tnkdecontinuousfunction {spNetwork} | R Documentation |
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)