This function fits the underlying CTMC give the state transition data and the transition times using the maximum likelihood method (MLE)
ctmcFit(data, byrow = TRUE, name = "", confidencelevel = 0.95)It is a list of two elements. The first element is a character vector denoting the states. The second is a numeric vector denoting the corresponding transition times.
Determines if the output transition probabilities of the underlying embedded DTMC are by row.
Optional name for the CTMC.
Confidence level for the confidence interval construnction.
It returns a list containing the CTMC object and the confidence intervals.
Note that in data, there must exist an element wise corresponding between the two elements of the list and that data[[2]][1] is always 0.
Continuous Time Markov Chains (vignette), Sai Bhargav Yalamanchi, Giorgio Alfredo Spedicato 2015
data <- list(c("a", "b", "c", "a", "b", "a", "c", "b", "c"), c(0, 0.8, 2.1, 2.4, 4, 5, 5.9, 8.2, 9))
ctmcFit(data)
#> $estimate
#> An object of class "ctmc"
#> Slot "states":
#> [1] "a" "b" "c"
#>
#> Slot "byrow":
#> [1] TRUE
#>
#> Slot "generator":
#> a b c
#> a -0.9090909 0.6060606 0.3030303
#> b 0.3225806 -0.9677419 0.6451613
#> c 0.3846154 0.3846154 -0.7692308
#>
#> Slot "name":
#> [1] ""
#>
#>
#> $errors
#> $errors$dtmcConfidenceInterval
#> $errors$dtmcConfidenceInterval$confidenceLevel
#> [1] 0.95
#>
#> $errors$dtmcConfidenceInterval$lowerEndpointMatrix
#> a b c
#> a 0 0 0
#> b 0 0 0
#> c 0 0 0
#>
#> $errors$dtmcConfidenceInterval$upperEndpointMatrix
#> a b c
#> a 0.0000000 1 0.9866548
#> b 0.9866548 0 1.0000000
#> c 1.0000000 1 0.0000000
#>
#>
#> $errors$lambdaConfidenceInterval
#> $errors$lambdaConfidenceInterval$lowerEndpointVector
#> [1] 0.04576665 0.04871934 0.00000000
#>
#> $errors$lambdaConfidenceInterval$upperEndpointVector
#> [1] 0.04576665 0.04871934 -0.12545166
#>
#>
#>