Monte Carlo resampling of a time series using a discretised version of y
, a sequence of bin
numbers with unique values equal to nbins
:
The discrete version of y
will be used to generate a transition matrix of size nbins X nbins
.
This transition matrix will be used to resample values
Time series 1. The goal of the permutation test will be to decide whether the difference y1-targetValue != 0
for each time point, given alpha
.
An optional second time series. If this timeseries is provided then the goal of the permutation test will be the to decide wether the difference y2-y1 != targetValue
for each time point, given alpha
.
The target value for the permutation test. If NULL
, the function will return a data frame with the block randomised surrogates columns (default = 0
)
Number of bins to use (default = ceiling(2*length(y1)^(1/3))
)
Number of permutations (default = 19
)
Alpha level for deciding significance (default = 0.05
)
keepNA
Resampled series
Other Time series operations:
ts_center()
,
ts_changeindex()
,
ts_checkfix()
,
ts_detrend()
,
ts_diff()
,
ts_discrete()
,
ts_duration()
,
ts_embed()
,
ts_integrate()
,
ts_levels()
,
ts_peaks()
,
ts_permtest_block()
,
ts_rasterize()
,
ts_sd()
,
ts_slice()
,
ts_slopes()
,
ts_standardise()
,
ts_sumorder()
,
ts_symbolic()
,
ts_trimfill()
,
ts_windower()
set.seed(4321)
y <- rnorm(5000)
ts_permtest_transmat(y)
#> rbin ry
#> [1,] 16 0.02075621
#> [2,] 20 0.78987315
#> [3,] 11 -1.14036837
#> [4,] 9 -1.46663841
#> [5,] 14 -0.38170474
#> [6,] 17 0.13077913
#> [7,] 22 1.21152759
#> [8,] 11 -1.01566755
#> [9,] 16 0.03722538
#> [10,] 17 0.28382814
#> [11,] 20 0.73169567
#> [12,] 12 -0.79037980
#> [13,] 13 -0.65595026
#> [14,] 12 -0.82335198
#> [15,] 19 0.67282503
#> [16,] 25 1.84039798
#> [17,] 15 -0.10940063
#> [18,] 7 -1.78496950
#> [19,] 19 0.70681715
#> [20,] 21 0.96253150
#> [21,] 22 1.31323045
#> [22,] 13 -0.69397775
#> [23,] 10 -1.22548324
#> [24,] 15 -0.25772863
#> [25,] 20 0.71951206
#> [26,] 21 0.93210270
#> [27,] 13 -0.66000699
#> [28,] 9 -1.41892332
#> [29,] 19 0.51425756
#> [30,] 24 1.73325662
#> [31,] 11 -0.99692179
#> [32,] 23 1.36824680
#> [33,] 18 0.33640892
#> [34,] 26 1.96289025
#> [35,] 26 2.01432372