Convert aggregate termination experience studies to the exp_df class.
Usage
as_exp_df(
x,
expected = NULL,
wt = NULL,
col_claims,
col_exposure,
col_n_claims,
col_weight_sq,
col_weight_n,
target_status = NULL,
start_date = as.Date("1900-01-01"),
end_date = NULL,
credibility = FALSE,
conf_level = 0.95,
cred_r = 0.05,
conf_int = FALSE
)
is_exp_df(x)Arguments
- x
An object. For
as_exp_df(),xmust be a data frame.- expected
A character vector containing column names in x with expected values
- wt
Optional. Length 1 character vector. Name of the column in
xcontaining weights to use in the calculation of claims, exposures, partial credibility, and confidence intervals.- col_claims
Optional. Name of the column in
xcontaining claims. The assumed default is "claims".- col_exposure
Optional. Name of the column in
xcontaining exposures. The assumed default is "exposure".- col_n_claims
Optional and only used used when
wtis passed. Name of the column inxcontaining the number of claims.- col_weight_sq
Optional and only used used when
wtis passed. Name of the column inxcontaining the sum of squared weights.- col_weight_n
Optional and only used used when
wtis passed. Name of the column inxcontaining exposure record counts.- target_status
Character vector of target status values. Default value =
NULL.- start_date
Experience study start date. Default value = 1900-01-01.
- end_date
Experience study end date
- credibility
If
TRUE, future calls tosummary()will include partial credibility weights and credibility-weighted termination rates.- conf_level
Confidence level used for the Limited Fluctuation credibility method and confidence intervals
- cred_r
Error tolerance under the Limited Fluctuation credibility method
- conf_int
If
TRUE, future calls tosummary()will include confidence intervals around the observed termination rates and any actual-to-expected ratios.
Details
is_exp_df() will return TRUE if x is an exp_df object.
as_exp_df() will coerce a data frame to an exp_df object if that
data frame has columns for exposures and claims.
as_exp_df() is most useful for working with aggregate summaries of
experience that were not created by actxps where individual policy
information is not available. After converting the data to the exp_df
class, summary() can be used to summarize data by any grouping variables,
and autoplot() and autotable() are available for reporting.
If nothing is passed to wt, the data frame x must include columns
containing:
Exposures (
exposure)Claim counts (
claims)
If wt is passed, the data must include columns containing:
Weighted exposures (
exposure)Weighted claims (
claims)Claim counts (
n_claims)The raw sum of weights NOT multiplied by exposures
Exposure record counts (
.weight_n)The raw sum of squared weights (
.weight_sq)
The names in parentheses above are expected column names. If the data
frame passed to as_exp_df() uses different column names, these can be
specified using the col_* arguments.
When a column name is passed to wt, the columns .weight, .weight_n,
and .weight_sq are used to calculate credibility and confidence intervals.
If credibility and confidence intervals aren't required, then it is not
necessary to pass anything to wt. The results of as_exp_df() and any
downstream summaries will still be weighted as long as the exposures and
claims are pre-weighted.
target_status, start_date, and end_date are optional arguments that are
only used for printing the resulting exp_df object.
See also
exp_stats() for information on how exp_df objects are typically
created from individual exposure records.
Examples
# convert pre-aggregated experience into an exp_df object
dat <- as_exp_df(agg_sim_dat, col_exposure = "exposure_n",
col_claims = "claims_n",
target_status = "Surrender",
start_date = 2005, end_date = 2019,
conf_int = TRUE)
dat
#> Experience study results
#>
#> Target status: Surrender
#> Study range: 2005 to 2019
#>
#> # A tibble: 180 × 17
#> pol_yr inc_guar qual product exposure claims av exposure_amt claims_amt
#> <int> <lgl> <lgl> <fct> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 FALSE FALSE a 880. 3 1204699 1168118. 1236
#> 2 1 FALSE FALSE b 885. 9 1209902 1154044. 8472
#> 3 1 FALSE FALSE c 1685. 12 2265030 2170944. 9245
#> 4 1 FALSE TRUE a 1073. 11 1433862 1375173. 8851
#> 5 1 FALSE TRUE b 1086. 8 1449080 1396136. 7095
#> 6 1 FALSE TRUE c 2111. 13 2907067 2794199. 12468
#> 7 1 TRUE FALSE a 1299. 9 1796934 1744069. 5272
#> 8 1 TRUE FALSE b 1230. 2 1732269 1662742. 1059
#> 9 1 TRUE FALSE c 2702. 11 3700302 3590893. 9647
#> 10 1 TRUE TRUE a 1601. 5 2234728 2147418. 3354
#> # ℹ 170 more rows
#> # ℹ 8 more variables: av_sq <dbl>, n <int>, wd <dbl>, wd_n <dbl>,
#> # wd_flag <int>, wd_sq <dbl>, av_w_wd <dbl>, n_claims <int>
is_exp_df(dat)
#> [1] TRUE
# summary by policy year
summary(dat, pol_yr)
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender
#> Study range: 2005 to 2019
#>
#> # A tibble: 15 × 7
#> pol_yr n_claims claims exposure q_obs q_obs_lower q_obs_upper
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 102 102 19252. 0.00530 0.00431 0.00634
#> 2 2 160 160 17715. 0.00903 0.00768 0.0104
#> 3 3 124 124 16097. 0.00770 0.00640 0.00907
#> 4 4 168 168 14536. 0.0116 0.00984 0.0133
#> 5 5 164 164 12916. 0.0127 0.0108 0.0146
#> 6 6 152 152 11376. 0.0134 0.0113 0.0155
#> 7 7 164 164 9917. 0.0165 0.0141 0.0191
#> 8 8 190 190 8448. 0.0225 0.0194 0.0257
#> 9 9 181 181 6960. 0.0260 0.0223 0.0297
#> 10 10 152 152 5604. 0.0271 0.0230 0.0314
#> 11 11 804 804 4390. 0.183 0.172 0.195
#> 12 12 330 330 2663. 0.124 0.112 0.137
#> 13 13 99 99 1620. 0.0611 0.0500 0.0728
#> 14 14 62 62 872. 0.0711 0.0551 0.0883
#> 15 15 17 17 268. 0.0634 0.0373 0.0932
# repeat the prior exercise on a weighted basis
dat_wt <- as_exp_df(agg_sim_dat, wt = "av",
col_exposure = "exposure_amt",
col_claims = "claims_amt",
col_n_claims = "claims_n",
col_weight_sq = "av_sq",
col_weight_n = "n",
target_status = "Surrender",
start_date = 2005, end_date = 2019,
conf_int = TRUE)
dat_wt
#> Experience study results
#>
#> Target status: Surrender
#> Study range: 2005 to 2019
#> Weighted by: av
#>
#> # A tibble: 180 × 16
#> pol_yr inc_guar qual product exposure_n n_claims .weight exposure claims
#> <int> <lgl> <lgl> <fct> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 FALSE FALSE a 880. 3 1204699 1168118. 1236
#> 2 1 FALSE FALSE b 885. 9 1209902 1154044. 8472
#> 3 1 FALSE FALSE c 1685. 12 2265030 2170944. 9245
#> 4 1 FALSE TRUE a 1073. 11 1433862 1375173. 8851
#> 5 1 FALSE TRUE b 1086. 8 1449080 1396136. 7095
#> 6 1 FALSE TRUE c 2111. 13 2907067 2794199. 12468
#> 7 1 TRUE FALSE a 1299. 9 1796934 1744069. 5272
#> 8 1 TRUE FALSE b 1230. 2 1732269 1662742. 1059
#> 9 1 TRUE FALSE c 2702. 11 3700302 3590893. 9647
#> 10 1 TRUE TRUE a 1601. 5 2234728 2147418. 3354
#> # ℹ 170 more rows
#> # ℹ 7 more variables: .weight_sq <dbl>, .weight_n <int>, wd <dbl>, wd_n <dbl>,
#> # wd_flag <int>, wd_sq <dbl>, av_w_wd <dbl>
# summary by policy year
summary(dat_wt, pol_yr)
#> Experience study results
#>
#> Groups: pol_yr
#> Target status: Surrender
#> Study range: 2005 to 2019
#> Weighted by: av
#>
#> # A tibble: 15 × 10
#> pol_yr n_claims claims exposure q_obs q_obs_lower q_obs_upper .weight
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 102 83223 25312813. 0.00329 0.00193 0.00465 26301746
#> 2 2 160 175428 24018741. 0.00730 0.00545 0.00916 24966449
#> 3 3 124 132261 22435291. 0.00590 0.00409 0.00770 23442831
#> 4 4 168 192473 20859830. 0.00923 0.00691 0.0115 21861723
#> 5 5 164 197240 19114538. 0.0103 0.00773 0.0129 20104670
#> 6 6 152 192088 17327030. 0.0111 0.00823 0.0139 18388593
#> 7 7 164 186649 15496358. 0.0120 0.00862 0.0155 16440062
#> 8 8 190 220055 13628469. 0.0161 0.0118 0.0205 14647775
#> 9 9 181 232704 11562793. 0.0201 0.0149 0.0253 12603553
#> 10 10 152 209249 9510599. 0.0220 0.0161 0.0279 10531703
#> 11 11 804 1153832 7654553. 0.151 0.134 0.167 8501473
#> 12 12 330 517875 4895865. 0.106 0.0879 0.124 5713464
#> 13 13 99 179358 3134823. 0.0572 0.0413 0.0731 3678776
#> 14 14 62 114390 1727042. 0.0662 0.0423 0.0902 2343501
#> 15 15 17 26265 520946. 0.0504 0.00836 0.0925 1019390
#> # ℹ 2 more variables: .weight_sq <dbl>, .weight_n <int>