[R-sig-Geo] predict a map from point data
tom.gottfried at wzw.tum.de
Mon Feb 28 10:41:25 CET 2011
Am 28.02.2011 01:54, schrieb Sadz A:
> I'm trying to predict the distribution of timber over an area, I have point
> location data- so it would make sense to use a krigging to interpolate the data
> over the whole map. Unfortunately the krigging predictions are pretty bad.
What does "pretty bad" mean? What did you actually do? There are many "flavours" of doing kriging
including variogram estimation and modelling. E.g. you seem to interpolate volume data, so I suppose
your "point location data" actually represent a volume too. Sounds like fitting a Gaußian model and
doing block kriging could be appropriate.
> I have now got environmental data for my study site and have used the r package
> 'quantreg' to make a model that I can predict the volume from
> R code:
> fit <- rqss(sum_vol~qss(env.factor1,lambda=1)+ qss(env.factor2,lambda=1), tau =
> predi<-predict(fit, new, interval = "none", level = 0.9)
Do you mean you have any covariates? Maybe multivariable geostatistics (the gstat package) are your
> unfortunately this is not working either (a problem with the predict function).
> I have also tried linear modelling, Gams and inverse interpolations.
Any errors, traceback()?
> Does anyone have any ideas on how I could get a spatial map of volume
> distribution from the point data?
> Any help is appreciated,
> thank you
> ps- if anything is unclear I would be happy to clarify
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