Hi, this is emanuele barca and I would like to learn the library
varycoef.
I created a simulation of an ideal spatial dataset (gaussian) and
applied SVC_mle function:
set.seed(1234)
library(gstat)
# create structure
nx <- 100
ny <- 100
xy <- expand.grid(1:nx, 1:ny)
names(xy) <- c("x","y")
g.dummy <- gstat(formula = z ~ 1 + x + I(y^0.5), locations = ~ x + y,
dummy = T, beta = c(1, 0.01, 0.005),
model = vgm(psill = 0.025, range = 20, model = 'Ste',
kappa = 10), nmax = 20)
yy <- predict(g.dummy, newdata = xy, nsim = 1)
xy.reduced <- as.data.frame(matrix(ncol = 3, nrow = 0))
for (i in 1:5000){
xy.reduced[i, ] <- yy[i*2, 1:3]
}
Fact<- 20/100 #% of reduction about 80%
training <- sample(nrow(xy.reduced), trunc(Fact*nrow(xy.reduced)))
Xtraining <- xy.reduced[training, ]
Xtest <- xy.reduced[-training, ]
df_train <- Xtraining
colnames(df_train) <- c("X", "Y", "sim")
fit_svc <- SVC_mle(sim ~ X + Y, data = df_train, locs = df_train[,
1:2])#
coef(fit_svc)
summary(fit_svc)
but it returns an error of the covariance matrix.
Could someone help me to overcome the error?
thanks in advance
emanuele
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