fit_model: Linear model fitting on a dataframe

In sitscholl/rebecka_package: Utility functions used in the REBECKA project

Description

This function allows you to fit a (multiple) linear model between dependend and independend variables and perform cross validation. DEPRECATED USE FIT_MODEL2 INSTEAD!!!!

Usage

fit_model(
  df,
  frml,
  family = "lm",
  subset = "full",
  preProc = "",
  verbose = F,
  force_in = "",
  gam_grid = NULL,
  cubist_grid = NULL,
  rf_grid = NULL,
  svmRadial_grid = NULL,
  ...
)

Arguments

df	data.frame, A data.frame that is used to fit a linear model between dependent and independent variables.
frml	formula, A formula object for building the linear model
family	string, 'lm' or 'gam' for linear model or generalized additive model, respectively.
verbose	logical, if True the R2 of every model fit will be printed.
...	further arguments passed on to caret::trainControl (method, number, repeats). Defaults correspond to repeatedcv using 5 folds repeated for 3 times. method = the resampling method: "boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "LGOCV" (for repeated training/test splits), "none" (only fits one model to the entire training set), "oob" (only for random forest, bagged trees, bagged earth, bagged flexible discriminant analysis, or conditional tree forest models), timeslice, "adaptive_cv", "adaptive_boot" or "adaptive_LGOCV"
plots	Should scatterplots of the form plot(frml, data = df) be added to the output?

Examples

fit_model(df = df.interp,
  frml = huglin ~ elevation,
  output = 'full',
  plots = T)

sitscholl/rebecka_package documentation built on Aug. 25, 2020, 4:20 a.m.