Description Usage Arguments Value Examples
This auxiliary function helps finding tuning parameters (lambda, zeta) for the B-spline tensor product spatial deformation. Execution might be slow.
1 2 3 |
model |
an object from the bdef function. |
grid.lambda |
Grid of values for the penalty parameter lambda. |
grid.zeta |
Grid of values for the penalty parameter zeta. |
verbose |
Verbose execution informs user of the LOOCV progress. Defaults to FALSE. |
a data frame with the selected grid, and the leave-one-out cross validation error
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | # Example using artificially generated data
## Not run:
set.seed(1)
m <- 10
x1 <- (0:m)/m
x2 <- (0:m)/m
x <- as.matrix(expand.grid(x1,x2))
n <- nrow(x)
F1 <- function(x1,x2, a = 2.5, b = 1.0) {
x <- x1 - 0.5; y <- x2 - 0.5
angle <- a*exp(-(x*x+y*y)/(b*b)) + 3*pi/2
return(cos(angle)*x + sin(angle)*y + 0.5)
}
F2 <- function(x1,x2, a = 2.5, b = 1.0) {
x <- x1 - 0.5; y <- x2 - 0.5
angle <- a*exp(-(x*x+y*y)/(b*b)) + 3*pi/2
return(-sin(angle)*x + cos(angle)*y + 0.5)
}
TIME <- 20
covModel <- RMexp(var = 1, scale = .25, proj = "space") + RMnugget(var = 1) # Independent in time
data <- RFsimulate(covModel, x = F1(x[,1],x[,2]), y = F2(x[,1],x[,2]),
T = seq(from = 1, by = 1, len = TIME)) # order ~ expand.grid(x, y, T)
y <- as.numeric(unlist(data@data))
# Model for spatial dependence, time is assumed independent
covModelM <- RMexp(var = NA, scale = NA) + RMnugget(var = NA)
# Calculates deformation, profle likelihood up to maxit times
test.def <- bdef(x, y, tim = 1:TIME, cov.model = covModelM, maxit = 10)
tuningChoice <- findTuning(test.def, verbose = TRUE)
require(ggplot2)
require(dplyr)
tuningChoice %>%
mutate(zeta = factor(zeta)) %>%
mutate(lambda = log(lambda)) %>%
ggplot(aes(x = lambda, y = LOOCV, color = zeta)) +
geom_point() +
geom_line(aes(group = zeta))
## End(Not run)
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