run a set of regressions

run_regression(dt, model, nest_model = FALSE, min_n_datapoints = 1,
  model_data = model_data, model_filter_condition = NULL,
  model_name = model_name, model_enough_data = model_enough_data,
  model_fit = model_fit, model_coefs = model_coefs,
  model_summary = model_summary, model_params = model_params,
  in_reg = in_reg, residual = residual)

Arguments

dt

data table

model

the regression model or named list of regression models. If a named list is provided, the name(s) will be stored in the model_name column instead of the formula.

nest_model

whether to nest the model outcome columns (for easier use in multi model systems), default is FALSE

min_n_datapoints

the minimum number of data points required for applying the model(s). Note that there is always an additional check to make sure the minimum number of degrees of freedom for each model is met. If the minimum number of degrees of freedom required is not met, the model will/can not be calculated no matter what min_n_datapoints is set to.

model_data

the nested model data column

model_filter_condition

a filter to apply to the data before running the regression (if only a subset of the data is part of the calibration data) - stored in in_reg, by default no filter

model_name

new column with the model formulae or names if supplied

model_enough_data

new column with information on whether the model has enough data (based on the required degrees of freedom for the model)

model_fit

the new model objects column

model_coefs

the new model coefficients nested data frame column

model_summary

the new model summary nested data frame column

model_params

the nested model information (only relevant if nest_model = TRUE)

in_reg

name of the new logical column in the nested model_data that flags the data included in the regression (i.e. the data that fullfills the model_filter_condition if provided)

residual

name of the new residual column in the nested model_data - residuals are only calculated for rows that are part of the regression (as determined by model_filter_condition)