Evaluate model performance

Calculate the model performance metrics using leave-group-out (or Monte Carlo) cross-validation.

Usage

evaluate(object, ...)

# S4 method for ProgressModel
evaluate(object, ...)

# S4 method for ProgressModelList
evaluate(object, ...)

Arguments

object

an object of class ProgressModel or ProgressModelList.

...

extra arguments.

Value

An object identical to object, with the metrics slot(s) altered.

Examples

if (FALSE) {
# Create a Region object
library(cronus)
region <- Region(name = "nebraska", type = "us state",
                 div = c(country = "United States", state = "Nebraska"))

# Create a model
object1 <- new("ProgressBM",
               region = region,
               crop = "Corn",
               data = data_progress$Corn,
               formula = "CumPercentage ~ Time + agdd") # ProgressModel

# Create another model
object2 <- new("ProgressCLM",
               region = region,
               crop = "Soybeans",
               data = data_progress$Soybeans,
               formula = "Stage ~ Time + agdd + adayl") # ProgressModel

# Concatenate the models
object <- c(object1, object2) # ProgressModelList

# Fit
object <- fit(object)

# Plot
plot(object, cumulative = TRUE, seasons = 2002)

# Predict
predict(object, data_progress)

# Evaluate
object <- evaluate(object, maxsam = 100, seed = 1)
plot_metrics(object)

# Summarize
summary(object)

# Report
report(object, name = "example_report", path = getwd())
}