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)
| data | 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. |
|---|---|
| byrow | Determines if the output transition probabilities of the underlying embedded DTMC are by row. |
| name | Optional name for the CTMC. |
| confidencelevel | 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
Sai Bhargav Yalamanchi
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 #> #> #>