autoKrigeST.cv: Cross-validation of spatiotemporal Kriging
View source: R/autoKrigeST.cv.R
| autoKrigeST.cv | R Documentation |
Cross-validation of spatiotemporal Kriging
Description
Cross-validation of spatiotemporal Kriging
Usage
autoKrigeST.cv(
data,
fold_dim,
nfold = 10L,
formula,
type_stv = "sumMetric",
block = 0,
model = c("Sph", "Exp", "Gau", "Ste"),
kappa = c(0.05, seq(0.2, 2, 0.1), 5, 10),
fix.values = c(NA, NA, NA),
tlags = 0:6,
cutoff = 20000,
width = 500,
nmax = Inf,
aniso_method = "vgm",
type_joint = "Exp",
prodsum_k = 0.25,
surface = FALSE,
start_vals = c(NA, NA, NA),
miscFitOptions = list(),
measurement_error = c(0, 0, 0),
cores = 1,
seed = 130425L
)
Arguments
data |
a 'STFDF'-class object |
fold_dim |
character. the dimension at which you want to cross-validate (spatial, temporal, and random) |
nfold |
integer. the number of folds. 10 as the default |
formula |
formula. e.g., y~1 |
type_stv |
character. One of 'sumMetric', 'metric', 'productSum', and 'separable' |
block |
numeric. passed to conduct block spatiotemporal Kriging. |
model |
character vector. Default is c("Sph", "Exp", "Gau", "Ste"), but users can specify the list of theoretical variograms by referring gstat::vgm. |
kappa |
numeric vector. Kappa values tested for Matern-family variogram models. |
fix.values |
numeric vector. Initial values in order of nugget, range, and sill, respectively. |
tlags |
integer vector (increasing, preferably to be consecutive). temporal lags. |
cutoff |
numeric. The maximum distance at which the sample variogram will be computed. |
width |
numeric. The interval at which the variogram cloud will be summarized. |
nmax |
integer or positive infinite. The maximum number of spatiotemporal neighbors to conduct the local spatiotemporal Kriging. |
aniso_method |
character. One of 'vgm', 'linear', 'range', and 'metric'. Please refer to ?gstat::estiStAni. |
type_joint |
character. The model form of joint spatiotemporal variogram. |
prodsum_k |
numeric. The parameter for the case when 'productSum' is chosen for type_stv. |
start_vals |
numeric vector (3). The initial values to optimize the spatiotemporal variogram model. |
miscFitOptions |
list. See ?automap::autofitVariogram. |
cores |
integer. The number of threads that will be used to compute the sample spatiotemporal variogram. |
newdata_mode |
character. One of 'rect' (rectangular grid) and 'chull' (convex hull) |
newdata_npoints |
integer. The number of points that will be generated in the range of geometry the user specified (one of rectangle or convex hull) |
GLS.model |
a variogram model. The default value is NA. If a variogram model is passed, a Generalized Lease Squares sample variogram will be calculated. |
predict_chunk |
integer. The number of data points per chunk in the new data for the large data. It should be meticulously chosen according to the user's machine specification. |
Value
The cross-validated spatiotemporal Kriging results.
Examples
library(sp)
library(gstat)
library(spacetime)
library(stars)
library(dplyr)
data(air)
deair = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
deair_sf = st_as_stars(deair, crs = '+proj=longlat +ellps=sphere')
deair_sf = st_transform(deair_sf, 3857)
deair_r = as(deair_sf, 'STFDF')
deair_r@sp@proj4string = CRS('+init=epsg:3857')
deair_rs = deair_r[,3751:3800]
## autoKrigeST.cv test
akst_cv_t = autoKrigeST.cv(formula = PM10~1, data = deair_rs, nfold = 3, fold_dim = 'temporal',
cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
akst_cv_s = autoKrigeST.cv(formula = PM10~1, data = deair_rs, nfold = 3, fold_dim = 'spatial',
cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
#akst_cv_r = autoKrigeST.cv(formula = PM10~1, data = deair_rs, nfold = 3, fold_dim = 'random',
# cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
akst_cv_spt = autoKrigeST.cv(formula = PM10~1, data = deair_rs, nfold = 4, fold_dim = 'spacetime',
cutoff = 300000, width = 30000, tlags = 0:7, cores = 8)
