predict_seatemp: Predict sea-surface temperature given d18O of foram calcite...
In bayfoxr: Global Bayesian Foraminifera Core Top Calibration

Description Usage Arguments Details Value See Also Examples

Predict sea-surface temperature given d18O of foram calcite and seawater d18O.

1 2	predict_seatemp(d18oc, d18osw, prior_mean, prior_std, foram = NULL, seasonal_seatemp = FALSE, drawsfun = get_draws)

`d18oc`	Numeric or vector of observed foram calcite d18O (‰ VPDB).
`d18osw`	Numeric or vector of observed seawater d18O (‰ VSMOW).
`prior_mean`	Numeric indicating prior mean for sea-surface temperature (°C).
`prior_std`	Numeric indicating prior standard deviation for sea-surface temperature (°C).
`foram`	Optional. String or `NULL`. String indicating the foram species/subspecies to infer for hierarchical models. String must be one of "G. bulloides", "G. ruber", "T. sacculifer", "N. incompta", or "N. pachyderma". `NULL` indicates that a pooled model is desired.
`seasonal_seatemp`	Optional boolean indicating whether to use the seasonal sea-surface temperature calibrations. Default is `FALSE`, i.e. using annual SST calibrations.
`drawsfun`	Optional function used to get get model parameter draws. Must take arguments for "foram" and "seasonal_seatemp" and return a list with members "alpha", "beta", "tau". This is for debugging and testing.

Four calibration models are available: an "annual pooled" model, a "seasonal pooled" model, an "annual hierarchical" model, and a "seasonal hierarchical" model. This function uses magic to determine which "pooled annual" model is used. Which is the simplest case with potential use for Deep Time reconstructions of nonexant foram species. Giving a valid string for foram will use a hierarchical model, which has foram-specific variability in calibration model parameters. Passing TRUE for seasonal_seatemp will use a model trained on season sea-surface temperatures. See reference paper for further details.

A prediction instance for inferred sea-surface temperature (°C).

predict_d18oc

data(bassriver)

# Using the "pooled annual" calibration model:
sst <- predict_seatemp(bassriver$d18o, d18osw=0.0, 
                       prior_mean=30.0, prior_std=20.0)
head(quantile(sst))  # Show only the top few values

predictplot(x=bassriver$depth, y=sst, ylim=c(20, 40), 
            ylab="SST (°C)", xlab="Depth (m)")