spm2 (version 1.1.3)

glmcv: Cross validation, n-fold and leave-one-out for generalised linear models ('glm')

Description

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

Usage

glmcv(
  formula = NULL,
  trainxy,
  y,
  family = "gaussian",
  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 modeling.

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 '?glm' for details.

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 'glm'.

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)
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)
glmcv1 <- glmcv(formula = model, gravel, log(gravel[, 7] +1), validation = "CV",
 predacc = "ALL")
glmcv1 # Since the default 'family' is used, it is actually a 'lm' model.

data(sponge)
model <- sponge ~ easting + I(easting^2)
set.seed(1234)
glmcv1 <- glmcv(formula = model, sponge, sponge[, 3], family = poisson,
validation = "CV",  predacc = "ALL")
glmcv1

# For glm
model <- gravel / 100 ~  lat +  bathy + I(long^3) + I(lat^2) + I(lat^3)
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
glmcv1 <- glmcv(formula = model, gravel, gravel[, 7] / 100, family =
binomial(link=logit), validation = "CV", predacc = "VEcv")
VEcv [i] <- glmcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for GLM", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)
# }