Hello, I would like to rise a question on the use of predict {gstat},
I'm trying to perform the estimation of a spatially distributed variable at
the support scale of a particular area (Block kriging). I have access to an
additional variable, it is known that the variable of interest is
correlated
13 January 2016 at 14:43, Edzer Pebesma
wrote:
>
>
> On 13/01/16 14:16, Antonio Manuel Moreno Ródenas wrote:
> > Hello, I would like to rise a question on the use of predict {gstat},
> >
> > I'm trying to perform the estimation of a spatially distributed variab
Dear r-sig-geo community,
I would like to bring a conceptual question on the implementation of
ordinary kriging in gstat.
I'm trying to account for my measurement error in a OK scheme. I assume
that my sampled vector Y(x_i) is a noisy realisation of S(x_i) (the real
variable), thus:
Y(x_i) = S(x_
n of the kriging variance).
>
> Best regards, Ruben.
>
> El 04/02/2016 a las 15:56, Antonio Manuel Moreno Ródenas escribió:
> > Dear r-sig-geo community,
> >
> > I would like to bring a conceptual question on the implementation of
> > ordinary kriging in gstat.
~1, meuse[1:5,], meuse[1,],
>vgm(.6, "Sph", 900, Err = .05), set = list(debug = 32))
>
> You'll see that the difference is in the right-hand-side, the covariance
> between observations and prediction location (C0).
>
> Hth,
>
>
> On 05/02/16 11:48, Anto
