spm2 (version 1.1.3)

svmcv: Cross validation, n-fold and leave-one-out for support vector machine ('svm')

Description

This function is a cross validation function for 'svm' regression in 'e1071' package.

Usage

svmcv(
  formula = NULL,
  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,
  validation = "CV",
  cv.fold = 10,
  predacc = "VEcv",
  ...
)

Value

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

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 modelling, need to 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).

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'.

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)
gravel <- petrel[, c(1, 2, 6:9, 5)]
model <- log(gravel + 1) ~ lat +  bathy + I(long^3) + I(lat^2) + I(lat^3)
set.seed(1234)
svmcv1 <- svmcv(formula = model, gravel, log(gravel[, 7] +1), validation = "CV",
 predacc = "ALL")
svmcv1

data(sponge2)
model <- species.richness ~ .
set.seed(1234)
svmcv1 <- svmcv(formula = model, sponge2[, -4], sponge[, 3], gamma = 0.01, cost = 3.5,
scale = TRUE, validation = "CV",  predacc = "VEcv")
svmcv1

# For svm
model <- species.richness ~ .
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
svmcv1 <- svmcv(formula = model, sponge2[, -4], sponge[, 3], gamma = 0.01, cost = 3.5,
scale = TRUE, validation = "CV",  predacc = "VEcv")
VEcv [i] <- svmcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for svm", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)
# }