glscv {spm2}

Cross validation, n-fold and leave-one-out for generalized least squares ('gls')

Description

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

Usage

glscv(
  model = var1 ~ 1,
  trainxy,
  y,
  corr.args = NULL,
  weights = NULL,
  validation = "CV",
  cv.fold = 10,
  predacc = "VEcv",
  ...
)

Arguments

model

a formula defining the response variable and predictive variables.

trainxy

a dataframe contains longitude (long), latitude (lat), predictive variables and the response variable of point samples. That is, the location information must be names as 'long' and 'lat'.

y

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

corr.args

arguments for 'correlation' in 'gls'. See '?corClasses' in 'nlme' for details. By default, "NULL" is used. When "NULL" is used, then 'gls' is actually performing 'lm'.

weights

describing the within-group heteroscedasticity structure. Defaults to "NULL", corresponding to homoscedastic errors. See '?gls' in 'nlme' 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 'gls'.

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 'gls' in 'library(nlme)'.

Author(s)

Jin Li

References

Pinheiro, J. C. and D. M. Bates (2000). Mixed-Effects Models in S and S-PLUS. New York, Springer.

Examples

library(spm)
library(nlme)

data(petrel)
gravel <- petrel[, c(1, 2, 6:9, 5)]
range1 <- 0.8
nugget1 <- 0.5

model <- log(gravel + 1) ~  long + lat +  bathy + dist + I(long^2) + I(lat^2) +
I(lat^3) + I(bathy^2) + I(bathy^3) + I(dist^2) + I(dist^3) + I(relief^2) + I(relief^3)


glscv1 <- glscv(model = model, gravel, log(gravel[, 7] +1), validation = "CV",
 corr.args = corSpher(c(range1, nugget1), form = ~ long + lat, nugget = TRUE),
 predacc = "ALL")
glscv1

#For gls
set.seed(1234)
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
glscv1 <- glscv(model = model, gravel, log(gravel[, 7] +1), validation = "CV",
          corr.args = corSpher(c(range1, nugget1), form = ~ long + lat,
          nugget = TRUE), predacc = "VEcv")
VEcv [i] <- glscv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for GLS", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

# For lm, that is, gls with 'correlation = NULL'
n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
set.seed(1234)
for (i in 1:n) {
glscv1 <- glscv(model = model, gravel, log(gravel[, 7] +1),
validation = "CV", predacc = "VEcv")
VEcv [i] <- glscv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for GLS", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)