tsDyn (version 0.7-60)

nlar methods: nlar methods

Description

Generic nlar methods

Usage

## S3 method for class 'nlar':
AIC(object, k=2,\dots)
## S3 method for class 'nlar':
coef(object, \dots)
## S3 method for class 'nlar':
fitted(object, \dots)
## S3 method for class 'nlar':
MAPE(object, \dots)
## S3 method for class 'nlar':
mse(object, \dots)
## S3 method for class 'nlar':
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'nlar':
residuals(object, \dots)
## S3 method for class 'nlar':
summary(object, \dots)
## S3 method for class 'nlar':
plot(x, ask=interactive(), ...)
## S3 method for class 'nlar':
predict(object, newdata, n.ahead, simulate=FALSE, \dots)
## S3 method for class 'nlar':
toLatex(object, \dots)

Arguments

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

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC

...

further arguments to be passed to and from other methods

Details

[object Object],[object Object],[object Object],[object Object]

See Also

availableModels for listing all currently available models.

Examples

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)