tnkdecontinuousfunction: The main function to calculate continuous TNKDE (with...

In spNetwork: Spatial Analysis on Network

The main function to calculate continuous TNKDE (with ARMADILO and sparse matrix)

Description

The main function to calculate continuous TNKDE (with ARMADILO and sparse matrix)

The main function to calculate continuous TNKDE (with ARMADILO and integer matrix)

Usage

continuous_tnkde_cpp_arma_sparse(
  neighbour_list,
  events,
  events_time,
  weights,
  samples,
  samples_time,
  bws_net,
  bws_time,
  kernel_name,
  nodes,
  line_list,
  max_depth,
  verbose,
  div
)

continuous_tnkde_cpp_arma(
  neighbour_list,
  events,
  events_time,
  weights,
  samples,
  samples_time,
  bws_net,
  bws_time,
  kernel_name,
  nodes,
  line_list,
  max_depth,
  verbose,
  div
)

Arguments

neighbour_list	a list of the neighbours of each node
events	a numeric vector of the node id of each event
events_time	a numeric vector with the time for the events
weights	a numeric vector of the weight of each event
samples	a DataFrame of the samples (with spatial coordinates and belonging edge)
samples_time	a NumericVector indicating when to do the samples
bws_net	the network kernel bandwidths for each event
bws_time	the time kernel bandwidths for each event
kernel_name	the name of the kernel 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	a string indicating how to standardize the kernel values

Value

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

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

spNetwork documentation built on June 22, 2024, 9:40 a.m.