greybox (version 0.3.0)

alm: Advanced Linear Model

Description

Function estimates model based on the selected distribution

Usage

alm(formula, data, subset = NULL, na.action, distribution = c("laplace",
  "s", "fnorm", "chisq"))

Arguments

formula

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

data

a data frame or a matrix, containing the variables in the model.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

na.action

a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The factory-fresh default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.

distribution

What density function to use in the process.

Value

Function returns model - the final model of the class "alm".

Details

This is a function, similar to lm, but for the cases of several non-normal distributions. These include:

1. Laplace distribution, dlaplace,

2. S-distribution, ds,

3. Folded-normal distribution, dfnorm,

4. Chi-Squared Distribution, dchisq.

Probably some other distributions will be added to this function at some point...

The estimation is done using likelihood of respective distributions.

See Also

stepwise, lmCombine

Examples

# NOT RUN {
xreg <- cbind(rlaplace(100,10,3),rnorm(100,50,5))
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rlaplace(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 <- alm(y~x1+x2, inSample, distribution="laplace")
summary(ourModel)
plot(forecast(ourModel,outSample))

# }