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disaggregation (version 0.2.1)

Disaggregation Modelling

Description

Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) .

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Version

Install

install.packages('disaggregation')

Monthly Downloads

247

Version

0.2.1

License

MIT + file LICENSE

Maintainer

Tim Lucas

Last Published

September 7th, 2023

Functions in disaggregation (0.2.1)

predict.disag_model

Predict mean and uncertainty from the disaggregation model result
summary.disag_data

Summary function for disaggregation input data
plot.disag_model

Plot results of fitted model
summary.disag_model

Summary function for disaggregation fit result
summary.disag_prediction

Summary function for disaggregation prediction
disaggregation-deprecated

Deprecated functions in disaggregation
build_mesh

Build mesh for disaggregaton model
getStartendindex

Function to match pixels to their corresponding polygon
getPolygonData

Extract polygon id and response data into a data.frame from a SpatialPolygonsDataFrame
getCovariateRasters

Get a RasterStack of covariates from a folder containing .tif files
dummy

Roxygen commands
prepare_data

Prepare data for disaggregation modelling
make_model_object

Create the TMB model object for the disaggregation model
as.disag_data

Function to fit the disaggregation model
fit_model

Fit the disaggregation model
parallelExtract

Parallel extraction of raster stack by shape file.
plot.disag_prediction

Plot mean and uncertainty predictions from the disaggregation model results
plot.disag_data

Plot input data for disaggregation
print.disag_model

Print function for disaggregation fit result.
print.disag_data

Print function for disaggregation input data
print.disag_prediction

Print function for disaggregation prediction
predict_model

Function to predict mean from the model result
predict_uncertainty

Function to predict uncertainty from the model result