Create a transition matrix from a discrete time series, e.g. to generate Monte Carlo simulations.
A discrete numeric vector or time series, e.g. transformed using ts_discrete(), or, ts_symbolic().
The number of bins used to transform a continuous time series, or, the number of expected (given nbins, or, theoretically possible) values for a discrete series (default = length(unique(yd)))
A transition probability matrix
set.seed(4321)
# Random uniform numbers
y <- runif(10,0,20)
# Discrete version
yd <- ts_discrete(y, nbins = 10)
# Transition probabilities
ts_transmat(yd = yd, nbins = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0000000 0.0000000
#> [2,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1000000 0.1000000
#> [3,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1000000 0.1000000
#> [4,] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0000000 1.0000000
#> [5,] 0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0000000 0.0000000
#> [6,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1000000 0.1000000
#> [7,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1000000 0.1000000
#> [8,] 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1000000 0.1000000
#> [9,] 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6666667 0.3333333
#> [10,] 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0000000 0.0000000
# Note: The number of 'observed' bins differs from 'expected' bins
table(yd)
#> yd
#> 1 4 5 6 9 10
#> 1 1 2 1 3 2
# Not specifying the expected bins will generate a different matrix!
ts_transmat(yd = yd, nbins = length(unique(yd)))
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#> [2,] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#> [3,] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#> [4,] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#> [5,] 0.5000000 0.0000000 0.0000000 0.0000000 0.0000000 0.5000000
#> [6,] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667