Calculate net visitation

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

Usage

visitation_net(samc, init, origin, dest)

# S4 method for class 'samc,missing,location,missing'
visitation_net(samc, origin)

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

# S4 method for class 'samc,ANY,missing,missing'
visitation_net(samc, init)

# S4 method for class 'samc,ANY,missing,location'
visitation_net(samc, init, dest)

Arguments

samc

A samc-class object created using the samc function.

init

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.

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 the dest 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 the origin parameter, multiple values may be provided as a vector.

Value

See Details

Details

Add details here

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: 51%  (~9s 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)