Calculates the probability of absorption for absorbing states rather than individual transient states. This is distint from, yet very closely linked to, the mortality() metric, which calculates the probability of absorption at individual transient states. If the results of the mortality() metric are decomposed into individual results for each absorbing state, then the sums of the individual results for every transient state are equivalent to the results of the absorption() metric.
Usage
absorption(samc, init, origin)
# S4 method for class 'samc,missing,missing'
absorption(samc)
# S4 method for class 'samc,missing,location'
absorption(samc, origin)
# S4 method for class 'samc,ANY,missing'
absorption(samc, init)Arguments
- samc
A
samc-classobject created using thesamcfunction.- init
Sets the initial state \(\psi\) of the transients states. Input must be able to pass the
checkfunction when compared against thesamc-classobject. Can only contain positive finite values.- origin
A positive integer or character name representing transient state \(\mathit{i}\). Corresponds to row \(\mathit{i}\) of matrix \(\mathbf{P}\) in the
samc-classobject. When paired with thedestparameter, multiple values may be provided as a vector.
Details
\(A = F R\)
absorption(samc)
The result is a matrix \(M\) where \(M_{i,k}\) is the probability of absorption due to absorbing state \(\mathit{k}\) if starting at transient state \(\mathit{i}\).
absorption(samc, origin)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_{k}\) is the probability of absorption due to absorbing state \(\mathit{k}\) if starting at transient state \(\mathit{i}\).
\(\psi^T A\)
absorption(samc, init)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_{k}\) is the probability of absorption due to absorbing state \(\mathit{k}\) given an initial state \(\psi\).
Performance
Any relevant performance information about this function can be found in the
performance vignette: vignette("performance", package = "samc")
Examples
# "Load" the data. In this case we are using data built into the package.
# In practice, users will likely load raster data using the raster() function
# from the raster package.
res_data <- samc::example_split_corridor$res
abs_data <- samc::example_split_corridor$abs
init_data <- samc::example_split_corridor$init
# Make sure our data meets the basic input requirements of the package using
# the check() function.
check(res_data, abs_data)
#> [1] TRUE
check(res_data, init_data)
#> [1] TRUE
# Setup the details for a random-walk model
rw_model <- list(fun = function(x) 1/mean(x), # Function for calculating transition probabilities
dir = 8, # Directions of the transitions. Either 4 or 8.
sym = TRUE) # Is the function symmetric?
# Create a `samc-class` object with the resistance and absorption data using
# the samc() function. We use the recipricol of the arithmetic mean for
# calculating the transition matrix. Note, the input data here are matrices,
# not RasterLayers.
samc_obj <- samc(res_data, abs_data, model = rw_model)
# Convert the initial state data to probabilities
init_prob_data <- init_data / sum(init_data, na.rm = TRUE)
# Calculate short- and long-term metrics using the analytical functions
short_mort <- mortality(samc_obj, init_prob_data, time = 50)
short_dist <- distribution(samc_obj, origin = 3, time = 50)
long_disp <- dispersal(samc_obj, init_prob_data)
#>
#> Cached diagonal not found.
#> Performing setup. This can take several minutes... Complete.
#> Calculating matrix inverse diagonal...
#>
Computing: 49% (~10s remaining)
Computing: 98% (~0s remaining)
Computing: 100% (done)
#>
Complete
#> Diagonal has been cached. Continuing with metric calculation...
visit <- visitation(samc_obj, dest = 4)
surv <- survival(samc_obj)
# Use the map() function to turn vector results into RasterLayer objects.
short_mort_map <- map(samc_obj, short_mort)
short_dist_map <- map(samc_obj, short_dist)
long_disp_map <- map(samc_obj, long_disp)
visit_map <- map(samc_obj, visit)
surv_map <- map(samc_obj, surv)