visitation_net: Calculate net visitation

Description

Calculates the net number of times that transient states are visited before absorption.

Usage

visitation_net(samc, init, origin, dest)
# S4 method for samc,missing,location,location
visitation_net(samc, origin, dest)

Value

See Details

Arguments

samc: A samc-class object created using the samc function.
init: Sets the initial state $ψ$ 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.
origin: A positive integer or character name representing transient state $i$ . Corresponds to row $i$ of matrix $P$ in the samc-class object. When paired with the dest parameter, multiple values may be provided as a vector.
dest: A positive integer or character name representing transient state $j$ . Corresponds to column $j$ of matrix $P$ in the samc-class object. When paired with the origin parameter, multiple values may be provided as a vector.

Performance

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

Details

The visitation function calculates the

Examples

Run this code

# "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)
check(res_data, init_data)

# 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)
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)

Run the code above in your browser using DataLab