calc.model

Modelling

Description

Creates Models for each climate value

Usage

calc.model(
  method = "monthly",
  timespan,
  climresp,
  classifier = c("rf", "pls", "lm", "glm"),
  seed = NULL,
  p = 0.8,
  folds = "all",
  predrows,
  mnote = NULL,
  k = NULL,
  tc_method = "cv",
  metric = "RMSE",
  doParallel = FALSE,
  autocorrelation = FALSE,
  ...
)

Arguments

timespan numeric. Vector or single input. Should contain all years to be modeled. The years have to be the same format as in the tabular data.
climresp numeric. Vector or single input. Should contain all column’s in the tabular data that contain response variables.
classifier vector or character. Model variants to be used. Supported models: Random Forest = “rf”, Partial-Least-Squares = “pls”, Neural Networks = “nnet”, Linear Regression = “lm” or generalized boosted regression = “gbm”.
seed integer. Seed to reproduce the same model over and over.
folds character. Vector or single input. Either folding over location only “LLO”, over time only “LTO”, or over both “LLTO”. Use “all” to use all possibilitys.
predrows numeric. Vector or single input. Should contain the rows where all the predictor values are stored in.
mnote character. Model note for special modifications used. Default: “normal”
k integer. When ‘fold’ = “LLO” or “LTO”. Set k to the number of unique spatial or temporal units. Leave out to use preset values.
tc_method character. Method for train control function from caret package. Default = “cv”.
metric character. See ‘train’. doParallel logical. Parallelization accelerates the modelling process. Warning: Your PC will slow down drastically. Make sure to not run any other heavy processes during this.
autocorrelation logical. Should autocorrelating data in the predictor variables be excluded from the model run? Only works if ‘autocorr’ has been executed beforehand.

Value

Data Frame (Evaluation Table) Models (Count depends on your settings, saved in “/workflow/models”

See Also

autocorr

Examples

## Not run: 
# Create 48 different models (12 months x 4 classifiers) for every month in 2017
calc.model(method = "monthly",
           timespan = c(2017),
           climresp = 5,
           classifier = c("rf",
                          "pls",
                          "nnet",
                          "lm"),
           seed = 707,
           p = 0.8,
           folds = "LLO",
           mnote = "vignette",
           predrows = c(8:24),
           tc_method = "cv",
           metric = "RMSE",
           autocorrelation = TRUE,
           doParallel = FALSE)

## End(Not run)

Updated: