hamstr: Fit a hamstr Age-Depth Model

In andrewdolman/baconr: Hierarchical Accumulation Modelling with Stan and R

Fit a hamstr Age-Depth Model

Description

hamstr is used to fit an age-depth model to a set of age-control points. Ages should already be on the desired scale, e.g. calendar ages, and will not be calibrated. The function calibrate_14C_age can be used to calibrate radiocarbon dates prior to fitting a hamstr model.

Usage

hamstr(
  depth,
  obs_age,
  obs_err,
  min_age = 1950 - as.numeric(format(Sys.Date(), "%Y")),
  K_fine = NULL,
  K_factor = NULL,
  K,
  top_depth = NULL,
  bottom_depth = NULL,
  acc_mean_prior = NULL,
  acc_shape = 4,
  mem_mean = 0.5,
  mem_strength = 10,
  model_bioturbation = FALSE,
  n_ind = NULL,
  L_prior_mean = 7.5,
  L_prior_shape = 2.5,
  L_prior_sigma = NULL,
  model_displacement = FALSE,
  D_prior_mean = 2,
  D_prior_shape = 1.5,
  D_prior_sigma = NULL,
  model_hiatus = FALSE,
  H_top = NULL,
  H_bottom = NULL,
  H_max = NULL,
  sample_posterior = TRUE,
  hamstr_control = list(),
  stan_sampler_args = list()
)

Arguments

depth	depths of observed ages (age control points)
obs_age	observed age at each depth (age control points)
obs_err	error associated with each observed age (1 standard error)
min_age	the minimum age that the first modelled depth can be. Useful if extrapolating above the shallowest age control point to e.g. the surface. So set min_age to the year the core was collected. E.g. for a core collected in 1990, with ages in years BP this would be -40 (present = 1950 by convention). The default value is the current year in calendar age BP (e.g. -71 for 2021).
K_fine	the number of sections at the highest resolution of the model.
K_factor	the rate at which the thickness of the sections grows between subsequent levels.
K	deprecated, use K_fine and K_factor instead.
top_depth, bottom_depth	the top and bottom depths of the desired age-depth model. Must encompass the range of the data. Defaults to the shallowest and deepest data points.
acc_mean_prior	hyperparameter for the prior on the overall mean accumulation rate for the record. Units are obs_age / depth. E.g. if depth is in cm and age in years then the accumulation rate is in years/cm. The overall mean accumulation rate is given a weak half-normal prior with mean = 0, SD = 10 * acc_mean_prior. If left blank, acc_mean_prior is set to the mean accumulation rate estimated by fitting a robust linear model using rlm.
acc_shape	hyperparameter for the shape of the priors on accumulation rates. Defaults to 4.
mem_mean	hyperparameter; a parameter of the Beta prior distribution on "memory", i.e. the autocorrelation parameter in the underlying AR1 model. The prior on the correlation between layers is scaled according to the thickness of the sediment sections in the highest resolution hierarchical layer, delta_c, which is determined by the total length age-models and the parameter vector K. mem_mean sets the mean value for R (defaults to 0.5), while w = R^(delta_c)
mem_strength	hyperparameter: sets the strength of the memory prior, defaults to 10 as in Bacon >= 2.5.1
model_bioturbation	defaults to FALSE. If TRUE, additional uncertainty in the observed ages due to sediment mixing (bioturbation) is modelled via a latent variable process. The amount of additional uncertainty is a function of the mixing depth L, the sedimentation rate, and the number of particles (e.g. individual foraminifera) per measured date. See description for details.
n_ind	the number of individual particles (e.g. foraminifera) in each sample that was dated by e.g. radiocarbon dating. This can be a single value or a vector the same length as obs_age. Only used if model_bioturbation = TRUE.
L_prior_mean	mean of the gamma prior on mixing depth, defaults to 7.5.
L_prior_shape, L_prior_sigma	shape and standard deviation of the gamma prior on the mixing depth. Set only one of these, the other will be calculated. Defaults to shape = 2.5. If either the shape or sigma parameter is set to zero, the mixing depth is fixed at the value of L_prior_mean, rather than being sampled.
model_displacement	model additional error on observed ages that does not scale with the number of individual particles in a sample, for example due to incomplete mixing.
D_prior_mean	mean of the gamma prior on additional error on observed ages. Units are those of the depth variable, e.g. cm.
D_prior_shape, D_prior_sigma	shape and standard deviation of the gamma prior on the additional error on observed ages. Set only one of these, the other will be calculated. Defaults to shape = 1.5. If either the shape or sigma parameter is set to zero, the additional error if fixed at the value of D_prior_mean, rather than being sampled.
model_hiatus	optionally model a hiatus.
H_top, H_bottom	limits to the location of a hiatus. By default these are set to the top and bottom data points but can be set by the user
H_max	maximum length of the hiatus in age units
sample_posterior	if set to FALSE, hamstr skips sampling the model and returns only the data, model structure and prior parameters so that data and prior distributions can be plotted and checked prior to running a model. Defaults to TRUE
hamstr_control	additional arguments to hamstr useful for debugging or development. See hamstr_control for details.
stan_sampler_args	additional arguments to sampling passed as a named list. e.g. list(chains = 8, iter = 4000) to run 8 MCMC chains of 4000 iterations instead of the default 4 chains of 2000 iterations. See get_stan_sampler_args for details.

Value

Returns an object of class "hamstr_fit", which is a list composed of the output from the stan sampler .$fit, and the list of data passed to the sampler, .$data

Examples

## Not run: 

fit <- hamstr(
  depth = MSB2K$depth,
  obs_age = MSB2K$age,
  obs_err = MSB2K$error)

plot(fit)


## End(Not run)

andrewdolman/baconr documentation built on Jan. 24, 2025, 10:22 a.m.