# discontinuousfunction: The main function to calculate discontinuous NKDE (ARMA and... In spNetwork: Spatial Analysis on Network

 discontinuousfunction R Documentation

## The main function to calculate discontinuous NKDE (ARMA and sparse matrix)

### Description

The main function to calculate discontinuous NKDE (ARMA and sparse matrix)

The main function to calculate discontinuous NKDE (ARMA and Integer matrix)

### Usage

``````discontinuous_nkde_cpp_arma_sparse(
neighbour_list,
events,
weights,
samples,
bws,
kernel_name,
nodes,
line_list,
max_depth,
verbose,
div = "bw"
)

discontinuous_nkde_cpp_arma(
neighbour_list,
events,
weights,
samples,
bws,
kernel_name,
nodes,
line_list,
max_depth,
verbose,
div = "bw"
)
``````

### Arguments

 `neighbour_list` a list of the neighbours of each node `events` a numeric vector of the node id of each event `weights` a numeric vector of the weight of each event `samples` a DataFrame of the samples (with spatial coordinates and belonging edge) `bws` the kernel bandwidth for each event `kernel_name` the name of the kernel function to use `nodes` a DataFrame representing the nodes of the graph (with spatial coordinates) `line_list` a DataFrame representing the lines of the graph `max_depth` the maximum recursion depth (after which recursion is stopped) `verbose` a boolean indicating if the function must print its progress `div` The divisor to use for the kernel. Must be "n" (the number of events within the radius around each sampling point), "bw" (the bandwidth) "none" (the simple sum).

### Value

a DataFrame with two columns : the kernel values (sum_k) and the number of events for each sample (n)

a DataFrame with two columns : the kernel values (sum_k) and the number of events for each sample (n)

spNetwork documentation built on May 29, 2024, 10:18 a.m.