greybox (version 0.3.0)

lmDynamic: Combine regressions based on point information criteria

Description

Function combines parameters of linear regressions of the first variable on all the other provided data using pAIC weights

Usage

lmDynamic(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", which includes time varying parameters and dynamic importance of each variable.

Details

The algorithm uses lm() to fit different models and then combines the models based on the selected point IC. This is a dynamic counterpart of lmCombine function.

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

stepwise, lmCombine

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 <- lmDynamic(inSample,bruteForce=TRUE)
forecast(ourModel,outSample)
plot(forecast(ourModel,outSample))

# }