assigninterp: assigninterp function

In bentaylor1/lgcp: Log-Gaussian Cox Process

Description

A function to assign an interpolation type to a variable in a data frame.

Usage

assigninterp(df, vars, value)

Arguments

df	a data frame
vars	character vector giving name of variables
value	an interpolation type, posssible options are given by typing interptypes(), see ?interptypes

Details

The three types of interpolation method employed in the package lgcp are:

1. 'Majority' The interpolated value corresponds to the value of the covariate occupying the largest area of the computational cell.

2. 'ArealWeightedMean' The interpolated value corresponds to the mean of all covariate values contributing to the computational cell weighted by their respective areas.

3. 'ArealWeightedSum' The interpolated value is the sum of all contributing covariates weighed by the proportion of area with respect to the covariate polygons. For example, suppose region A has the same area as a computational grid cell and has 500 inhabitants. If that region occupies half of a computational grid cell, then this interpolation type assigns 250 inhabitants from A to the computational grid cell.

Value

assigns an interpolation type to a variable

See Also

minimum.contrast, minimum.contrast.spatiotemporal, chooseCellwidth, getpolyol, guessinterp, getZmat, addTemporalCovariates, lgcpPrior, lgcpInits, CovFunction lgcpPredictSpatialPlusPars, lgcpPredictAggregateSpatialPlusPars, lgcpPredictSpatioTemporalPlusPars, lgcpPredictMultitypeSpatialPlusPars

Examples

## Not run: spdf a SpatialPolygonsDataFrame
## Not run: spdf@data <- assigninterp(df=spdf@data,vars="pop",value="ArealWeightedSum")

bentaylor1/lgcp documentation built on May 12, 2019, 2:09 p.m.