# Make predictions from a linear or generalized linear model to a GRaster This version of the `predict()` function make predictions to a set of `GRaster`s from a model object. The model must be either a linear model, which is of class `lm` and typically created using the `stats::lm()` function or a generalized linear model (GLM), which is class `glm` and typically created using `stats::glm()`. Other packages can also create `lm` or `glm` objects, but they may not work in this function. For example, generalized additive models, which can be created using the `gam()` function in the mgcv package, inherit the `glm` class, but cannot be used in this function. However, `glm` objects created with the speedglm package should work with this function. This `predict()` function can handle: * Linear predictors and intercepts like `1 + x`; * Quadratic terms like `x^2` (or, in R formula notation, `I(x^2)`); * Two-way interaction terms between scalars like `x1:x2` and `x1 * x2`; * Categorical predictors (i.e., categorical `GRaster`s; see `vignette("GRasters", package = "fasterRaster")`); * Two-way interactions between a categorical predictor and a scalar predictor; and * Two-way interactions between categorical predictors. ```r ## S4 method for signature 'GRaster' predict(object, model, type = "response") ``` ## Arguments - `object`: A `GRaster` with one or more layers. - `model`: An `lm` or `glm` model object. - `type`: Character: Type of prediction to make. This can be either `link` (default; predictions are made on the scale of the link function) or `response` (predictions are made on the scale of the response variable). This function can only make predictions on the scale of the response for the identity, logit, log, or cloglog (complementary log-log) link functions. ## Returns A `GRaster`. ## Examples ```r if (grassStarted()) { # Setup library(sf) library(terra) ### This example creates a simple model of Dypsis distribution using # elevation, distance to forest, land cover class, and nearness to rivers. # Elevation raster, forest cover in year 2000, land cover class, and # points where Dypsis plants have been collected madElev <- fastData("madElev") madForest2000 <- fastData("madForest2000") madCover <- fastData("madCover") madRivers <- fastData("madRivers") madDypsis <- fastData("madDypsis") # Convert SpatRasters to GRasters and sf vectors to GVectors: elev <- fast(madElev) forest <- fast(madForest2000) cover <- fast(madCover) rivers <- fast(madRivers) dypsis <- fast(madDypsis) # Distance to forest distToForest <- distance(forest, unit = "m") distToForest <- log1p(distToForest) # log(x + 1) of distance names(distToForest) <- "distToForest" # "Stack" elevation and forest cover continuous <- c(elev, distToForest) # Scale continuous predictors to mean of 0 and sd of 1 continuousScaled <- scale(continuous) names(continuousScaled) <- c("elevation", "distToForest") # Project land cover raster coverProj <- project(cover, continuousScaled) # Near a river? riverBuffer <- buffer(rivers, 5000) nearRiver <- rasterize(riverBuffer, elev, background = 0) names(nearRiver) <- "nearRiver" levels(nearRiver) <- data.frame(value = 0:1, label = c("far", "near")) # Combine continuous/categorical data covariateRasters <- c(continuousScaled, coverProj, nearRiver) plot(covariateRasters) # Extract environmental values at Dypsis locations: presEnv <- extract(covariateRasters, dypsis, cats = TRUE) presEnv$presBg <- 1 head(presEnv) # Extract elevation and forest cover at 2000 background sites: bgEnv <- spatSample(covariateRasters, size = 3000, values = TRUE, cats = TRUE) bgEnv <- bgEnv[stats::complete.cases(bgEnv), ] bgEnv <- bgEnv[1:2000, ] bgEnv$presBg <- 0 head(bgEnv) # Combine presence and background data: env <- rbind(presEnv, bgEnv) # Calibrate model: form <- presBg ~ elevation + distToForest + I(distToForest^2) + elevation * distToForest + madCover + nearRiver model <- stats::glm(form, data = env, family = stats::binomial) summary(model) # Make predictions and map: prediction <- predict(covariateRasters, model, type = "response") prediction # Not a great model! plot(prediction, main = "Predicted") plot(dypsis, pch = 1, add = TRUE) } ``` ## See Also `terra::predict()`; `stats::predict()`

predict function