glmcv: Cross validation, n-fold and leave-one-out for generalised...

In spm2: Spatial Predictive Modeling

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",
  ...
)

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'.

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' and 'glm' in 'stats'.

Author(s)

Jin Li

References

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

Examples

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)

spm2 documentation built on April 6, 2023, 5:19 p.m.