continue_with_constrained_far: Compute the FAR from observations using constraints
In thaos/FARallnat: FAR analysis on European Summer Temperature

Description Usage Arguments Value Examples

compute_forall Compute the FAR from the observations using the GCM to constrain the evolution of the statistical distribution of the variable of interest y. The relationship between the variable y and the covariate x in the observational should be in the observation the same as in the GCM.

1	continue_with_constrained_far(merged_res)

merged_res

a result from the compute_forall function. the dataset "obs" should have been treated in the compute_forall function.

return a dataframe with the confidence intervals for the constrained FAR and all the other computed parameters (e.g, p and q) at each time.

# compute the FAR for the CNRM and the IPSL GCMs using a  gam decomposition
# and a gaussian fit with only three bootstrap samples
ans <- compute_forall(compute_far.default, stat_model=gauss_fit, p=0.01, R=3)
ans_cstr <- continue_with_constrained_far(ans) 
summary_plot(ans_cstr)