Function reference • MetricGraph

MetricGraph package

MetricGraph MetricGraph-package: Gaussian processes on metric graphs

logo_lines(): Create lines for package name

Metric graph constructor

metric_graph: Metric graph

graph_components: Connected components of metric graph

INLA and SPDE approach on metric graphs

graph_spde(): 'INLA' implementation of Whittle-Matérn fields for metric graphs

spde_metric_graph_result(): Metric graph SPDE result extraction from 'INLA' estimation results

summary(<metric_graph_spde_result>): Summary for posteriors of field parameters for an inla_rspde model from a rspde.result object

gg_df(<metric_graph_spde_result>): Data frame for metric_graph_spde_result objects to be used in 'ggplot2'

graph_data_spde(): Data extraction for 'spde' models

graph_spde_basis(): Observation/prediction matrices for 'SPDE' models

graph_spde_make_A(): Deprecated - Observation/prediction matrices for 'SPDE' models

inlabru and SPDE approach on metric graphs

bru_get_mapper.inla_metric_graph_spde() ibm_n.bru_mapper_inla_metric_graph_spde() ibm_values.bru_mapper_inla_metric_graph_spde() ibm_jacobian.bru_mapper_inla_metric_graph_spde(): Metric graph 'inlabru' mapper

predict(<inla_metric_graph_spde>): Predict method for 'inlabru' fits on Metric Graphs

predict(<rspde_metric_graph>): Predict method for 'inlabru' fits on Metric Graphs for 'rSPDE' models

plot(<graph_bru_pred>): Plot of predicted values with 'inlabru'

graph_bru_process_data(): Prepare data frames or data lists to be used with 'inlabru' in metric graphs

Linear mixed-effects models

graph_lme(): Metric graph linear mixed effects models

predict(<graph_lme>): Prediction for a mixed effects regression model on a metric graph

simulate(<graph_lme>): Simulation of models on metric graphs

summary(<graph_lme>): Summary Method for graph_lme Objects

posterior_crossvalidation(): Leave-one-out crossvalidation for graph_lme models assuming observations at the vertices of metric graphs

graph_starting_values(): Starting values for random field models on metric graphs

glance(<graph_lme>): Glance at a graph_lme object

augment(<graph_lme>): Augment data with information from a graph_lme object

Log-Cox Gaussian processes

graph_lgcp(): Simulation of log-Gaussian Cox processes driven by Whittle-Matérn fields on metric graphs

Space-time models

simulate_spacetime(): space-time simulation based on implicit Euler discretization in time

make_Q_euler(): Space-time precision operator Euler discretization

make_Q_spacetime(): Space-time precision operator discretization

Sampling SPDE on metric graphs

sample_spde(): Samples a Whittle-Matérn field on a metric graph

Data manipulation on metric graphs

select(<metric_graph_data>): A version of dplyr::select() function for datasets on metric graphs

filter(<metric_graph_data>): A version of dplyr::filter() function for datasets on metric graphs

mutate(<metric_graph_data>): A version of dplyr::mutate() function for datasets on metric graphs

summarise(<metric_graph_data>): A version of dplyr::summarise() function for datasets on metric graphs

drop_na(<metric_graph_data>): A version of tidyr::drop_na() function for datasets on metric graphs

Methods for metric graphs

summary(<metric_graph>): Summary Method for metric_graph Objects

Precision matrices

spde_precision(): Precision matrix for Whittle-Matérn fields

Covariance functions

exp_covariance(): Exponential covariance function

spde_covariance(): Covariance function for Whittle-Matérn fields

Datasets

pems: Traffic speed data from San Jose, California