Regression modeler {MXM} | R Documentation |
Generic regression modelling function
Description
Generic regression modelling function.
Usage
modeler(target, dataset = NULL, test = "testIndFisher")
Arguments
target |
The target (dependent) variable. It can be a numerical variable, factor, ordinal factor, percentages, or time to event. |
dataset |
The predictor variable(s). It can be a vector, a matrix with continuous only variables. If there are no predictor variables leave this NULL. |
test |
Unlike |
Details
This is a generic regression function designed for continuous predictor variables only. It was useful for me so I decided to epxort it.
Value
A list including:
mod |
The fitted model. |
dev |
The deviance. For some models though ("testIndMMReg", "testIndRQ", "censIndCR", "censIndWR", "testIndTobit", "testIndBeta", "testIndNB", ""testIndQPois", "testIndQBinom") this contains twice the log-likelihood. |
bic |
The BIC of the model. This is NA for the "testIndQPois" and "testIndQBinom" because they are quasi likhelidood models and hence have no BIC. |
res |
The residuals of the fitted model. |
Author(s)
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr
References
Almost the same as in CondIndTests
.
See Also
reg.fit, fbedreg.bic, mmpc.model, ridge.reg
Examples
#simulate a dataset with continuous data
dataset <- matrix(runif(100 * 5, 1, 100), nrow = 100 )
#the target feature is the last column of the dataset as a vector
target <- dataset[, 1]
dataset <- dataset[, -1]
a <- modeler(target, dataset)