Calculates the expected time to absorption
survival(samc, init)
# S4 method for samc,missing
survival(samc)
# S4 method for samc,ANY
survival(samc, init)A samc-class object created using the samc function.
Sets the initial state \(\psi\) of the transients states. Input
must be able to pass the check function when compared against
the samc-class object. Can only contain positive finite values.
See Details
\(z=(I-Q)^{-1}{\cdot}1=F{\cdot}1\)
survival(samc)
The result is a vector \(\mathbf{v}\) where \(\mathbf{v}_i\) is the expected time to absorption 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.
\(\psi^Tz\)
survival(samc, init)
The result is a numeric that is the expected time to absorption given an initial state \(\psi\).
Any relevant performance information about this function can be found in the
performance vignette: vignette("performance", package = "samc")
# "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)