coef.gets: Extraction functions for 'gets' objects
In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

coef.gets

R Documentation

Extraction functions for 'gets' objects

Description

Extraction functions for objects of class 'gets'

Usage

  ## S3 method for class 'gets'
coef(object, spec=NULL, ...)
  ## S3 method for class 'gets'
fitted(object, spec=NULL, ...)
  ## S3 method for class 'gets'
logLik(object, ...)
  ## S3 method for class 'gets'
plot(x, spec=NULL, col=c("red","blue"),
    lty=c("solid","solid"), lwd=c(1,1), ...)
  ## S3 method for class 'gets'
predict(object, spec=NULL, n.ahead=12, newmxreg=NULL,
    newvxreg=NULL, newindex=NULL, n.sim=5000, innov=NULL, probs=NULL,
    ci.levels=NULL, quantile.type=7, return=TRUE, verbose=FALSE, plot=NULL,
    plot.options=list(), ...)
  ## S3 method for class 'gets'
print(x, signif.stars=TRUE, ...)
  ## S3 method for class 'gets'
residuals(object, std=NULL, ...)
  ## S3 method for class 'gets'
sigma(object, ...)
  ## S3 method for class 'gets'
summary(object, ...)
  ## S3 method for class 'gets'
vcov(object, spec=NULL, ...)

Arguments

`object`	an object of class 'gets'
`x`	an object of class 'gets'
`spec`	NULL, "mean", "variance" or, in some instances, "both". When `NULL` is a valid value, then it is automatically determined whether information pertaining to the mean or variance specification should be returned
`signif.stars`	`logical`. If `TRUE`, then p-values are additionally encoded visually, see `printCoefmat`
`std`	`logical`. If `FALSE` (default), then the mean residuals are returned. If TRUE, then the standardised residuals are returned
`n.ahead`	`integer` that determines how many steps ahead predictions should be generated (the default is 12)
`newmxreg`	a `matrix` of `n.ahead` rows and `NCOL(mxreg)` columns with the out-of-sample values of the `mxreg` regressors
`newvxreg`	a `matrix` of `n.ahead` rows and `NCOL(vxreg)` columns with the out-of-sample values of the `vxreg` regressors
`newindex`	`NULL` (default) or the date-index for the `zoo` object returned by `predict.arx`. If `NULL`, then the function uses the in-sample `index` to generate the out-of-sample index
`n.sim`	`integer`, the number of replications used for the generation of the forecasts
`innov`	`NULL` (default) or a vector of length `n.ahead * n.sim` containing the standardised errors (that is, zero mean and unit variance) used for the forecast simulations. If `NULL`, then a classica bootstrap procedure is used to draw from the standardised in-sample residuals
`probs`	`NULL` (default) or a `vector` with the quantile-levels (values strictly between 0 and 1) of the forecast distribution. If `NULL`, then no quantiles are returned unless `ci.levels` is non-`NULL`
`ci.levels`	`NULL` (default) or a `vector` with the confidence levels (expressed as values strictly between 0 and 1) of the forecast distribution. The upper and lower values of the confidence interval(s) are returned as quantiles
`quantile.type`	an integer between 1 and 9 that selects which algorithm to be used in computing the quantiles, see the argument `type` in `quantile`
`return`	`logical`. If `TRUE` (default), then the out-of-sample predictions are returned. The value `FALSE`, which does not return the predictions, may be of interest if only a prediction plot is of interest
`verbose`	`logical` with default `FALSE`. If `TRUE`, then additional information (typically the quantiles and/or the simulated series) used in the generation of forecasts is returned. If `FALSE`, then only the forecasts are returned
`plot`	`NULL` (default) or logical. If `NULL`, then the value set by `options$plot` (see `options`) determines whether a plot is produced or not. If `TRUE`, then the out-of-sample forecasts are plotted.
`plot.options`	a `list` of options related to the plotting of forecasts, see 'Details'
`col`	colours of fitted (default=red) and actual (default=blue) lines
`lty`	types of fitted (default=solid) and actual (default=solid) lines
`lwd`	widths of fitted (default=1) and actual (default=1) lines
`...`	additional arguments

Details

The plot.options argument is a list that controls the prediction plot, see 'Details' in predict.arx

Value

`coef:`	a numeric vector containing parameter estimates
`fitted:`	a `zoo` object with fitted values
`logLik:`	a numeric, the log-likelihood (normal density)
`plot:`	a plot of the fitted values and the residuals
`predict:`	a `vector` of class `zoo` containing the out-of-sample forecasts, or a `matrix` of class `zoo` containing the out-of-sample forecasts together with prediction-quantiles, or - if `return=FALSE` - `NULL`
`print:`	a print of the estimation results
`residuals:`	a `zoo` object with the residuals
`sigma:`	the regression standard error ('SE of regression')
`summary:`	a print of the items in the `gets` object
`vcov:`	a variance-covariance matrix

Author(s)

Felix Pretis, http://www.felixpretis.org/
James Reade, https://sites.google.com/site/jjamesreade/
Moritz Schwarz, https://www.inet.ox.ac.uk/people/moritz-schwarz/
Genaro Sucarrat, http://www.sucarrat.net/

Examples

##Simulate from an AR(1):
set.seed(123)
y <- arima.sim(list(ar=0.4), 100)

##Simulate four independent Gaussian regressors:
xregs <- matrix(rnorm(4*100), 100, 4)

##estimate an AR(2) with intercept and four conditioning
##regressors in the mean, and a log-ARCH(3) in the variance:
mymod <- arx(y, mc=TRUE, ar=1:2, mxreg=xregs, arch=1:3)

##General-to-Specific (GETS) model selection of the mean:
meanmod <- getsm(mymod)

##General-to-Specific (GETS) model selection of the variance:
varmod <- getsv(mymod)

##print results:
print(meanmod)
print(varmod)

##plot the fitted vs. actual values, and the residuals:
plot(meanmod)
plot(varmod)

##generate and plot predictions of the mean:
predict(meanmod, plot=TRUE)

##print the entries of object 'gets':
summary(meanmod)
summary(varmod)

##extract coefficients of the simplified (specific) model:
coef(meanmod) #mean spec
coef(varmod) #variance spec

##extract log-likelihood:
logLik(mymod)

##extract coefficient-covariance matrix of simplified
##(specific) model:
vcov(meanmod) #mean spec
vcov(varmod) #variance spec

##extract and plot the fitted values:
mfit <- fitted(meanmod) #mean fit
plot(mfit)
vfit <- fitted(varmod) #variance fit
plot(vfit)

##extract and plot residuals:
epshat <- residuals(meanmod)
plot(epshat)

##extract and plot standardised residuals:
zhat <- residuals(varmod)
plot(zhat)

gets documentation built on Oct. 10, 2022, 1:06 a.m.