Is there a way to force kriging to keep unchanged measured values of observation points?
For example: if the observed value of a location is 1, after the interpolation it could be 0.9 or even 0.5, depending on the other locations' values. I'd like to have 1 for that observed location after the prediction too.
I know that, considering the kriging formula, this is a stretch but predicting values where it is actually known seems to me a stretch as well. I found that the only prediction method that preserves observation points' values is IDW but this is not the best method to predict values for the other prediction locations.
I'm using R and the package "intamap".
I'm using the "automatic" method provided by "intamap" package. I need to use it for two reasons: the first is for the very strong skewness of my data and the second reason is that kriging interpolation is the final step of method I'm developing that is devoted to be user friendly for inexpert persons.
"Intamap" package claims to meet these two needs. The reason why I need to keep original data unchanged is that observation points represent the probability values of a phenomenon to occur. Pay attention, I'm not talking about indicator kriging, because observed values are just probabilities. So I need to provide inexpert users the geographical distribution of different degree of probabilities. If I have two observation points with 1 and 0.5 probability values, I need these values to be unchanged and that kriging interpolates the values in the prediction locations between them.
I hope to have been clear in my explanation. How can I proceed now?