greybox | R Documentation |
Toolbox for working with univariate models for purposes of analysis and forecasting
Package: | greybox |
Type: | Package |
Date: | 2018-02-13 - Inf |
License: | GPL-2 |
The following functions are included in the package:
AICc and BICc - AIC / BIC corrected for the sample size.
pointLik - point likelihood of the function.
pAIC, pAICc, pBIC, pBICc - point versions of respective information criteria.
coefbootstrap - Method that uses a simple implementation of the case resampling to get bootstrapped estimates of parameters of the model.
dsrboot - Bootstrap inspired by the meboot package, that creates bootstraped series based on the provided one.
determination - Coefficients of determination between different exogenous variables.
temporaldummy - Matrix with seasonal dummy variables.
outlierdummy - Matrix with dummies for outliers.
alm - Advanced Linear Model - regression, estimated using likelihood with specified distribution (e.g. Laplace or Chi-Squared).
sm - Scale Model - Regression model for scale of distribution
(e.g. for Variance of Normal distribution). Requires an lm()
or alm()
model.
stepwise - Stepwise based on information criteria and partial correlations. Efficient and fast.
xregExpander - Function that expands the provided data into the data with lags and leads.
xregTransformer - Function produces mathematical transformations of the variables, such as taking logarithms, square roots etc.
xregMultiplier - Function produces cross-products of the matrix of the provided variables.
lmCombine - Function combines lm models from the estimated based on information criteria weights.
lmDynamic - Dynamic regression based on point AIC.
ro - Rolling origin evaluation.
Distributions - document explaining the distribution functions of the greybox package.
spread - function that produces scatterplots / boxplots / tableplots, depending on the types of variables.
assoc - function that calculates measures of association, depending on the types of variables.
Ivan Svetunkov, ivan@svetunkov.com
stepwise, lmCombine
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)
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