This function defines a regression model with parameters, where the model can be one of a set of pre-defined models (see the argument RegressionModel). The parameters can either be defined in a table or read from a resource file.
DefineRegression(
processData,
UseProcessData = FALSE,
DefinitionMethod = c("ResourceFile", "Table"),
GroupingVariables = character(),
RegressionModel = c("SimpleLinear", "Power"),
RegressionTable = data.table::data.table(),
FileName = character()
)The current data produced by a previous instance of the function.
Logical: If TRUE use the existing function output in the process.
Character: A string naming the method to use, one of "Table" to define a table directly (in the GUI), and ResourceFile to read a file.
An optional vector of strings defining variables seving as grouping variables in the RegressionTable. Setting this adds the its elements as columns in the RegressionTable in the GUI.
Character: A string naming the model to use for the regression. See Details for options.
A table with one row defining the name of the dependent variable (column name DependentVariable), the name of the independent variable (column name IndependentVariable), and the Intersect and Slope if RegressionModel = "SimpleLinear" and Factor and Exponent if RegressionModel = "Power".
The path to a CSV file containing the columns DependentVariable), IndependentVariable and the RegressionTable.
An object of StoX data type Regression.
The currently implemented models are listed below:
SimpleLinear $$DependentVariable = Intercept + Slope * IndependentVariable$$
Power $$DependentVariable = Factor * IndependentVariable^{Exponent}$$
EstimateBioticRegression for estimating regression parameters from a StoxBioticData, IndividualsData or SuperIndividualsData object, and ImputeSuperIndividuals for applying the regression to SuperIndividualsData.