Calculates the probability of absorption at individual transient states.
Usage
mortality(samc, init, origin, dest, time)
# S4 method for class 'samc,missing,missing,missing,numeric'
mortality(samc, time)
# S4 method for class 'samc,missing,location,missing,numeric'
mortality(samc, origin, time)
# S4 method for class 'samc,missing,missing,location,numeric'
mortality(samc, dest, time)
# S4 method for class 'samc,missing,location,location,numeric'
mortality(samc, origin, dest, time)
# S4 method for class 'samc,ANY,missing,missing,numeric'
mortality(samc, init, time)
# S4 method for class 'samc,ANY,missing,location,numeric'
mortality(samc, init, dest, time)
# S4 method for class 'samc,missing,missing,missing,missing'
mortality(samc)
# S4 method for class 'samc,missing,location,missing,missing'
mortality(samc, origin)
# S4 method for class 'samc,missing,missing,location,missing'
mortality(samc, dest)
# S4 method for class 'samc,missing,location,location,missing'
mortality(samc, origin, dest)
# S4 method for class 'samc,ANY,missing,missing,missing'
mortality(samc, init)
# S4 method for class 'samc,ANY,missing,location,missing'
mortality(samc, init, dest)
Arguments
- samc
A
samc-class
object created using thesamc
function.- init
Sets the initial state \(\psi\) of the transients states. Input must be able to pass the
check
function when compared against thesamc-class
object. 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-class
object. When paired with thedest
parameter, multiple values may be provided as a vector.- dest
A positive integer or character name representing transient state \(\mathit{j}\). Corresponds to column \(\mathit{j}\) of matrix \(\mathbf{P}\) in the
samc-class
object. When paired with theorigin
parameter, multiple values may be provided as a vector.- time
A positive integer or a vector of positive integers representing \(\mathit{t}\) time steps. Vectors must be ordered and contain no duplicates. Vectors may not be used for metrics that return dense matrices. The maximum time step value is capped at 10,000 due to numerical precision issues.
Details
\(\tilde{B}_t = \tilde{F} \tilde{R}\)
mortality(samc, time)
The result is a matrix \(M\) where \(M_{i,j}\) is the probability of absorption at transient state \(\mathit{j}\) within \(\mathit{t}\) or fewer steps if starting at transient state \(\mathit{i}\).
The returned matrix will always be dense and cannot be optimized. Must enable override to use (see
samc-class
).mortality(samc, origin, time)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_j\) is the probability of absorption at transient state \(\mathit{j}\) within \(\mathit{t}\) or fewer steps if starting at transient state \(\mathit{i}\).
If multiple time steps were provided as a vector, then the result will be an ordered named list containing a vector for each time step.
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.mortality(samc, dest, time)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_i\) is the probability of absorption at transient state \(\mathit{j}\) within \(\mathit{t}\) or fewer steps if starting at transient state \(\mathit{i}\).
If multiple time steps were provided as a vector, then the result will be an ordered named list containing a vector for each time step.
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.mortality(samc, origin, dest, time)
The result is a numeric value that is the probability of absorption at transient state \(\mathit{j}\) within \(\mathit{t}\) or fewer time steps if starting at transient state \(\mathit{i}\).
If multiple time steps were provided as a vector, then the result will be an ordered named list containing a numeric value for each time step.
\(\psi^T \tilde{B}_t\)
mortality(samc, init, time)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_j\) is the unconditional probability of absorption at transient state \(\mathit{j}\) within \(\mathit{t}\) or fewer steps given an initial state \(\psi\).
If multiple time steps were provided as a vector, then the result will be an ordered named list containing a vector for each time step.
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.
\(B = F \tilde{R}\)
mortality(samc)
The result is a matrix \(M\) where \(M_{i,j}\) is the probability of absorption at transient state \(\mathit{j}\) if starting at transient state \(\mathit{i}\).
The returned matrix will always be dense and cannot be optimized. Must enable override to use (see
samc-class
).mortality(samc, origin)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_j\) is the probability of absorption at transient state \(\mathit{j}\) if starting at transient state \(\mathit{i}\).
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.mortality(samc, dest)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_i\) is the probability of absorption at transient state \(\mathit{j}\) if starting at transient state \(\mathit{i}\).
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.mortality(samc, origin, dest)
The result is a numeric value that is the probability of absorption at transient state \(\mathit{j}\) if starting at transient state \(\mathit{i}\).
\(\psi^T B\)
mortality(samc, init)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_j\) is the unconditional probability of absorption at transient state \(\mathit{j}\) given an initial state \(\psi\).
If the samc-class object was created using matrix or RasterLayer maps, then vector \(\mathbf{v}\) can be mapped to a RasterLayer using the
map
function.
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: 99% (~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)