Generic mnf method
Compute mnf from spatial, temporal, or spatio-temporal data
mnf(x, ...) ## S3 method for class 'matrix' mnf(x, ..., Sigma.Noise, use = "complete.obs") ## S3 method for class 'mts' mnf(x, ..., use = "complete.obs") ## S3 method for class 'zoo' mnf(x, ..., use = "complete.obs") ## S3 method for class 'SpatialPixelsDataFrame' mnf(x, ..., use = "complete.obs") ## S3 method for class 'SpatialGridDataFrame' mnf(x, ..., Sigma.Noise, use = "complete.obs") ## S3 method for class 'RasterStack' mnf(x, ..., use = "complete.obs") ## S3 method for class 'RasterBrick' mnf(x, ..., use = "complete.obs") ## S3 method for class 'STSDF' mnf(x, ..., use = "complete.obs", mode = "temporal") ## S3 method for class 'STFDF' mnf(x, ..., use = "complete.obs", mode = "temporal")
x |
object for which an mnf method is available |
... |
ignored |
Sigma.Noise |
Noise covariance matrix; when missing, estimated from the data by using the covariance of lag-one spatial or temporal differences (MAF) |
use |
method to deal with missing values when computing covariances; see cov |
mode |
for |
Uses MAF (Min/max Autocorrelation Factors) to estimate the noise covariance. This implementation estimates the noise covariance by 0.5 \mbox{Cov}(Z(s)-Z(s+Δ)), so that eigenvalues can be directly interpreted as approximate estimates of the noice covariance.
object of class (c("mnf", "prcomp"); see prcomp. Additional elements
are values, containing the eigenvalues.
http://r-spatial.org/r/2016/03/09/MNF-PCA-EOF.html
# temporal data:
set.seed(13531) # make reproducible
s1 = arima.sim(list(ma = rep(1,20)), 500)
s2 = arima.sim(list(ma = rep(1,20)), 500)
s3 = arima.sim(list(ma = rep(1,20)), 500)
s3 = s3 + rnorm(500, sd = 10)
d = cbind(s1,s2,s3)
plot(d)
m = mnf(d)
m
summary(m)
plot(predict(m))
# spatial example:
## Not run:
library(sp)
grd = SpatialPoints(expand.grid(x=1:100, y=1:100))
gridded(grd) = TRUE
fullgrid(grd) = TRUE
pts = spsample(grd, 50, "random")
pts$z = rnorm(50)
library(gstat)
v = vgm(1, "Sph", 90)
out = krige(z~1, pts, grd, v, nmax = 20, nsim = 4)
out[[3]] = 0.5 * out[[3]] + 0.5 * rnorm(1e4)
out[[4]] = rnorm(1e4)
spplot(out, as.table = TRUE)
m = mnf(out)
m
summary(m)
## End(Not run)
if (require(gstat)) {
data(wind)
library(sp)
wind.loc$y = as.numeric(char2dms(as.character(wind.loc[["Latitude"]])))
wind.loc$x = as.numeric(char2dms(as.character(wind.loc[["Longitude"]])))
coordinates(wind.loc) = ~x+y
proj4string(wind.loc) = "+proj=longlat +datum=WGS84"
# match station order to names in wide table:
stations = 4:15
wind.loc = wind.loc[match(names(wind[stations]), wind.loc$Code),]
row.names(wind.loc) = wind.loc$Station
wind$time = ISOdate(wind$year+1900, wind$month, wind$day, 0)
space = list(values = names(wind)[stations])
wind.st = stConstruct(wind[stations], space, wind$time, SpatialObj = wind.loc, interval = TRUE)
m = mnf(wind.st)
m
plot(m)
stplot(predict(m), mode = "tp")
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