Mallow's Cp is one of the very useful metrics and selection criteria for machine learning algorithms (models). It is used to estimate the closest number to the number of predictors and the intercept (approximate number of explanatory variables) of linear and non-linear based models. The function inherits residuals from the estimated model. The uniqueness of this function compared to other procedures for computing Mallow's Cp is that it does not require nested models for computation and it is not limited to lm based models only.
Arguments
- Model
The estimated model from which the Mallows Cp would be computed
- y
The vector of the LHS variable of the estimated model
- x
The matrix of the RHS variable of the estimated model. Note that if the model adds additional factor variables into the output, then the number of additional factors
Nlevelsis required otherwise the computed Cp would be biased.- type
The type of model (
LM,ALM,GLM,N-LM,nls,ARDL,SMOOTH,SPLINE,ARIMA,plm) for which Cp would be computed broadly divided in to linear (LM,ALM,GLM,ARDL,SMOOTH,SPLINE,ARIMA,plm) and non-linear (GLM,N-LM,nls). The type of model must be specified as indicated. Supported models areLM,ALM,GLM(for binary based models),N-LM(not linear for models not clearly defined as linear or non-linear especially some of the essemble models that are merely computed not estimated) ornlsfor other non linear models,ARDL,SMOOTHfor smooth.spline,SPLINEfor bs spline models,ARIMAandplm.- Nlevels
Optional number of additional variables created if the model has categorical variables that generates additional dummy variables during estimation or the number of additional variables created if the model involves interaction terms.
Examples
library(Dyn4cast)
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
x <- gl(2, 10, 20, labels = c("Ctl","Trt"))
y <- c(ctl, trt)
Model <- lm(y ~ x)
Type <- "LM"
MallowsCp(Model = Model, y = y, x = x, type = Type, Nlevels = 0)
#> [1] 2