spm2 (version 1.1.3)

glmidwpred: Generate spatial predictions using the hybrid method of generalised linear models ('glm') and inverse distance weighted ('IDW') ('glmidw')

Description

This function is for generating spatial predictions using the hybrid method of 'glm' and 'idw' ('glmidw') (see reference #1).

Usage

glmidwpred(
  formula = NULL,
  longlat,
  trainxy,
  y,
  longlatpredx,
  predx,
  family = "gaussian",
  idp = 2,
  nmaxidw = 12,
  ...
)

Value

A dataframe of longitude, latitude, and predictions.

Arguments

formula

a formula defining the response variable and predictive variables for 'glm'.

longlat

a dataframe contains longitude and latitude of point samples. The location information must be named as 'long' and 'lat'.

trainxy

a dataframe contains longitude (long), latitude (lat), predictive variables and the response variable of point samples. That is, the location information must be named as 'long' and 'lat'.

y

a vector of the response variable in the formula, that is, the left part of the formula.

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.

idp

a numeric number specifying the inverse distance weighting power.

nmaxidw

for a local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.

...

other arguments passed on to 'glm'.

Author

Jin Li

References

Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F. and Nichol, S. (2017). "Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness." Environmental Modelling & Software 97: 112-129.

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

# \donttest{
library(spm)
data(petrel)
data(petrel.grid)

gravel <- petrel[, c(1, 2, 6:9, 5)]
longlat <- petrel[, c(1, 2)]
model <- log(gravel + 1) ~  lat +  bathy + I(long^3) + I(lat^2) + I(lat^3)
y <- log(gravel[, 7] +1)

glmidwpred1 <- glmidwpred(formula = model, longlat = longlat, trainxy =  gravel,
y = y, longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid, idp = 2,
 nmaxidw = 12)
 # Since the default 'family' is used, actually a 'lm' model is used.

names(glmidwpred1)

# Back transform 'glmidwpred$predictions' to generate the final predictions
glmidwpred1$predictions.bt <- exp(glmidwpred1$predictions) - 1
range(glmidwpred1$predictions.bt)
# }