readme.md
In EarthSystemDiagnostics/hamstrbacon: Interface Between the rbacon and hamstr packages
hamstrbacon: An Interface Between rbacon and hamstr.
Andrew M. Dolman
2025-05-02
Installation
hamstrbacon can be installed directly from Github
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("earthsystemdiagnostics/hamstrbacon")
Fitting age-models with rbacon via hamstrbacon
Using the example core “MSB2K” from the
rbacon
package.
library(hamstrbacon)
hambac_1 <- hamstr_bacon(id = "tst",
depth = MSB2K$depth,
obs_age = MSB2K$age,
obs_err = MSB2K$error,
thick = 5, cc = 1,
# pass seed to rbacon so that mcmc sample is the same
# each time for the example
seed = 20220103
)
#> The run's files will be put in this folder: /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL
#> Warning in file(con, "r"): file("") only supports open = "w+" and open = "w+b":
#> using the former
#> Reading /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon
#> Constant calibration curve.
#> IntCal20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_intcal20.14C
#> Marine20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_marine20.14C
#> SHCal20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_shcal20.14C
#> Added det: tst: 4128.0+-65.0 d=1.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4106.0+-60.0 d=4.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4046.0+-59.0 d=8.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4184.0+-58.0 d=12.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4076.0+-62.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4107.0+-61.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4097.0+-58.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4177.0+-53.0 d=17.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4220.0+-59.0 d=20.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4281.0+-64.0 d=21.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4374.0+-64.0 d=21.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4493.0+-62.0 d=22.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4452.0+-52.0 d=28.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4616.0+-64.0 d=31.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4662.0+-64.0 d=32.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4743.0+-67.0 d=33.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4638.0+-67.0 d=34.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4810.0+-67.0 d=37.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4757.0+-82.0 d=38.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4839.0+-59.0 d=41.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4913.0+-65.0 d=43.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4880.0+-57.0 d=46.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4989.0+-70.0 d=47.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5070.0+-66.0 d=48.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4993.0+-67.0 d=49.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5115.0+-79.0 d=50.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5026.0+-51.0 d=52.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5242.0+-64.0 d=53.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5159.0+-50.0 d=54.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5130.0+-66.0 d=55.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5238.0+-65.0 d=58.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5293.0+-38.0 d=59.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5293.0+-54.0 d=64.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5368.0+-51.0 d=70.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5498.0+-69.0 d=71.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5588.0+-55.0 d=73.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5514.0+-57.0 d=75.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5535.0+-52.0 d=77.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5644.0+-77.0 d=79.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5885.0+-45.0 d=99.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Since no file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon.init was provided with initial values for the twalk, I will be using simulated values.
#> BaconFixed: Bacon jumps model with fixed c's.
#> K=21, H=0, dim=23, Seed=20220103, Dc=5.000000, c(0)=1.500000, c(K)=106.500000
#>
#> twalk: 2875000 iterations to run, Fri May 2 14:03:38 2025
#>
#> BUFSIZ is 1024, optimal block size changed to 4096
#> twalk: All stored iterations to be saved in file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.out
#> twalk: Finished, 0.7% of moved pars per iteration (ratio 20012.869565/2875000). Output in file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.out,
#> Fri May 2 14:03:42 2025
#>
#> Writing two last points of the twalk in /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon.last.
#> bacon: burn in (initial iterations which will be removed): 1725000
#> Eso es to...eso es to...eso es to...eso es toooodo amigos!
#> Previous runs of core RtmpgO5rDL with thick=5 cm deleted. Now try running the core again
Plot the Bacon model with hamstr plotting functions
plot(hambac_1)
Extract summary downcore age-depth model
summary(hambac_1)
#> # A tibble: 22 × 10
#> depth mean sd `2.5%` `15.9%` `25%` `50%` `75%` `84.1%` `97.5%`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.5 4565. 70.3 4433. 4495. 4526. 4563. 4607. 4630. 4710
#> 2 6.5 4630. 69.7 4485. 4560. 4581. 4632. 4679. 4701. 4762.
#> 3 11.5 4697. 64.4 4565. 4623. 4649. 4709. 4744. 4759. 4802.
#> 4 16.5 4765. 55.6 4643. 4700. 4723. 4784. 4806. 4816. 4840.
#> 5 21.5 4864. 48.8 4768. 4828. 4839. 4856. 4885. 4913. 4971.
#> 6 26.5 5082. 106. 4884. 4963. 5002. 5087. 5164. 5199. 5259.
#> 7 31.5 5330. 64.9 5208. 5272. 5289. 5321. 5377. 5405. 5449.
#> 8 36.5 5479. 46.1 5376. 5437. 5452. 5479. 5508. 5525. 5566.
#> 9 41.5 5579. 43.5 5489. 5535. 5554. 5582. 5603. 5622. 5660.
#> 10 46.5 5677. 52.9 5586. 5631. 5641. 5674. 5706. 5719. 5828.
#> # ℹ 12 more rows
Get the individual model realisations
predict(hambac_1)
#> # A tibble: 99,022 × 3
#> iter depth age
#> <dbl> <dbl> <dbl>
#> 1 501 1.5 4691.
#> 2 501 6.5 4730.
#> 3 501 11.5 4742.
#> 4 501 16.5 4802.
#> 5 501 21.5 4851.
#> 6 501 26.5 4986.
#> 7 501 31.5 5299.
#> 8 501 36.5 5535.
#> 9 501 41.5 5636.
#> 10 501 46.5 5714.
#> # ℹ 99,012 more rows
Plot the accumulation rates
plot(hambac_1, type = "acc")
#> Joining with `by = join_by(depth)`
Smooth the accumulation rates with 10 cm filter before calculating the
statistics
plot(hambac_1, type = "acc", tau = 10)
#> Joining with `by = join_by(depth)`
Get the ages interpolated at specific depths
spec_depths <- predict(hambac_1, depth = c(10:15))
spec_depths
#> # A tibble: 27,006 × 3
#> iter depth age
#> <int> <int> <dbl>
#> 1 1 10 4738.
#> 2 1 11 4741.
#> 3 1 12 4748.
#> 4 1 13 4760.
#> 5 1 14 4772.
#> 6 1 15 4784.
#> 7 2 10 4738.
#> 8 2 11 4740.
#> 9 2 12 4747.
#> 10 2 13 4759.
#> # ℹ 26,996 more rows
Summary also works after interpolation
summary(spec_depths)
#> # A tibble: 6 × 10
#> depth mean sd `2.5%` `15.9%` `25%` `50%` `75%` `84.1%` `97.5%`
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10 4676. 62.5 4552. 4607. 4628. 4688. 4722. 4736. 4785.
#> 2 11 4690. 63.4 4561. 4619. 4642. 4702. 4737. 4751. 4796.
#> 3 12 4703. 62.1 4577. 4632. 4656. 4717. 4749. 4763. 4804.
#> 4 13 4717. 58.2 4596. 4651. 4669. 4734. 4760. 4771. 4808.
#> 5 14 4731. 55.5 4610. 4669. 4683. 4752. 4772. 4780. 4813.
#> 6 15 4744. 54.4 4626. 4683. 4699. 4767. 4785. 4793. 4818.
References
- Blaauw, Maarten, and J. Andrés Christen. 2011. Flexible Paleoclimate
Age-Depth Models Using an Autoregressive Gamma Process. Bayesian
Analysis 6 (3): 457-74. .
EarthSystemDiagnostics/hamstrbacon documentation built on June 2, 2025, 5:04 a.m.
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hamstrbacon: An Interface Between rbacon and hamstr.
Andrew M. Dolman 2025-05-02
Installation
hamstrbacon can be installed directly from Github
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("earthsystemdiagnostics/hamstrbacon")
Fitting age-models with rbacon via hamstrbacon
Using the example core “MSB2K” from the rbacon package.
library(hamstrbacon)
hambac_1 <- hamstr_bacon(id = "tst",
depth = MSB2K$depth,
obs_age = MSB2K$age,
obs_err = MSB2K$error,
thick = 5, cc = 1,
# pass seed to rbacon so that mcmc sample is the same
# each time for the example
seed = 20220103
)
#> The run's files will be put in this folder: /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL
#> Warning in file(con, "r"): file("") only supports open = "w+" and open = "w+b":
#> using the former
#> Reading /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon
#> Constant calibration curve.
#> IntCal20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_intcal20.14C
#> Marine20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_marine20.14C
#> SHCal20: Reading from file: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/rintcal/extdata/3Col_shcal20.14C
#> Added det: tst: 4128.0+-65.0 d=1.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4106.0+-60.0 d=4.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4046.0+-59.0 d=8.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4184.0+-58.0 d=12.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4076.0+-62.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4107.0+-61.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4097.0+-58.0 d=14.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4177.0+-53.0 d=17.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4220.0+-59.0 d=20.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4281.0+-64.0 d=21.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4374.0+-64.0 d=21.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4493.0+-62.0 d=22.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4452.0+-52.0 d=28.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4616.0+-64.0 d=31.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4662.0+-64.0 d=32.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4743.0+-67.0 d=33.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4638.0+-67.0 d=34.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4810.0+-67.0 d=37.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4757.0+-82.0 d=38.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4839.0+-59.0 d=41.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4913.0+-65.0 d=43.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4880.0+-57.0 d=46.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4989.0+-70.0 d=47.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5070.0+-66.0 d=48.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 4993.0+-67.0 d=49.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5115.0+-79.0 d=50.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5026.0+-51.0 d=52.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5242.0+-64.0 d=53.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5159.0+-50.0 d=54.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5130.0+-66.0 d=55.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5238.0+-65.0 d=58.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5293.0+-38.0 d=59.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5293.0+-54.0 d=64.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5368.0+-51.0 d=70.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5498.0+-69.0 d=71.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5588.0+-55.0 d=73.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5514.0+-57.0 d=75.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5535.0+-52.0 d=77.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5644.0+-77.0 d=79.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Added det: tst: 5885.0+-45.0 d=99.5 ResCorr= 0.0+-0.0 a=3 b=4 cc=IntCal20
#> Since no file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon.init was provided with initial values for the twalk, I will be using simulated values.
#> BaconFixed: Bacon jumps model with fixed c's.
#> K=21, H=0, dim=23, Seed=20220103, Dc=5.000000, c(0)=1.500000, c(K)=106.500000
#>
#> twalk: 2875000 iterations to run, Fri May 2 14:03:38 2025
#>
#> BUFSIZ is 1024, optimal block size changed to 4096
#> twalk: All stored iterations to be saved in file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.out
#> twalk: Finished, 0.7% of moved pars per iteration (ratio 20012.869565/2875000). Output in file /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.out,
#> Fri May 2 14:03:42 2025
#>
#> Writing two last points of the twalk in /private/var/folders/w7/h2mftm493q51t0v8grt97cbnhy0_jv/T/RtmpgO5rDL/RtmpgO5rDL_21.bacon.last.
#> bacon: burn in (initial iterations which will be removed): 1725000
#> Eso es to...eso es to...eso es to...eso es toooodo amigos!
#> Previous runs of core RtmpgO5rDL with thick=5 cm deleted. Now try running the core again
Plot the Bacon model with hamstr plotting functions
plot(hambac_1)
Extract summary downcore age-depth model
summary(hambac_1)
#> # A tibble: 22 × 10
#> depth mean sd `2.5%` `15.9%` `25%` `50%` `75%` `84.1%` `97.5%`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.5 4565. 70.3 4433. 4495. 4526. 4563. 4607. 4630. 4710
#> 2 6.5 4630. 69.7 4485. 4560. 4581. 4632. 4679. 4701. 4762.
#> 3 11.5 4697. 64.4 4565. 4623. 4649. 4709. 4744. 4759. 4802.
#> 4 16.5 4765. 55.6 4643. 4700. 4723. 4784. 4806. 4816. 4840.
#> 5 21.5 4864. 48.8 4768. 4828. 4839. 4856. 4885. 4913. 4971.
#> 6 26.5 5082. 106. 4884. 4963. 5002. 5087. 5164. 5199. 5259.
#> 7 31.5 5330. 64.9 5208. 5272. 5289. 5321. 5377. 5405. 5449.
#> 8 36.5 5479. 46.1 5376. 5437. 5452. 5479. 5508. 5525. 5566.
#> 9 41.5 5579. 43.5 5489. 5535. 5554. 5582. 5603. 5622. 5660.
#> 10 46.5 5677. 52.9 5586. 5631. 5641. 5674. 5706. 5719. 5828.
#> # ℹ 12 more rows
Get the individual model realisations
predict(hambac_1)
#> # A tibble: 99,022 × 3
#> iter depth age
#> <dbl> <dbl> <dbl>
#> 1 501 1.5 4691.
#> 2 501 6.5 4730.
#> 3 501 11.5 4742.
#> 4 501 16.5 4802.
#> 5 501 21.5 4851.
#> 6 501 26.5 4986.
#> 7 501 31.5 5299.
#> 8 501 36.5 5535.
#> 9 501 41.5 5636.
#> 10 501 46.5 5714.
#> # ℹ 99,012 more rows
Plot the accumulation rates
plot(hambac_1, type = "acc")
#> Joining with `by = join_by(depth)`
Smooth the accumulation rates with 10 cm filter before calculating the statistics
plot(hambac_1, type = "acc", tau = 10)
#> Joining with `by = join_by(depth)`
Get the ages interpolated at specific depths
spec_depths <- predict(hambac_1, depth = c(10:15))
spec_depths
#> # A tibble: 27,006 × 3
#> iter depth age
#> <int> <int> <dbl>
#> 1 1 10 4738.
#> 2 1 11 4741.
#> 3 1 12 4748.
#> 4 1 13 4760.
#> 5 1 14 4772.
#> 6 1 15 4784.
#> 7 2 10 4738.
#> 8 2 11 4740.
#> 9 2 12 4747.
#> 10 2 13 4759.
#> # ℹ 26,996 more rows
Summary also works after interpolation
summary(spec_depths)
#> # A tibble: 6 × 10
#> depth mean sd `2.5%` `15.9%` `25%` `50%` `75%` `84.1%` `97.5%`
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10 4676. 62.5 4552. 4607. 4628. 4688. 4722. 4736. 4785.
#> 2 11 4690. 63.4 4561. 4619. 4642. 4702. 4737. 4751. 4796.
#> 3 12 4703. 62.1 4577. 4632. 4656. 4717. 4749. 4763. 4804.
#> 4 13 4717. 58.2 4596. 4651. 4669. 4734. 4760. 4771. 4808.
#> 5 14 4731. 55.5 4610. 4669. 4683. 4752. 4772. 4780. 4813.
#> 6 15 4744. 54.4 4626. 4683. 4699. 4767. 4785. 4793. 4818.
References
- Blaauw, Maarten, and J. Andrés Christen. 2011. Flexible Paleoclimate Age-Depth Models Using an Autoregressive Gamma Process. Bayesian Analysis 6 (3): 457-74. .
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