Package index • oneimpact

Zone of influence (ZoI) functions

Functions to represent the zone of influence

dist_decay() threshold_decay() step_decay() bartlett_decay() tent_decay() linear_decay() gaussian_decay() half_norm_decay() exp_decay(): Zone of Influence (ZOI) functions

plot_zoi1d(): Plot the functions for the nearest and cumulative zone of influence in 1 dimension

Compute zones of influence (ZoI)

Functions to compute the zone of influence for raster maps

calc_zoi(): Calculates the zone of influence from the nearest feature and the cumulative zone of influence of multiple features

calc_zoi_cumulative(): Calculate the cumulative zone of influence of multiple features

calc_zoi_nearest(): Calculate the zone of influence from the nearest feature

calc_zoi_sql(): Compute Zone of Influence for points and annotate them to points using SQL

Spatial filters

Creating spatial filters to compute a meaningful zone of influence

filter_create(): Create filters or kernel matrices for raster neighborhood analyses

filter_na_strata(): Remove missing values and ensure case-control per stratum

filter_save(): Save kernel/filter matrix to use outside R

Estimating ZOI - set up analysis

Functions to set up RSF and SSF analyses using ZOI variables

add_zoi_formula(): Adds ZOI radii to formula

add_zoi_layers(): Create ZOI layer names as strings for data annotation

explore_blocks(): Explore hierarchical blocks after sampling or spatial stratification

explore_blocks_pre(): Explore potential hierarchical blocks before sampling or spatial stratification

spat_strat(): Preparing data for spatially stratified cross‐validation schemes

create_resamples(): Create samples for fitting, calibrating, and validating models

Estimating ZOI - fit models

Functions to fit RSF and SSF and estimate ZOI using penalized regression

filter_na_strata(): Remove missing values and ensure case-control per stratum

extract_response_strata(): Separates elements in a statistical formula

net_clogit() net_ssf() net_issf(): Fits a conditional logistic regression/SSF/iSSF using glmnet

net_logit() net_rsf(): Fits a logistic regression/RSF using glmnet

fit_net_clogit() fit_net_ssf() fit_net_issf() fit_net_logit() fit_net_rsf(): Fits a conditional logistic regression/SSF/iSSF with penalized regression using glmnet in a train-validate-test setup

bag_fit_net_clogit() bag_fit_net_logit(): Fit a bag of conditional logistic regression/SSF/iSSF models with penalized regression in a train-validate-test setup

bag_load_models(): Load a series of files output of fit_net_clogit models and put them on a bag

bag_models() w_strech_maxmin_squared() w_strech_max_squared() score2weight_mean() score2weight_min_mean() score2weight_invmean() score2weight_min_invmean(): Summary of a bag of models

conditionalBoyce() conditionalSomersD() conditionalAUC() AUC() coxnet.deviance() Cindex(): Computes the conditional Boyce index for model evaluation

kernel_prediction(): Prediction based only on step length terms

pretty_seq(): Set sampling parameters

Estimating ZOI - interpret and visualize models

Functions to help interpreting parameters and visualizing cumulative impacts

predict(): Prediction of a bag of models to new data

plot_importance(): Plot variable importance

variable_importance(): Computes variable importance

plot_coef(): Plot the coefficients of bags of models

plot_response(): Plot responses from a bag of models

bag_predict_spat() bag_predict_spat_vars(): Predict bag of models in space

rescale_coefficients(): Rescale standardized coefficients back to their original range after model fitting

create_linear_feature_zoi(): Creates a line feature and gets the value of their zone of influence on locations over the feature

Raster processing ancillary functions

GRASS GIS ancillary functions to process rasters and prepare inputs for computing zones of influence

grass_binarize(): Binarize continuous raster maps

grass_find_layer(): Find layers within GRASS GIS with multiple patterns

grass_v2rast_count(): Rasterizes a vector counting the number of features in each pixel

raster_rescale(): Rescale raster values

Support for landscape simulation

set_points(): Simulate points in a landscape

set_points_from_raster(): Simulate points using input raster as weights

set_points_sample(): Simulate regular or random points in 2D

isolation() mean_isolation(): Isolation and mean isolation of points in space

Datasets

Datasets for testing the ZoI approach

reindeer: GPS positions for wild reindeer in Norway.

reindeer_area.gpkg: Reindeer area: a polygon vector data for the Setesdal Austhei reindeer herding area

reindeer_cabins.gpkg: Cabins vector data for the reindeer area

reindeer_roads_private.gpkg: Private roads vector data for the reindeer area

reindeer_roads_public.gpkg: Public roads vector data for the reindeer area

reindeer_rsf: Annotated data of wild reindeer in Norway, prepared for point resource-selection analysis.

reindeer_ssf: Annotated data of wild reindeer in Norway, prepared for step-selection analysis.

rast_predictors_hardanger_500.tif: Predictor environmental variables for the Hardangervidda wild reindeer area in Norway

sample_area_cabins.tif: Cabin presence raster data for the sample area

sample_area_cabins_count.tif: Cabin count raster data for the sample area

sample_area_roads.tif: Road raster data for the sample area

sample_area.gpkg: Sample area: a polygon vector data

sample_area_cabins.gpkg: Cabins vector data for the sample area

sample_area_roads.gpkg: Road vector data for the sample area