Check that landscape inputs have valid values and matching properties.

check(a, b)

# S4 method for Raster,missing

# S4 method for SpatRaster,missing

# S4 method for matrix,missing

# S4 method for SpatRaster,SpatRaster
check(a, b)

# S4 method for Raster,Raster
check(a, b)

# S4 method for matrix,matrix
check(a, b)

# S4 method for samc,Raster
check(a, b)

# S4 method for samc,SpatRaster
check(a, b)

# S4 method for samc,matrix
check(a, b)

# S4 method for samc,numeric
check(a, b)



A samc-class, matrix, or RasterLayer-class object


A matrix or RasterLayer-class object


See Details section.


This function is used to ensure that inputs (resistance, absorption, fidelity, and occupancy) have valid values and the same properties. This includes checking the CRS (if using raster inputs), dimensions, and locations of cells with NA data. It can be used to directly compare two matrices or two rasters, or it can be used to check a samc-class object against a matrix or raster.

It can also be used to check a numeric vector against a samc-class object created from a P matrix. In this case, the length of the vector must be equal to the number of transient states. If the transient states are named, the vector must contain the same names.

The function returns TRUE if the inputs have matching properties. Otherwise, it will stop execution and print an error message with details about the difference between the two inputs.

Note that the package assumes the different landscape inputs will be the same type, either matrices or RasterLayers. Mixing RasterLayer data and matrix data is not supported.


# "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: 50%  (~10s remaining)       
Computing: 99%  (~0s remaining)       
Computing: 100% (done)                         
#> 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)