nlar methods {tsDyn} | R Documentation |
Generic ‘nlar’ methods
## S3 method for class 'nlar': AIC(object, ...) ## S3 method for class 'nlar': coef(object, ...) ## S3 method for class 'nlar': fitted(object, ...) ## S3 method for class 'nlar': MAPE(object, ...) ## S3 method for class 'nlar': mse(object, ...) ## S3 method for class 'nlar': print(x, digits = max(3, getOption("digits") - 3), ...) ## S3 method for class 'nlar': residuals(object, ...) ## S3 method for class 'nlar': summary(object, ...) ## S3 method for class 'nlar': plot(x, ask=interactive(), ...) ## S3 method for class 'nlar': predict(object, newdata, n.ahead, simulate=FALSE, ...) ## S3 method for class 'nlar': toLatex(object, ...)
x, object |
fitted ‘nlar’ object |
newdata |
data to which to apply the prediction |
n.ahead |
number of steps ahead at which to predict |
simulate |
if TRUE , new observations are simulated from underlying Data Generating Process |
ask |
graphical option. See par |
digits |
See printCoefmat |
... |
further arguments to be passed to and from other methods |
Antonio, Fabio Di Narzo
availableModels
for listing all currently available models.
x <- log10(lynx) mod.setar <- setar(x, m=2, thDelay=1, th=3.25) mod.setar AIC(mod.setar) mse(mod.setar) MAPE(mod.setar) coef(mod.setar) summary(mod.setar) e <- residuals(mod.setar) e <- e[!is.na(e)] plot(e) acf(e) plot(x) lines(fitted(mod.setar), lty=2) legend(x=1910, y=3.9,lty=c(1,2), legend=c("observed","fitted")) plot(mod.setar)