predict_seatemp {bayfoxr} | R Documentation |
Predict sea-surface temperature given d18O of foram calcite and seawater d18O.
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 |
seasonal_seatemp |
Optional boolean indicating whether to use the seasonal
sea-surface temperature calibrations. Default is |
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).
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)")