spm2 (version 1.1.3)

glmnetcv: Cross validation, n-fold and leave-one-out, for 'glmnet' in 'glmnet' package

Description

This function is a cross validation function for 'glmnet' method in 'glmnet' package.

Usage

glmnetcv(
  trainx,
  y,
  family = "gaussian",
  alpha = 0.5,
  relax = FALSE,
  type.measure = "mse",
  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

trainx

a matrix contains predictive variables of point samples. The location information, longitude (long), latitude (lat), need to be included in the 'trainx' for spatial predictive modelling.

y

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

family

a description of the error distribution and link function to be used in the model. See '?glmnet' for details.

alpha,

an elasticnet mixing parameter, with $0 <= alpha <= 1$. See '?glmnet' for details.

relax,

if TRUE then for each active set in the path of solutions, the model is refit without any regularization. See '?glmnet' for more information.

type.measure,

loss to use for cross-validation. The default is type.measure="mse". See '?cv.glmnet' for more information.

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 'fields'.

Author

Jin Li

References

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Examples

# \donttest{
library(spm)

data(petrel)
x <- as.matrix(petrel[, c(1, 2, 6:9)])
y <- log(petrel[, 5] + 1)
set.seed(1234)
glmnetcv1 <- glmnetcv(x, y, validation = "CV",  predacc = "ALL")
glmnetcv1

data(sponge)
x <- as.matrix(cbind(sponge$easting, sponge$easting^2))
set.seed(1234)
glmnetcv1 <- glmnetcv(x, sponge[, 3], family = poisson, validation = "CV",
predacc = "ALL")
glmnetcv1

# For glmnet with gaussian
x <- as.matrix(petrel[, c(1, 2, 6:9)])
y <- log(petrel[, 5] + 1)
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
  glmnetcv1 <- glmnetcv(x, y, validation = "CV", predacc = "VEcv")
  VEcv [i] <- glmnetcv1
 }
plot(VEcv ~ c(1:n), xlab = "Iteration for glmnet", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

# For glmnet with binomial
x <- as.matrix(cbind(petrel[, c(2, 6)], petrel$long^3, petrel$lat^2, petrel$lat^3))
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
glmnetcv1 <- glmnetcv(x, petrel[, 5] / 100, family = binomial(link=logit),
validation = "CV", predacc = "VEcv")
VEcv [i] <- glmnetcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for glmnet", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)
# }