prep_MRF_covariates_spatial {MRFcov}

Cross-multiply response and covariate variables and build spatial splines

Description

This function performs the cross-multiplication necessary for prepping datasets to be used in MRFcov_spatial models.

Usage

prep_MRF_covariates_spatial(data, n_nodes, coords)

Arguments

data

Dataframe. The input data where the n_nodes left-most variables are outcome variables to be represented by nodes in the graph

n_nodes

Integer. The index of the last column in data which is represented by a node in the final graph. Columns with index greater than n_nodes are taken as covariates. Default is the number of columns in data, corresponding to no additional covariates

coords

A two-column dataframe (with nrow(coords) == nrow(data)) representing the spatial coordinates of each observation in data. Ideally, these coordinates will represent Latitude and Longitude GPS points for each observation. The coordinates are used to create smoothed Gaussian Process spatial regression splines via smooth.construct2. Here, the basis dimension of the smoothed term is chosen based on the number of unique GPS coordinates in coords. If this number is less than 100, then this number is used. If the number of unique coordiantes is more than 100, a value of 100 is used (this parameter needs to be large in order to ensure enough degrees of freedom for estimating 'wiggliness' of the smooth term; see choose.k for details).

Details

Observations of nodes (species) in data are prepped for MRFcov_spatial analysis by multiplication. This function is useful if users wish to prep the spatial splines beforehand and split the data manually for out-of-sample cross-validation. To do so, prep the splines here and set prep_splines = FALSE in MRFcov_spatial

Value

Dataframe of the prepped response and covariate variables necessary for input in MRFcov_spatial models