pnacf {GNAR} | R Documentation |
Computes the Partial Network Autocorrelation Function (PNACF)
Description
Computes the PNACF for a choice of lag h
and r-stage depth r
, the PNACF is given by
\mathrm{pnacf}(h, r) = \frac{\sum_{t=1}^{T - h} ( \boldsymbol{\hat{u}}_{t + h} - \boldsymbol{\overline{u}})^{T} \big ( \mathbf{W} \odot \mathbf{S}_r + \mathbf{I_d} \big )
( \boldsymbol{\hat{u}}_{t} - \boldsymbol{\overline{u}})}
{\sum_{t=1}^{T} ( \boldsymbol{\hat{u}}_{t} - \boldsymbol{\overline{u}})^{T} \big \{ \big (1 + \lambda \big) \mathbf{I_d} \big \} ( \boldsymbol{\hat{u}}_{t} - \boldsymbol{\overline{u}})},
where \hat{\boldsymbol{X}}_{t}^{h - 1, r - 1} = \sum_{k = 1}^{h - 1} ( \hat{\alpha}_k \boldsymbol{X}_{t - k} + \sum_{s = 1}^{r - 1} \hat{\beta}_{ks} \boldsymbol{Z}_{t - k}^{s} )
, \boldsymbol{\hat{u}}_{t + h} = \boldsymbol{X}_{t + h} - \hat{\boldsymbol{X}}_{t + h}^{h - 1, r - 1}
, and \boldsymbol{\hat{u}}_{t} = \boldsymbol{X}_{t} - \hat{\boldsymbol{X}}_{t}^{h - 1, r - 1}
are the empirical residuals corresponding to GNAR(h -1, [r-1, ..., r - 1]) fits, \lambda
is the same as for the NACF; see nacf
, and \boldsymbol{\overline{u}}
is the mean of the fitted residuals
Usage
pnacf(h, s, weight_matrix, stages_tensor, nts_data)
Arguments
h |
Lag (i.e., time-steps behind) at which the NACF is computed. |
s |
r-stage depth at which the NACF is computed (i.e., shortest distance between nodes). |
weight_matrix |
Weight matrix of the GNAR process, each entry corresponds to the weight between two nodes; see |
stages_tensor |
List of r-stage adjacency matrices |
nts_data |
Network time series observations, the number of rows is equal to the number of time steps, and the number of columns is equal to the number of series (variables). |
Value
If the network time series contains missing values, then the weights matrix and \lambda
are adjusted, so that missing values do not contribute to the partial network autocorrelation. This is done by setting to zero the weights which correspond to a missing value and computing the new weight matrix and \lambda
value.
Author(s)
Daniel Salnikov and Guy Nason
References
Nason, G.P., Salnikov, D. and Cortina-Borja, M. (2023) New tools for network time series with an application to COVID-19 hospitalisations. https://arxiv.org/abs/2312.00530
Examples
#
# Compute the PNACF with respect to a stationary GNAR simulation
#
gnar_simulation <- GNARsim(n = 100, net=fiveNet, alphaParams = list(rep(0.35, 5)),
betaParams = list(c(0.25)), sigma=1)
W = weights_matrix(fiveNet)
stages_list = get_k_stages_adjacency_tensor(as.matrix(GNARtoigraph(fiveNet)), 3)
# PNACF
pnacf(3, 1, W, stages_list, gnar_simulation)