spm2 (version 1.1.3)

svmpred: Generate spatial predictions using support vector machine ('svm')

Description

This function is for generating spatial predictions using 'svm' method in 'e1071' package.

Usage

svmpred(
  formula = NULL,
  trainxy,
  longlatpredx,
  predx,
  scale = TRUE,
  type = NULL,
  kernel = "radial",
  degree = 3,
  gamma = if (is.vector(trainxy)) 1 else 1/ncol(trainxy),
  coef0 = 0,
  cost = 1,
  nu = 0.5,
  tolerance = 0.001,
  epsilon = 0.1,
  ...
)

Value

A dataframe of longitude, latitude and predictions.

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, need to be named as 'long' and 'lat'.

predx

a dataframe or matrix contains columns of predictive variables for the grids to be predicted.

scale

A logical vector indicating the variables to be scaled (default: TRUE).

type

the default setting is 'NULL'. See '?svm' for various options.

kernel

the default setting is 'radial'. See '?svm' for other options.

degree

a parameter needed for kernel of type polynomial (default: 3).

gamma

a parameter needed for all 'kernels' except 'linear' (default: 1/(data dimension)).

coef0

a parameter needed for kernels of type 'polynomial' and 'sigmoid'(default: 0).

cost

cost of constraints violation (default: 1).

nu

a parameter needed for 'nu-classification', 'nu-regression', and 'one-classification' (default: 0.5).

tolerance

tolerance of termination criterion (default: 0.001).

epsilon

'epsilon' in the insensitive-loss function (default: 0.1). See '?svm' for details.

...

other arguments passed on to 'svm'.

Author

Jin Li

References

David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel and Friedrich Leisch (2020). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-4. https://CRAN.R-project.org/package=e1071.

Examples

# \donttest{
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)

svmpred1 <- svmpred(formula = model, trainxy = gravel,
longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid)

names(svmpred1)

# Back transform 'svmpred1$pred.svm1' to generate the final predictions
svm.predictions <- exp(svmpred1$pred.svm1) - 1
range(svm.predictions)
# }