require(geoR)
data(ca20) ## data set from geoR
ca20.df <- as.data.frame(ca20)
head(ca20.df)
RFoptions(coordnames=c("east", "north"), varnames="data")
## covariance model with variance, scale and nugget to be estimated;
## just to abbreviate later on
M <- RMexp(var=NA, scale=NA) + RMnugget(var=NA)
## short definition of a trend using the fact that ca20.df is a
## data.frame
ca20.RFmod02 <- ~ 1 + altitude + M
(ca20.fit02.RF <- RFfit(ca20.RFmod02, data=ca20.df))
## long definition, which allows also for more general constructions
ca20.RFmod02 <- NA + NA*RMcovariate(ca20.df$altitude) + M
(ca20.fit02.RF <- RFfit(ca20.RFmod02, data=ca20.df))
## Note that the following also works.
## Here, the covariance model must be the first summand
ca20.RFmod02 <- M + NA + ca20.df$altitude
print(ca20.fit02.RF <- RFfit(ca20.RFmod02, data=ca20.df))
### The following does NOT work, as R assumes (NA + ca20.df$altitude) + M
(ca20.RFmod02 <- NA + ca20.df$altitude + M)
try(ca20.fit02.RF <- RFfit(ca20.RFmod02, data=ca20.df)) ### error ...
## factors:
ca20.RFmod03 <- ~ 1 + area + M ###
(ca20.fit03.RF <- RFfit(ca20.RFmod03, data=ca20.df))
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