svmidwpred {spm2}

Generate spatial predictions using the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') ('svmidw')

Description

This function is for generating spatial predictions using the hybrid method of 'svm' and 'idw' ('svmidw').

Usage

svmidwpred(
  formula = NULL,
  longlat,
  trainxy,
  y,
  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,
  idp = 2,
  nmaxidw = 12,
  ...
)

Arguments

formula

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

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.

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.

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.

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 'svm'.

Value

A dataframe of longitude, latitude, and predictions.

Author(s)

Jin Li

References

Li, J., Potter, A., Huang, Z., and Heap, A. (2012). Predicting Seabed Sand Content across the Australian Margin Using Machine Learning and Geostatistical Methods, Geoscience Australia, Record 2012/48, 115pp.

Li, J., Heap, A., Potter, A., and Danilel, J.J. (2011). Predicting Seabed Mud Content across the Australian Margin II: Performance of Machine Learning Methods and Their Combination with Ordinary Kriging and Inverse Distance Squared, Geoscience Australia, Record 2011/07, 69pp.

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.

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

Examples

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)

svmidwpred1 <- svmidwpred(formula = model, longlat = longlat, trainxy =  gravel,
y = y, longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid, idp = 2,
 nmaxidw = 12)

names(svmidwpred1)

# Back transform 'svmidwpred$predictions' to generate the final predictions
svmidw.predictions <- exp(svmidwpred1$predictions) - 1
range(svmidw.predictions)