greybox (version 0.3.0)

lmCombine: Combine regressions based on information criteria

Description

Function combines parameters of linear regressions of the first variable on all the other provided data.

Usage

lmCombine(data, ic = c("AICc", "AIC", "BIC", "BICc"), bruteForce = FALSE,
  silent = TRUE)

Arguments

data

Data frame containing dependent variable in the first column and the others in the rest.

ic

Information criterion to use.

bruteForce

If TRUE, then all the possible models are generated and combined. Otherwise the best model is found and then models around that one are produced and then combined.

silent

If FALSE, then nothing is silent, everything is printed out. TRUE means that nothing is produced.

Value

Function returns model - the final model of the class "lm.combined".

Details

The algorithm uses lm() to fit different models and then combines the models based on the selected IC.

References

• Burnham Kenneth P. and Anderson David R. (2002). Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach. Springer-Verlag New York. DOI: [10.1007/b97636](http://dx.doi.org/10.1007/b97636).

See Also

step, xregExpander, stepwise

Examples

# NOT RUN {
### Simple example
xreg <- cbind(rnorm(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
colnames(xreg) <- c("y","x1","x2","Noise")
inSample <- xreg[1:80,]
outSample <- xreg[-c(1:80),]
# Combine all the possible models
ourModel <- lmCombine(inSample,bruteForce=TRUE)
forecast(ourModel,outSample)
plot(forecast(ourModel,outSample))

### Fat regression example
xreg <- matrix(rnorm(5000,10,3),50,100)
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(50,0,3),xreg,rnorm(50,300,10))
colnames(xreg) <- c("y",paste0("x",c(1:100)),"Noise")
inSample <- xreg[1:40,]
outSample <- xreg[-c(1:40),]
# Combine only the models close to the optimal
ourModel <- lmCombine(inSample,ic="BICc",bruteForce=FALSE)
summary(ourModel)
plot(forecast(ourModel,outSample))

# }