stcov {stopp}

Create stcov objects and interpolate spatio-temporal covariates on a regular grid

Description

This function interpolates the covariate values observed at some observed sites to a regular grid. The imput object should be either a matrix or a dataframe with four columns: x, y, t, and the covariate values, named as the covariate later called in the model formula (see stppm). The interpolation is performed through Inverse Distance Weighting (IDW). See the Details.

Usage

stcov(
  x,
  interp = TRUE,
  nx = NULL,
  mult = 1,
  p = 81,
  names = NULL,
  verbose = FALSE
)

Arguments

x

A data.frame with four columns, containing the spatio-temporal coordinates and the covariate values.

interp

Logical value indicating whether to interpolate the covariate on a regular grid. Default to TRUE.

nx

Number of coordinates to generate for each dimention. The default is floor((mult * nrow(cov)) ^ (1/3)).

mult

The multiplicand of the number of points in the default for nx.

p

Power of IDW distances.

names

Factor string to name the covariate.

verbose

Default to FALSE. If TRUE, the elapsed minutes are printed.

Details

The function builds a regular grid with equispaced values along the three coordinates and interpolates the covariate values at the new locations. The interpolation at a point location x_k is performed through the inverse-distance weighting smoothing procedure of the covariate values Z(x_j) at their sampling locations j=1, \ldots, J. In such a case, the smoothed value at location x_k is

Z(x_k) = \frac{\sum_j w_j Z(x_j)}{\sum_j w_j},

where the weight w_j is the j-th element of the inverse pth powers of distance,

\textbf{w}=\{w_j\}_{j=1}^J=\{\frac{1}{d(x_k-x_j)^p}\}_{j=1}^J,

with

d(x_k-x_j) = ||x_k-x_j||

the Euclidean distance from x_k to x_j.

Value

A stpm object, to be imputed as list object in stppm.

Author(s)

Nicoletta D'Angelo

See Also

stppm

Examples

set.seed(2)
df <- data.frame(runif(100), runif(100), runif(100), rpois(100, 15))

cov <- stcov(df, interp = FALSE)