nlar methods
Generic ‘nlar’ methods. Method ‘nlar’ is described in a separate page: predict.nlar
## S3 method for class 'nlar'
AIC(object, k=2,...)
## 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'
toLatex(object, digits, label, ...)x, object |
fitted ‘nlar’ object |
ask |
graphical option. See |
digits |
For print method, see |
k |
numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC |
label |
LaTeX label passed to the equation |
... |
further arguments to be passed to and from other methods |
Mean Absolute Percent Error
Mean Square Error
Diagnostic plots
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.