, q_obs_lower, q_obs_upper)
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#>
#> # A tibble: 30 × 5
#> pol_yr inc_guar q_obs q_obs_lower q_obs_upper
#> <int> <lgl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 0.00725 0.00570 0.00894
#> 2 1 TRUE 0.00399 0.00303 0.00494
#> 3 2 FALSE 0.0130 0.0108 0.0152
#> 4 2 TRUE 0.00641 0.00518 0.00773
#> 5 3 FALSE 0.0104 0.00838 0.0126
#> 6 3 TRUE 0.00591 0.00466 0.00725
#> 7 4 FALSE 0.0212 0.0181 0.0243
#> 8 4 TRUE 0.00515 0.00389 0.00641
#> 9 5 FALSE 0.0190 0.0159 0.0221
#> 10 5 TRUE 0.00858 0.00691 0.0104
#> # ℹ 20 more rowsIf expected values are passed to expected, the output
will also contain confidence intervals around any actual-to-expected
ratios.
exposed_data2 |>
group_by(pol_yr, inc_guar) |>
exp_stats(conf_int = TRUE, expected = "expected_1") |>
select(pol_yr, inc_guar, starts_with("ae_"))
#> Experience study results
#>
#> Groups: pol_yr, inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1
#>
#> # A tibble: 30 × 5
#> pol_yr inc_guar ae_expected_1 ae_expected_1_lower ae_expected_1_upper
#> <int> <lgl> <dbl> <dbl> <dbl>
#> 1 1 FALSE 1.45 1.09 1.84
#> 2 1 TRUE 0.798 0.572 1.04
#> 3 2 FALSE 1.67 1.34 2.01
#> 4 2 TRUE 0.824 0.630 1.03
#> 5 3 FALSE 0.985 0.764 1.22
#> 6 3 TRUE 0.560 0.422 0.707
#> 7 4 FALSE 1.59 1.32 1.88
#> 8 4 TRUE 0.386 0.275 0.506
#> 9 5 FALSE 1.18 0.948 1.42
#> 10 5 TRUE 0.532 0.413 0.660
#> # ℹ 20 more rowsLastly, if credibility is TRUE and
expected values are passed to expected, confidence
intervals will also be calculated for any credibility-weighted
termination rates.
As noted above, the result of exp_stats() is an
exp_df object. If the summary() function is
applied to an exp_df object, the data will be summarized
again and return a higher level exp_df object.
If no additional arguments are passed, summary() returns
a single row of aggregate results.
summary(exp_res)
#> Experience study results
#>
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1, expected_2
#>
#> # A tibble: 1 × 8
#> n_claims claims exposure q_obs expected_1 expected_2 ae_expected_1
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2869 2869 132634. 0.0216 0.0242 0.0209 0.892
#> # ℹ 1 more variable: ae_expected_2 <dbl>If additional variable names are passed to the summary()
function, then the output will group the data by those variables. In our
example, if pol_yr is passed to summary(), the
output will contain one row per policy year.
summary(exp_res, pol_yr)
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1, expected_2
#>
#> # A tibble: 15 × 9
#> pol_yr n_claims claims exposure q_obs expected_1 expected_2 ae_expected_1
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 102 102 19252. 0.00530 0.005 0.0210 1.06
#> 2 2 160 160 17715. 0.00903 0.00778 0.0210 1.16
#> 3 3 124 124 16097. 0.00770 0.0106 0.0210 0.730
#> 4 4 168 168 14536. 0.0116 0.0133 0.0210 0.867
#> 5 5 164 164 12916. 0.0127 0.0161 0.0209 0.788
#> 6 6 152 152 11376. 0.0134 0.0189 0.0209 0.707
#> 7 7 164 164 9917. 0.0165 0.0217 0.0209 0.763
#> 8 8 190 190 8448. 0.0225 0.0244 0.0208 0.920
#> 9 9 181 181 6960. 0.0260 0.0272 0.0208 0.955
#> 10 10 152 152 5604. 0.0271 0.03 0.0208 0.904
#> 11 11 804 804 4390. 0.183 0.2 0.0208 0.916
#> 12 12 330 330 2663. 0.124 0.15 0.0200 0.826
#> 13 13 99 99 1620. 0.0611 0.05 0.0197 1.22
#> 14 14 62 62 872. 0.0711 0.05 0.0195 1.42
#> 15 15 17 17 268. 0.0634 0.05 0.0191 1.27
#> # ℹ 1 more variable: ae_expected_2 <dbl>Similarly, if inc_guar is passed to
summary(), the output will contain a row for each unique
value in inc_guar.
summary(exp_res, inc_guar)
#> Experience study results
#>
#> Groups: inc_guar
#> Target status: Surrender
#> Study range: 1900-01-01 to 2019-12-31
#> Expected values: expected_1, expected_2
#>
#> # A tibble: 2 × 9
#> inc_guar n_claims claims exposure q_obs expected_1 expected_2 ae_expected_1
#> <lgl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 FALSE 1601 1601 52123. 0.0307 0.0235 0.03 1.31
#> 2 TRUE 1268 1268 80511. 0.0157 0.0247 0.015 0.637
#> # ℹ 1 more variable: ae_expected_2 <dbl>As a default, exp_stats() assumes the input data frame
uses the following naming conventions:
exposurestatusThese default names can be overridden using the
col_exposure and col_status arguments.
For example, if the status column was called curr_stat
in our data, we could write:
exposed_data |>
exp_stats(col_status = "curr_stat")exposed_df data frameexp_stats() can still work when given a
non-exposed_df data frame. However, it will be unable to
infer certain attributes like the target status and the study dates. For
target status, all statuses except the first level are assumed to be
terminations. Since this may not be desirable, a warning message will
appear informing what statuses were assumed to be terminated.
not_exposed_df <- data.frame(exposed_data)
exp_stats(not_exposed_df)
#> Warning: ✖ No target status was provided.
#> ℹ Death, Surrender was assumed.
#> Experience study results
#>
#> Target status: Death, Surrender
#> Study range: to
#>
#> # A tibble: 1 × 4
#> n_claims claims exposure q_obs
#> <int> <int> <dbl> <dbl>
#> 1 4639 4639 132634. 0.0350If target_status is provided, no warning message will
appear.
exp_stats(not_exposed_df, target_status = "Surrender")
#> Experience study results
#>
#> Target status: Surrender
#> Study range: to
#>
#> # A tibble: 1 × 4
#> n_claims claims exposure q_obs
#> <int> <int> <dbl> <dbl>
#> 1 2869 2869 132634. 0.0216The exp_stats() function only supports termination
studies. It does not contain support for transaction studies or studies
with multiple changes from an active to an inactive status. For
information on transaction studies, see
vignette("transactions").
See vignette('exposures') for more
information on creating exposure records.↩︎
This adjustment is not necessary on surrenders because
the expose() function previously did this for us.↩︎
When weights are supplied, additional columns are created containing the sum of weights, the sum of squared weights, and the number of records. These columns are used for re-summarizing the data (see the “Summary method” section on this page).↩︎
See Herzog, Thomas (1999). Introduction to Credibility Theory for more information on Limited Fluctuation Credibility.↩︎