DefineRegression | R Documentation |
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()
)
processData |
The current data produced by a previous instance of the function. |
UseProcessData |
Logical: If TRUE use the existing function output in the process. |
DefinitionMethod |
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. |
GroupingVariables |
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. |
RegressionModel |
Character: A string naming the model to use for the regression. See Details for options. |
RegressionTable |
A table with one row defining the name of the dependent variable (column name |
FileName |
The path to a CSV file containing the columns |
The currently implemented models are listed below:
SimpleLinear
DependentVariable = Intercept + Slope * IndependentVariable
Power
DependentVariable = Factor * IndependentVariable^{Exponent}
An object of StoX data type Regression
.
EstimateBioticRegression
for estimating regression parameters from a StoxBioticData
, IndividualsData
or SuperIndividualsData
object, and ImputeSuperIndividuals
for applying the regression to SuperIndividualsData
.
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