stepwise: Stepwise selection of regressors

In greybox: Toolbox for Model Building and Forecasting

Stepwise selection of regressors

Description

Function selects variables that give linear regression with the lowest information criteria. The selection is done stepwise (forward) based on partial correlations. This should be a simpler and faster implementation than step() function from ‘stats’ package.

Usage

stepwise(data, ic = c("AICc", "AIC", "BIC", "BICc"), silent = TRUE,
  df = NULL, formula = NULL, subset = NULL, method = c("pearson",
  "kendall", "spearman"), distribution = c("dnorm", "dlaplace", "ds",
  "dgnorm", "dlogis", "dt", "dalaplace", "dlnorm", "dllaplace", "dls",
  "dlgnorm", "dbcnorm", "dfnorm", "dinvgauss", "dgamma", "dpois", "dnbinom",
  "dlogitnorm", "plogis", "pnorm"), occurrence = c("none", "plogis",
  "pnorm"), ...)

Arguments

data	Data frame containing dependant variable in the first column and the others in the rest.
ic	Information criterion to use.
silent	If silent=FALSE, then nothing is silent, everything is printed out. silent=TRUE means that nothing is produced.
df	Number of degrees of freedom to add (should be used if stepwise is used on residuals).
formula	If provided, then the selection will be done from the listed variables in the formula after all the necessary transformations.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
method	Method of correlations calculation. The default is Pearson's correlation, which should be applicable to a wide range of data in different scales.
distribution	Distribution to pass to alm(). See alm for details.
occurrence	what distribution to use for occurrence part. See alm for details.
...	This is temporary and is needed in order to capture "silent" parameter if it is provided.

Details

The algorithm uses alm() to fit different models and cor() to select the next regressor in the sequence.

Some details and examples of application are also given in the vignette "Greybox": vignette("greybox","greybox")

Value

Function returns model - the final model of the class "alm". See alm for details of the output.

Author(s)

Ivan Svetunkov, [e-mail]

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).

• McQuarrie, A. D. (1999). A small-sample correction for the Schwarz SIC model selection criterion. Statistics & Probability Letters, 44(1), 79–86. [10.1016/S0167-7152(98)00294-6](https://doi.org/10.1016/S0167-7152(98)00294-6).

See Also

step, xregExpander, lmCombine

Examples

### 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")
stepwise(xreg)

### Mixture distribution of Log Normal and Cumulative Logit
xreg[,1] <- xreg[,1] * round(exp(xreg[,1]-70) / (1 + exp(xreg[,1]-70)),0)
colnames(xreg) <- c("y","x1","x2","Noise")
ourModel <- stepwise(xreg, distribution="dlnorm",
                     occurrence=stepwise(xreg, distribution="plogis"))
summary(ourModel)

### Fat regression example
xreg <- matrix(rnorm(20000,10,3),100,200)
xreg <- cbind(100+0.5*xreg[,1]-0.75*xreg[,2]+rnorm(100,0,3),xreg,rnorm(100,300,10))
colnames(xreg) <- c("y",paste0("x",c(1:200)),"Noise")
ourModel <- stepwise(xreg,ic="AICc")
plot(ourModel$ICs,type="l",ylim=range(min(ourModel$ICs),max(ourModel$ICs)+5))
points(ourModel$ICs)
text(c(1:length(ourModel$ICs))+0.1,ourModel$ICs+5,names(ourModel$ICs))

greybox documentation built on Sept. 16, 2023, 9:07 a.m.