Skip to contents

Calculate beta credible intervals for all transition matrix entries

Source: R/CrI_matrix.R
transition_CrI.Rd

Computes the marginal posterior beta credible intervals for every entry in the transition matrix, including the probability of dying. The marginal posterior distribution of each transition probability follows a beta distribution derived from the Dirichlet-multinomial model, using the augmented fate matrix (TN) returned by fill_transitions.

Usage

transition_CrI(
  TF,
  N,
  P = NULL,
  priorweight = -1,
  ci = 0.95,
  stage_names = NULL
)

Arguments

TF

A list of two matrices, T and F, as output by projection.matrix.

N

A vector of observed stage distribution at the start of the transition period.

P

A matrix of priors for each column. Defaults to uniform.

priorweight

Total weight for each column of prior as a percentage of sample size, or 1 if negative. Defaults to -1 (uninformative).

ci

Credible interval width as a probability between 0 and 1. Defaults to 0.95 (95% credible interval).

stage_names

Optional character vector of stage names in the same order as the columns of the transition matrix. If NULL, names are taken from colnames(TF$T), or generic labels "Stage 1", "Stage 2", etc. are used.

Value

A data frame with one row per fate per stage (including the dead fate) and the following columns:

from_stage

Character. The source stage (column of the matrix).

to_stage

Character. The destination stage, including "dead".

mean

Numeric. Posterior mean transition probability.

lower

Numeric. Lower bound of the credible interval.

upper

Numeric. Upper bound of the credible interval.

See also

plot_transition_CrI for visualising the output.

Examples

T_mat <- matrix(c(0.5, 0.3, 0.0,
                  0.2, 0.4, 0.1,
                  0.0, 0.1, 0.7), nrow = 3, ncol = 3)
F_mat <- matrix(c(0.0, 0.0, 1.5,
                  0.0, 0.0, 0.0,
                  0.0, 0.0, 0.0), nrow = 3, ncol = 3)
TF <- list(T = T_mat, F = F_mat)
N  <- c(10, 5, 8)

# Default 95% credible intervals
cri <- transition_CrI(TF, N, stage_names = c("plantula", "juvenile", "adult"))
cri
#>    from_stage to_stage       mean        lower     upper
#> 1    plantula plantula 0.47727273 2.050069e-01 0.7573488
#> 2    plantula juvenile 0.29545455 7.933447e-02 0.5813692
#> 3    plantula    adult 0.02272727 2.540382e-08 0.1523734
#> 4    plantula     dead 0.20454545 3.411207e-02 0.4745221
#> 5    juvenile plantula 0.20833333 1.210489e-02 0.5781745
#> 6    juvenile juvenile 0.37500000 7.220923e-02 0.7550201
#> 7    juvenile    adult 0.12500000 1.277616e-03 0.4572469
#> 8    juvenile     dead 0.29166667 3.612337e-02 0.6744512
#> 9       adult plantula 0.02777778 3.146432e-08 0.1851079
#> 10      adult juvenile 0.11666667 3.866234e-03 0.3785689
#> 11      adult    adult 0.65000000 3.323634e-01 0.9050806
#> 12      adult     dead 0.20555556 2.575628e-02 0.5055951

# 90% credible intervals
transition_CrI(TF, N, stage_names = c("plantula", "juvenile", "adult"), ci = 0.90)
#>    from_stage to_stage       mean        lower     upper
#> 1    plantula plantula 0.47727273 2.419147e-01 0.7175380
#> 2    plantula juvenile 0.29545455 1.019478e-01 0.5328783
#> 3    plantula    adult 0.02272727 4.064623e-07 0.1100185
#> 4    plantula     dead 0.20454545 4.799268e-02 0.4238845
#> 5    juvenile plantula 0.20833333 2.140606e-02 0.5095062
#> 6    juvenile juvenile 0.37500000 1.007898e-01 0.6997954
#> 7    juvenile    adult 0.12500000 3.234720e-03 0.3843419
#> 8    juvenile     dead 0.29166667 5.490716e-02 0.6119090
#> 9       adult plantula 0.02777778 5.034306e-07 0.1344012
#> 10      adult juvenile 0.11666667 7.575849e-03 0.3214247
#> 11      adult    adult 0.65000000 3.826318e-01 0.8776073
#> 12      adult     dead 0.20555556 3.851405e-02 0.4493039