Simulate isotopic values
This function allows for the simulation of isoscapes. Both partial or complete (i.e. maps) isoscapes can be simulated.
isosim(
data,
mean_model_fix_coef = c(intercept = 64, elev = -0.01, lat_abs = -2.3, lat_2 = 0, long
= 0, long_2 = 0),
disp_model_fix_coef = c(intercept = 5.8, elev = 0, lat_abs = 0, lat_2 = 0, long = 0,
long_2 = 0),
mean_model_matern_coef = c(nu = 0.35, rho = 5e-05, lambda = 899),
disp_model_matern_coef = c(nu = 0.32, rho = 1.5e-05, lambda = 5),
mean_model_uncorr_coef = c(nugget = 0, lambda = 0),
disp_model_uncorr_coef = c(nugget = 0, lambda = 0),
dist_method = "Earth",
seed = NULL,
save_dataframe = FALSE,
verbose = interactive()
)data |
A data.frame containing the covariates needed for the simulation, or alternatively a structural raster of class RasterLayer |
mean_model_fix_coef |
A vector of coefficients for fixed-effects |
disp_model_fix_coef |
A vector of coefficients for fixed-effects |
mean_model_matern_coef |
A vector of coefficients for the spatial random effect |
disp_model_matern_coef |
A vector of coefficients for the spatial random effect |
mean_model_uncorr_coef |
A vector of coefficients for the uncorrelated random effect |
disp_model_uncorr_coef |
A vector of coefficients for the uncorrelated random effect |
dist_method |
A string indicating the distance method |
seed |
An integer used to set the seed of the random generator |
save_dataframe |
A logical indicating whether the detailed data.frame containing the simulated data should be saved |
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. |
This function takes as inputs the values for all covariates matching a series of locations (which can be provided as an structural raster or as a data.frame), as well as the parameters of the isoscape model. The function is not required to fit an isoscape, nor to perform assignments. It is an additional function that can be useful to test the method, and to study the effect of different parameters on isoscapes. We chose default values for parameters inspired to model fits in order to simulate a relatively realistic isoscape. We say 'relatively' because, in the simulations, the Matérn realizations for the mean and the dispersion are drawn independently, which is not the case in nature. Note that extra parameters present in the input lists will not make the function crash but won't be considered during computations either.
This function returns a list of class ISOSCAPE
containing a set of raster and an optional data.frame. The set
contains the raster mean_raster storing the mean isotopic value, and
the raster disp_raster storing the residual dispersion variance.
The optional data.frame contains the simulated data and details of the
computation in an object called data.
The spatial autocorrelation and the Nugget are computed by the
functions RMwhittle and
RMnugget, respectively. These two functions are
part of the powerful package RandomFields.
isofit for the function fitting the isoscape model
IsoriX for the complete work-flow
## 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") > 60) {
## We simulate data under default settings
Simu <- isosim(data = ElevRasterDE,
save_dataframe = TRUE,
seed = 1)
Simu
## We build the plots of the outcome using IsoriX
PlotMeanSimu <- plot(x = Simu, which = "mean")
PlotDispSimu <- plot(x = Simu, which = "disp")
## We fit the simulated data by sampling 50 locations
set.seed(123)
Newdat <- Simu$data[sample(1:nrow(Simu$data), 50), ]
NewdatFit <- isofit(data = Newdat,
mean_model_fix = list(elev = TRUE, lat_abs = TRUE))
Isoscape <- isoscape(ElevRasterDE, NewdatFit)
PlotMeanFitted <- plot(x = Isoscape, which = "mean", sources = list(draw = FALSE))
PlotDispFitted <- plot(x = Isoscape, which = "disp", sources = list(draw = FALSE))
## We compare simulated and fitted data visually
print(PlotMeanSimu, split = c(1, 1, 2, 2), more = TRUE)
print(PlotDispSimu, split = c(2, 1, 2, 2), more = TRUE)
print(PlotMeanFitted, split = c(1, 2, 2, 2), more = TRUE)
print(PlotDispFitted, split = c(2, 2, 2, 2), more = FALSE)
## We compare simulated and fitted data numerically
## Note that differences are expected, as the geographic
## area is small compared to the scale at which
## spatial auto-correlation occurs
round(cbind(
simulated = c(
intercept = 64,
lat_abs = -2.3,
elev = -0.01,
nu = 0.35,
rho = 5e-5,
rho_div_nu = 5e-5/0.35,
lambda_ID = 0,
lambda_matern = 899,
intercept_disp = 5.8,
nu_disp = 3.2e-01,
rho_disp = 1.5e-05,
lambda_matern_source_ID = 0,
lambda_matern_disp = 5),
fitted = c(
intercept = NewdatFit$mean_fit$fixef[1],
lat.abs = NewdatFit$mean_fit$fixef[2],
elev = NewdatFit$mean_fit$fixef[3],
nu = get_ranPars(NewdatFit$mean_fit, which = "corrPars")[[1]]$nu,
rho = get_ranPars(NewdatFit$mean_fit, which = "corrPars")[[1]]$rho,
rho_div_nu = with(get_ranPars(NewdatFit$mean_fit, which = "corrPars")[[1]],rho/nu),
lambda.matern = NewdatFit$mean_fit$lambda,
intercept.disp = NewdatFit$disp_fit$fixef[1],
nu.disp = get_ranPars(NewdatFit$disp_fit, which = "corrPars")[[1]]$nu,
rho.disp = get_ranPars(NewdatFit$disp_fit, which = "corrPars")[[1]]$rho,
lambda.matern.disp = NewdatFit$disp_fit$lambda)), 4)
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