samc (version 2.0.1)

survival: Calculate survival metrics

Description

Calculates the expected time to absorption

Usage

survival(samc, occ)
# S4 method for samc,missing
survival(samc)
# S4 method for samc,ANY
survival(samc, occ)

Value

See Details

Arguments

samc

A samc-class object created using the samc function.

occ

The initial state \(\psi\) of the Markov chain. If the samc-class objects was constructed using map inputs, then occ must be the same type of input (either RasterLayer-class or matrix), and must have the same properties as initial map inputs (see the check function).

Performance

Any relevant performance information about this function can be found in the performance vignette: vignette("performance", package = "samc")

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, occ)

  The result is a numeric that is the expected time to absorption given an initial state \(\psi\).

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::ex_res_data
abs_data <- samc::ex_abs_data
occ_data <- samc::ex_occ_data


# Make sure our data meets the basic input requirements of the package using
# the check() function.
check(res_data, abs_data)
check(res_data, occ_data)

# Setup the details for our transition function
tr <- 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, tr_args = tr)


# Convert the occupancy data to probability of occurrence
occ_prob_data <- occ_data / sum(occ_data, na.rm = TRUE)


# Calculate short- and long-term metrics using the analytical functions
short_mort <- mortality(samc_obj, occ_prob_data, time = 50)
short_dist <- distribution(samc_obj, origin = 3, time = 50)
long_disp <- dispersal(samc_obj, occ_prob_data)
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)