data: The dataframe containing the data used for fitting the isoscape model
split_by: A string indicating the name of the column of data used to split the dataset. The function isofit will then be called on each of these sub-datasets. The default behaviour is to consider that the dataset should be split per months (split_by = "month").
mean_model_fix: A list of logical indicating which fixed effects to consider in mean_fit
disp_model_fix: A list of logical indicating which fixed effects to consider in disp_fit
mean_model_rand: A list of logical indicating which random effects to consider in mean_fit
disp_model_rand: A list of logical indicating which random effects to consider in disp_fit
uncorr_terms: A list of two strings defining the parametrization used to model the uncorrelated random effects for mean_fit and disp_fit
spaMM_method: A list of two strings defining the spaMM functions used for mean_fit and disp_fit
dist_method: A string indicating the distance method
control_mean: A list of additional arguments to be passed to the call of mean_fit
control_disp: A list of additional arguments to be passed to the call of disp_fit
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.
Returns
This function returns a list of class MULTIISOFIT
containing all pairs of inter-related fits (stored under multi_fits). The returned list also contains the object info_fit that contains all the call arguments.
Details
This function is a wrapper around the function isofit.
Examples
## 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 GNIP monthly data between January and June for Germany GNIPDataDEmonthly <- prepsources( data = GNIPDataDE, month =1:6, split_by ="month") head(GNIPDataDEmonthly)## We fit the isoscapes GermanMonthlyFit <- isomultifit(data = GNIPDataDEmonthly)
GermanMonthlyFit
plot(GermanMonthlyFit)}
See Also
isofit for information about the fitting procedure of each isoscape.