Run cross-validation routines of the abc R package

This function is a convenience wrapper around the functions cv4postpr and anc::cv4abc from the R package abc

Usage

cross_validate(x, nval, tols, method, ...)

Arguments

x

A list of objects of the class demografr_sims_abc (each representing an ABC inference result) for the function abc::cv4postpr, or an object demografr_abc.abc for the function abc::cv4abc

nval, tols, method

Required arguments of abc::cv4postpr

...

Other optional arguments to be passed to abc::cv4postpr

Value

Object of the class cv4postpr and demografr_cv

Examples

#####################################################
# can we even distinguish between competing models?

if (FALSE) { # \dontrun{
# read a list of three different ABC models
models <- lapply(c("X", "Y", "Z"), function(i) { readRDS(url(paste0(
"raw.githubusercontent.com/bodkan/demografr/refs/heads/main/inst/examples/downstream_abc",
i, ".rds"
))) })

# note that each element of the list is, indeed, a demografr ABC result
models[[1]]

 # run cross validation to find out if we have even the power to distinguish
# our competing models(see the abc package vignette for interpretation)
cv_models <- cross_validate(models, nval = 10, tols = c(0.005, 0.01, 0.05), method = "neuralnet")
cv_model

#####################################################
# can our model even estimate the parameters?

# read an example result of an ABC inference
abc_res <- readRDS(system.file("examples/basics_abc.rds", package = "demografr"))

# perform cross-validation
cv_params <- cross_validate(abc_res, nval = 10, tols = c(0.005, 0.01, 0.05),
                            method = "neuralnet")
cv_params
} # }