terra (version 1.9-50)

regress: Cell level regression

Description

Run a regression model for each cell of a SpatRaster. The independent variable can be defined either by:

• a numeric vector of length nlyr(y) -- one covariate that varies across the layers but is constant across cells (e.g. time);

• a SpatRaster of the same number of layers as y -- one covariate that varies both across layers and across cells;

• a data.frame with one row per layer of y and one column per predictor -- multiple covariates that vary across layers but are constant across cells (e.g. an experimental design like NPK fertilizer levels).

Usage

# S4 method for SpatRaster,numeric
regress(y, x, formula=y~x, na.rm=FALSE, cores=1, filename="", overwrite=FALSE, ...)
# S4 method for SpatRaster,SpatRaster
regress(y, x, formula=y~x, na.rm=FALSE, cores=1, filename="", overwrite=FALSE, ...)
# S4 method for SpatRaster,data.frame
regress(y, x, formula=NULL, na.rm=FALSE, cores=1, filename="", overwrite=FALSE, ...)

Value

SpatRaster. One layer per regression coefficient, named after the corresponding column of model.matrix(formula, x) (e.g. (Intercept), the names of the predictors, and any interaction or polynomial terms).

Examples

s <- rast(system.file("ex/logo.tif", package="terra"))
x <- regress(s, 1:nlyr(s))

# data.frame method: a small NPK fertilizer experiment.
# Suppose `y` has one layer per (N, P, K) treatment.
if (FALSE) {
NPK <- data.frame(
    N = c(0, 0, 0, 50, 50, 50, 100, 100, 100),
    P = c(0, 25, 50, 0, 25, 50, 0, 25, 50),
    K = c(0, 0, 0, 10, 10, 10, 20, 20, 20)
)
y <- rast(ncol=2, nrow=2, nlyr=nrow(NPK))
values(y) <- rep(1:nrow(NPK) * 200, each=4)

# Default formula y ~ N + P + K:
fit <- regress(y, NPK)

# Custom formula with interactions and a quadratic term:
fit2 <- regress(y, NPK, formula = yield ~ N + P + K + I(N^2) + N:P)
}