Fit the calibration model
This function fits a model that establishes the relationship between the isotopic values of organisms (e.g. tissues such as hair, horn, ivory or feathers) and the isotopic values of their environment (e.g. precipitation).
calibfit(data, isofit, verbose = interactive(), control_optim = list())
data |
A dataframe containing the calibration data (see note below) |
isofit |
The fitted isoscape created by |
verbose |
A logical indicating whether information about the progress of the procedure should be displayed or not while the function is running. By default verbose is TRUE if users use an interactive R session and FALSE otherwise. |
control_optim |
A list to pass information to the argument control of the optim call (for advanced users only). |
The calibration model is a linear mixed-effects model (LMM) that fits the isotopic values of sedentary organisms as a linear function of the isotopic values in their environment (e.g. precipitation).
This function considers that the isotopic values from the environment (e.g.
from precipitation) at the locations at which organisms were sampled are not
known. The function therefore predicts these isotopic values from the
geostatistical model fitted by the function isofit, which is
provided to calibfit using the argument isofit.
The LMM used to fit the calibration function has a simple fixed-effect structure: an intercept and a slope. The random effect is more complex: it is normally distributed with mean zero, a certain variance between locations proportional to the squared fixed slope, and a covariance structure defined by the prediction covariance matrix of the isoscape model between the calibration locations. See appendix in Courtiol et al. 2019 for more details.
This function is only needed in the case for which the assignment of organisms has to be performed within an isoscape that was built using another source of isotopic values (e.g., precipitation). This implies that if the isoscape had been fitted using isotopic ratios from sedentary animals, then this calibration step is not needed.
If source isotopic values are known at the locations where sedentary
organisms were sampled, users should calibrate their data directly using the
function lm by fitting tissue isotopic values as a
function of source isotopic values.
This function returns a list of class CALIBFIT containing the fixed-effect estimates of the calibration function, the covariance of the fixed effects, the residual variance of the calibration fit, the fitted calibration model, the original calibration data set with additional information added during the fit, and the location of the calibration points as spatial points.
See CalibDataAlien to know which variables are needed to
perform the calibration fit and their names.
Courtiol A, Rousset F, Rohwäder M, Soto DX, Lehnert L, Voigt CC, Hobson KA, Wassenaar LI, Kramer-Schadt S (2019). Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. In Hobson KA, Wassenaar LI (eds.), Tracking Animal Migration with Stable Isotopes, second edition. Academic Press, London.
## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. options_IsoriX(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)
if(getOption_IsoriX("example_maxtime") > 30) {
## We prepare the data:
GNIPDataDEagg <- prepsources(data = GNIPDataDE)
## We fit the models for Germany:
GermanFit <- isofit(data = GNIPDataDEagg,
mean_model_fix = list(elev = TRUE, lat_abs = TRUE))
## We fit the calibration model:
CalibAlien <- calibfit(data = CalibDataAlien, isofit = GermanFit)
## We display minimal information:
CalibAlien
## We display more information:
summary(CalibAlien)
## We plot the calibration function:
plot(CalibAlien)
## Add other calibrations on the same plot
CalibDataAlien3 <- CalibDataAlien2 <- CalibDataAlien
CalibDataAlien2$sample_value <- rnorm(nrow(CalibDataAlien2),
mean = CalibDataAlien2$sample_value, sd = 10)
CalibDataAlien3$sample_value <- rnorm(nrow(CalibDataAlien3),
mean = CalibDataAlien3$sample_value, sd = 10)
CalibAlien2 <- calibfit(data = CalibDataAlien2, isofit = GermanFit)
CalibAlien3 <- calibfit(data = CalibDataAlien3, isofit = GermanFit)
plot(CalibAlien3)
points(CalibAlien)
points(CalibAlien2, pch = 3, col = "green", CI = list(col = "green"))
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