notrend_test  R Documentation 
A combination of time series trend tests for testing the null hypothesis of no trend, versus the alternative hypothesis of a linear trend (Student's ttest), or monotonic trend (Mannâ€“Kendall test), or more general, possibly nonmonotonic trend (WAVK test).
notrend_test(
x,
B = 1000,
test = c("t", "MK", "WAVK"),
ar.method = "HVK",
ar.order = NULL,
ic = "BIC",
factor.length = c("user.defined", "adaptive.selection"),
Window = NULL,
q = 3/4,
j = c(8:11)
)
x 
a vector containing a univariate time series. Missing values are not allowed. 
B 
number of bootstrap simulations to obtain empirical critical values. Default is 1000. 
test 
trend test to implement: Student's ttest ( 
ar.method 
method of estimating autoregression coefficients.
Default 
ar.order 
order of the autoregressive model when 
ic 
information criterion used to select the order of autoregressive filter (AIC of BIC),
considering models of orders 
factor.length 
method to define the length of local windows (factors).
Used only if 
Window 
length of the local window (factor), default is

q 
scalar from 0 to 1 to define the set of possible windows when

j 
numeric vector to define the set of possible windows when

This function tests the null hypothesis of no trend
versus different alternatives.
To set some other shape of trend as the null hypothesis, use wavk_test
.
Note that wavk_test
employs hybrid bootstrap, which is an alternative
to the sieve bootstrap employed by the current function.
A list with class "htest"
containing the following components:
method 
name of the method. 
data.name 
name of the data. 
statistic 
value of the test statistic. 
p.value 

alternative 
alternative hypothesis. 
estimate 
list with the following elements: employed AR order and estimated AR coefficients. 
parameter 
window that was used in WAVK test, included in the output only
if 
Vyacheslav Lyubchich
ar
, HVK
, WAVK
,
wavk_test
, vignette("trendtests", package = "funtimes")
## Not run:
# Fix seed for reproducible simulations:
set.seed(1)
#Simulate autoregressive time series of length n with smooth linear trend:
n < 200
tsTrend < 1 + 2*(1:n/n)
tsNoise < arima.sim(n = n, list(order = c(2, 0, 0), ar = c(0.5, 0.1)))
U < tsTrend + tsNoise
plot.ts(U)
#Use ttest
notrend_test(U)
#Use MannKendall test and YuleWalker estimates of the AR parameters
notrend_test(U, test = "MK", ar.method = "yw")
#Use WAVK test for the H0 of no trend, with moutofn selection of the local window:
notrend_test(U, test = "WAVK", factor.length = "adaptive.selection")
# Sample output:
## Sievebootstrap WAVK trend test
##
##data: U
##WAVK test statistic = 21.654, moving window = 15, pvalue < 2.2e16
##alternative hypothesis: (non)monotonic trend.
##sample estimates:
##$AR_order
##[1] 1
##
##$AR_coefficients
## phi_1
##0.4041848
## End(Not run)
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