Compute probability to be in a given state

Compute probability to be in a given state

compute.state.prob(state_base, tree, state, state_change)

Arguments

state_base

vector of base states.

tree

matrix of probability of being in a given branch. First column is for the basic states, the other are for the change states. Beware tree must be "cumulative".

state

vector of all admissible states

state_change

vector of change states.

Value

matrix of probability to be in a given state (columns) at give year (rows)

Author

Frédéric Grelot, frederic.grelot.1994_cran@m4x.org

Examples

 
state = 1:4
year = 0:10
tree = matrix(1, nrow = length(year), dimnames = list(year = year))
state_base = sample(1:4, length(year), replace = TRUE)
compute.state.prob(state_base, tree, state)
#>     state
#> year 1 2 3 4
#>   0  1 0 0 0
#>   1  0 0 1 0
#>   2  0 0 0 1
#>   3  0 0 1 0
#>   4  1 0 0 0
#>   5  0 0 1 0
#>   6  0 0 1 0
#>   7  0 1 0 0
#>   8  0 0 0 1
#>   9  0 0 0 1
#>   10 0 1 0 0
state_change = sample(1:4, length(year), replace = TRUE)
tree = structure(
     rbind(c(1, 0), matrix(.5, nrow = length(year) - 1, ncol = 2)),
     dimnames = list(year = year))
result = compute.state.prob(state_base, tree, state, state_base)
rowSums(result)
#>   0   1   2   3   4   5   6   7   8   9  10 
#> 1.0 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

tree = structure(
    rbind(c(1, 0, 0), c(.5, .5, 0), matrix(1/3, nrow = length(year) - 2, ncol = 3)),
    dimnames = list(year = year))
result = compute.state.prob(state_base, tree, state, state_base)
rowSums(result)
#>         0         1         2         3         4         5         6         7 
#> 1.0000000 1.0000000 1.0000000 0.3333333 0.3333333 0.3333333 0.3333333 0.3333333 
#>         8         9        10 
#> 0.3333333 0.3333333 0.3333333