glmpred: Generate spatial predictions using generalised linear models...

In spm2: Spatial Predictive Modeling

Generate spatial predictions using generalised linear models ('glm')

Description

This function is for generating spatial predictions using 'glm' method in 'stats' package.

Usage

glmpred(formula = NULL, trainxy, longlatpredx, predx, family = "gaussian", ...)

Arguments

formula	a formula defining the response variable and predictive variables.
trainxy	a dataframe contains predictive variables and the response variable of point samples. The location information, longitude (long), latitude (lat), need to be included in the 'trainx' for spatial predictive modeling, need to be named as 'long' and 'lat'.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centers of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
family	a description of the error distribution and link function to be used in the model. See '?glm' for details.
...	other arguments passed on to 'glm'.

Value

A dataframe of longitude, latitude and predictions.

Author(s)

Jin Li

Examples

library(spm)
data(petrel)
data(petrel.grid)

gravel <- petrel[, c(1, 2, 6:9, 5)]
model <- log(gravel + 1) ~  lat +  bathy + I(long^3) + I(lat^2) + I(lat^3)

glmpred1 <- glmpred(formula = model, trainxy = gravel,
longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid)

names(glmpred1)

# Back transform 'glmpred1$pred.glm1' to generate the final predictions
glm.predictions <- exp(glmpred1$pred.glm1) - 1
range(glm.predictions)

spm2 documentation built on April 6, 2023, 5:19 p.m.