This package provides a series of functions for working with absorbing Markov chains. A Markov chain is a model consisting of multiple states and describes how transitions occur between these states. An absorbing Markov chain is a special kind of Markov chain where every state is a transient state that can eventually reach one or more absorbing states. Absorbing states are a special type of state that cannot be left. Absorbing Markov chains can be represented using a \(P\) matrix with the following structure:
\[ P = \begin{bmatrix} Q & R \\ 0 & I \end{bmatrix} \] Where:
samc-class
The samc-class
is used to manage the \(P\) matrix and other information to help ensure that the calculations used by the rest of the package are used correctly. Creating an samc-class
object is the mandatory first step in the package, and is created using the samc()
utility function. When creating the \(P\) matrix, samc()
only treats the \(R\) matrix portion as a single column containing the total absorption probability for each transient state. The samc()
function has several parameters that provide a number of different options for constructing the \(P\) matrix that is at the core of the samc-class
.
The first option is using a map of resistance (or conductance) and a map of total absorption with a list of transition arguments to calculate the transition probabilities between cells in the maps. There are certain requirements for these maps:
RasterLayer
objects. They have to be the same type.NA
s are allowed in the cells, but must match between the sets of data. I.e., if cell [3, 6]
of the resistance data has a NA
value, then cell [3, 6]
of the absorption data must also have a NA
value, and vice versa.If using RasterLayer
objects, then additional conditions must be met:
An optional fidelity map may be provided. This map would represent the probability of no transition between timesteps (e.g., no movement). By default, the package treats all cells in the maps the same and uses a value of 0
for fidelity. If used, the fidelity map must meet all of the same requirements listed above for the other map inputs.
A TransitionLayer
object can be manually created using the gdistance package and can be further modified before being supplied as an input to samc()
, allowing for additional flexibility in constructing the \(P\) matrix. While the package is able to check for some potential issues in the TranstionLayer
, it is still possible to accidentally modify the TransitionLayer
in a way that causes issues that cannot be detected by the package, so extra care must be taken by the user.
This option requires a RasterLayer
map of total absorption. This map must match the RasterLayer
that was used to create the TransitionLayer
using the same conditions as Option 1 (above).
This option also allows the input of a RasterLayer
fidelity map with the same requirements as Option 1 (above).
The Third option for using this package is to directly supply a \(P\) matrix. The \(P\) matrix can be provided either as a regular matrix or a dgCmatrix
, which is a sparse matrix object available through the Matrix package. The \(R\) portion of the \(P\) matrix must be a single column which represents the total absorption probability for each transient state.
The advantage of this approach is total flexibility. The disadvantage is that the \(P\) matrix can be created with certain properties that would lead to crashes, and the package is unable to detect all of them at this time. The other disadvantage is that the package cannot map the results back to anything for visualization purposes.
A future version of the package will incorporate igraph support for graph based inputs. This will provide the flexibility of a custom \(P\) matrix, but will generally be more user-friendly to construct, be able to perform more thorough data checking to avoid issues in the \(P\) matrix, and allow for mapping the results back to a graph for visualization purposes.
In addition to the samc()
function, the package has other utility functions that users might find helpful:
check()
function is used to check that input map data meets the data requirements outlined above. It can be used to compare two RasterLayer
objects, two matrix
objects, or check either a RasterLayer
or a matrix
against an already created samc-class
object. It can also be used with a RasterStack
to check all the layers in the stack against one another.map()
function is used to simplify mapping vector results based on input maps and returns a RasterLayer
. This is provided because R handles matrices and raster layers somewhat differently when reading and writing vector data, which can cause users to map the data incorrectly if they aren’t careful. It also handles mapping with NA values, another potential source of error.locate()
function is used to get cell numbers for use as origin
and dest
values in various analytical function arguments. This function should be used instead of cellFromXY()
in the raster package because cellFromXY()
cell numbers do not necessarily correspond to cell numbers in the samc package (the samc package does not assign cell numbers to NA
cells, whereas the raster package does). The locate()
function can be used to return a RasterLayer
with the cell numbers encoded as cell values by simply excluding the xy
argument.pairwise()
function is provided to easily and efficiently run specific metrics for all the pairwise combinations of start and end locations.The package implements functions for the formulas provided in Table 1 of Fletcher et al. (2019), as well as other new ones since that publication. Many of the formulas are related conceptually, and are grouped together into single functions with multiple parameter signatures to reduce the number of unique function names needed. Note that the descriptions assume \(\psi\) contains probabilities (see above). The following descriptions were written in an ecological context; the function reference pages provide mathematically formal descriptions.
Function | Equation | Description |
---|---|---|
absorption() |
\(A = F R\) | Probability of an individual experiencing a specific type of mortality |
\(\psi^T A\) | Probability of an individual experiencing a specific type of mortality, regardless of initial location | |
cond_passage() |
\(\tilde{t} = \tilde{B}_j^{-1}\tilde{F}\tilde{B}_j{\cdot}1\) | Mean first conditional passage time |
dispersal() |
\(\tilde{D}_{jt}=({\sum}_{n=0}^{t-1}\tilde{Q}^n)\tilde{q}_j\) | Probability of an individual visiting a location, if starting at any other location, before or at time t |
\(\psi^T\tilde{D}_{jt}\) | Probability of an individual visiting a location, before or at time t, regardless of initial location | |
\(D=(F-I)diag(F)^{-1}\) | Probability of an individual visiting a location | |
\(\psi^TD\) | Probability of an individual visiting a location, regardless of initial location | |
distribution() |
\(Q^t\) | Probability of an individual being at a location at time t |
\(\psi^TQ^t\) | Probability of an individual being at a location at time t, regardless of initial location | |
mortality() |
\(\tilde{B}_t = (\sum_{n=0}^{t-1} Q^n) \tilde{R}\) | Probability of an individual experiencing mortality at a location before or at time t |
\(\psi^T \tilde{B}_t\) | Probability of an individual experiencing mortality at a location, before or at time t, regardless of initial location | |
\(B = F \tilde{R}\) | Probability of an individual experiencing mortality at a location | |
\(\psi^T B\) | Probability of an individual experiencing mortality at a location, regardless of initial location | |
survival() |
\(z=(I-Q)^{-1}{\cdot}1=F{\cdot}1\) | Expected life expectancy of an individual |
\({\psi}^Tz\) | Overall life expectancy, regardless of initial location | |
visitation() |
\(F = (I-Q)^{-1}\) | Expected number of times an individual visits a location |
Depending on the combination of inputs used, a function might return a single value, a vector, a matrix, or a list. In some cases, the calculations will be impractical with sufficiently large landscape datasets due to memory and other performance constraints. To work around this, many equations have multiple associated function signatures that allow users to calculate individual portions of the result rather than the entire result. This opens up multiple optimizations that makes calculating many of the metrics more practical. More specific details about performance considerations can be found in the Performance vignette.
Several of the analytical functions allow the input of an initial state \(\psi\) for the Markov chain via the occ
parameter. The descriptions for these analytical functions assume that values in \(\psi\) sum to one. When this is the case, \(\psi_i\) represents the probability that the Markov chain starts in transient state \(i\).
When the values in \(\psi\) sum to a value other than one, care must be taken in the interpretation of the results. For example, \(\psi\) could be used to represent a population of individuals where \(\psi_i\) represents the number of individuals that start in transient state \(i\). In this case, the results of the functions using \(\psi\) aren’t probabilities, but rather the expected number of individuals.
The package includes built-in example map data. Some of this data was used to create the figures in the SAMC paper, and is used in numerous package tutorials. They are:
ex_res_data
: A matrix with landscape resistance data.ex_abs_data
: A matrix with landscape absorption (mortality) data.ex_occ_data
: A matrix with landscape occupancy data.str(samc::ex_res_data)
str(samc::ex_abs_data)
str(samc::ex_occ_data)
plot(raster(samc::ex_res_data, xmn = 1, xmx = ncol(samc::ex_res_data), ymn = 1, ymx = nrow(samc::ex_res_data)),
main = "Example Resistance Data", xlab = "x", ylab = "y", col = viridis(256))
plot(raster(samc::ex_abs_data, xmn = 1, xmx = ncol(samc::ex_abs_data), ymn = 1, ymx = nrow(samc::ex_abs_data)),
main = "Example Absorption Data", xlab = "x", ylab = "y", col = viridis(256))
plot(raster(samc::ex_occ_data, xmn = 1, xmx = ncol(samc::ex_occ_data), ymn = 1, ymx = nrow(samc::ex_occ_data)),
main = "Example Occupancy Data", xlab = "x", ylab = "y", col = viridis(256))