karma.adf: Augmented Dickey-Fuller test for stationarity.

In snarf-snarf/karma: Complementary tools for automated time-series machine learning.

Description

Augmented Dickey-Fuller test for stationarity.

Usage

karma.adf(y, modeltype = "none", lags = 1, diffs = 0, log = F, dw = F,
  stdout = T)

Arguments

y	A univariate time-series vector; type <numeric> or <ts>.
modeltype	Set ADF model with intercept and trend, intercept only, or neither. Takes values: "trend", "drift", "none".
lags	Length of lags for autoregression.
diffs	Differencing step. Indicate whether the input series needs to be differenced for stationarity (and to what degree); 0,1,...,n; type <int>.
log	Logarithmic transformation flag. Indicate whether the input series needs to be log-transformed for stationarity; T, F; type <logical>.
stdout	Option to print out all test diagnostics; <logical>.

Value

Object of class "karma.adf".

See Also

tseries, forecast, car

Examples

# Apply ADF test and print out diagnostics:
adf.object <- karma.adf(WWWusage, modeltype = "drift", lags = 1, diffs = 0, log = F, stdout = T)
print(adf.object$stationarity)

snarf-snarf/karma documentation built on May 24, 2019, 7:19 a.m.