dcatState1Alive1Dead | R Documentation |
The dcatState1Alive1Dead
distribution is a NIMBLE custom distribution which can be used to model and simulate
individual state transition. This function can be used to model transitions from one alive and one dead state.
If z_i,t = 1, individual i can be recruited (transition to state 2) with probability prob1To2_t, so z_i,t+1 ~ dcat(1- prob1To2_t, prob1To2_t, 0,0 , 0) where prob1To2_t represent the probability of an unborn individual to be recruited.
If z_i,t = 2, individual i can die and transition to z_i,t+1=3 with probability prob2To3, or survive with probability 1-prob2To3
Individuals in dead states (z_i,t = 3 ) remain in that state with probability 1, the absorbing state.
If transition probabilities are spatially variable, a probability vector containing the transition probability value in each habitat window can be provided using the "Hab" arguments (e.g. prob1To2Hab,prob2To3Hab).
dcatState1Alive1Dead( x, z, prob1To2 = -999, prob1To2Hab, prob2To3 = -999, prob2To3Hab, s, habitatGrid, log = 0 ) rcatState1Alive1Dead( n, z, prob1To2 = -999, prob1To2Hab, prob2To3 = -999, prob2To3Hab, s, habitatGrid )
x |
Scalar, individual state z_i,t+1. |
z |
Scalar, initial individual state z_i,t. |
prob1To2 |
scalar, probability to transition from state 1 to 2. |
prob1To2Hab |
vector, Spatially-explicit probability to transition from state 2 to 3. The length of the vector should be equal the number of habitat windows in |
prob2To3 |
scalar, probability to transition from state 2 to 3. |
prob2To3Hab |
vector, Spatially-explicit probability to transition from state 2 to 3. The length of the vector should be equal the number of habitat windows in |
s |
Vector of x- and y-coordinates corresponding to the AC location of the individual. Used to extract transition spatially-explicit probabilities when they are provided. |
habitatGrid |
Matrix of habitat window indices. Cell values should correspond to the
order of habitat windows in |
log |
Logical argument, specifying whether to return the log-probability of the distribution.
|
n |
Integer specifying the number of realizations to generate. Only n = 1 is supported. |
Cyril Milleret
# Use the distribution in R z <- 2 prob1To2 <- 0.2 prob2To3 <- 0.7 lowerCoords <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1), nrow = 4, byrow = TRUE) upperCoords <- matrix(c(1, 1, 2, 1, 1, 2, 2, 2), nrow = 4, byrow = TRUE) logIntensities <- log(rep(1,4)) logSumIntensity <- log(sum(c(1:4))) habitatGrid <- matrix(c(1:4), nrow = 2, byrow = TRUE) numGridRows <- nrow(habitatGrid) numGridCols <- ncol(habitatGrid) s <- rbernppAC(n=1, lowerCoords, upperCoords, logIntensities, logSumIntensity, habitatGrid, numGridRows, numGridCols) ## No spatial mortality zPlusOne <- rcatState1Alive1Dead( z = z , prob1To2 = prob1To2 , prob2To3 = prob2To3 , s = s , habitatGrid = habitatGrid) zPlusOne dcatState1Alive1Dead( x = zPlusOne , z = z , prob1To2 = prob1To2 , prob2To3 = prob2To3 , s = s , habitatGrid = habitatGrid) ## With spatial mortality prob2To3Hab <- c(0.60, 0.70, 0.74, 0.65) prob1To2Hab <- c(0.4,0.5,0.1,0.3) zPlusOne <- rcatState1Alive1Dead( z = z , prob1To2Hab = prob1To2Hab , prob2To3Hab = prob2To3Hab , s = s , habitatGrid = habitatGrid) zPlusOne dcatState1Alive1Dead( x = zPlusOne , z = z , prob1To2Hab = prob1To2Hab , prob2To3Hab = prob2To3Hab , s = s , habitatGrid = habitatGrid)
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