svmidwcv {spm2}

Cross validation, n-fold and leave-one-out for the hybrid method of support vector machine ('svm') regression and inverse distance weighted ('IDW') (svmidw)

Description

This function is a cross validation function for the hybrid method of 'svm' regression and 'idw' using 'gstat' (svmidw), where the data splitting is based on a stratified random sampling method (see the 'datasplit' function for details).

Usage

svmidwcv(
  formula = NULL,
  longlat,
  trainxy,
  y,
  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,
  validation = "CV",
  cv.fold = 10,
  predacc = "VEcv",
  ...
)

Arguments

formula

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

longlat

a dataframe contains longitude and latitude of point samples.

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.

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).

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.

validation

validation methods, include 'LOO': leave-one-out, and 'CV': cross-validation.

cv.fold

integer; number of folds in the cross-validation. if > 1, then apply n-fold cross validation; the default is 10, i.e., 10-fold cross validation that is recommended.

predacc

can be either "VEcv" for 'vecv' or "ALL" for all measures in function pred.acc.

...

other arguments passed on to 'svm' and 'gstat'.

Value

A list with the following components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv only.

Note

This function is largely based on 'rfcv' in 'randomForest', 'idwcv' in 'spm'and 'svm' in 'e1071'.

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)
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)

set.seed(1234)
svmidwcv1 <- svmidwcv(formula = model, longlat = longlat, trainxy =  gravel,
y = y, idp = 2, nmaxidw = 12, validation = "CV", predacc = "ALL")
svmidwcv1

# svmidw for count data
data(sponge2)
model <- species.richness ~ . # use all predictive variables in the dataset
longlat <- sponge2[, 1:2]
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
 svmidwcv1 <- svmidwcv(formula = model, longlat = longlat, trainxy = sponge2[, -4],
 y = sponge[, 3], gamma = 0.01,  cost = 3.5, scale = TRUE, idp = 2, nmaxidw = 12,
 validation = "CV", predacc = "VEcv")
 VEcv [i] <- svmidwcv1
 }
 plot(VEcv ~ c(1:n), xlab = "Iteration for svmidw", ylab = "VEcv (%)")
 points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
 abline(h = mean(VEcv), col = 'blue', lwd = 2)