Re: [R-sig-Geo] negative range from fit.variogram through autofitVariogram

Tue, 04 May 2010 07:15:28 -0700 

Thanks.  I'll pick it up as it becomes available.
Does the possibility exist that you would delete all models? In such a case, what does the function return? 

On 05/04/2010 05:49 AM, Paul Hiemstra wrote:

I just uploaded a new version of automap (1.07) that fixes this 
problem. It deletes fitted variogram models with a negative 
sill/range/nugget.


cheers,
Paul

Paul Hiemstra wrote:

Hi Mark,


Thanks for the reproducible example. The problem is that when I look 
at the sample variogram, the semivariance values start high and end 
low. This is best illustrated by:


plot(variogram(theta_percent~1, sparse))


You see that there are outliers in the data that cause high 
semi-variance at a short distance. I would say that is 'strange' ;). 
You can try and identify which point causes this by:


plot(variogram(theta_percent~1, sparse, cloud = TRUE), identify = TRUE)
# Click on the plot to identify the point pairs


There is not really one value I think that causes this. It might be 
also attributable to the fact that your dataset is somewhat sparse in 
the distance range from 20-40 m.



As a quick fix you can restrict the model selection to "Sph", this 
works. And I would not be enthusiastic about using Ste. This is 
because the main difference between different kappa values if the 
behavior at short distances, but you don't have a lot of data on the 
short distance to fit this value on in a meaningful way.



At this stage I don't see a (relatively quick) fix that could solve 
this problem in an automatic way and on a more fundamental level. Do 
you have any suggestions? From an implementation point of view I can 
let automap discard any model that has negative values in it, this 
would ensure that the user gets at least the Sph model back.


cheers,
Paul

Mark Connolly wrote:

I am using autofitVariogram during the process of interpolating a 
large set of daily observations through a volume.  Each volume is 
decomposed into 2D layers prior to selecting a model to use for 
interpolation.  I made it through 2010 interpolations and then ran 
into a failed interpolation when the best model selected by 
autofitVariogram had a negative range.  This was rejected by the 
krige function.  I see mention of negative sills but not of negative 
ranges.



It appears that autofitVariogram is having some issues with the 
trial arguments sent to fit.variogram.  This is repeatable.  Not 
sure if this is a bug for some package or a data issue.  The data 
values do not look overly strange.




# data
sparse =
structure(list(x = c(740381.862, 740456.052, 740503.958, 740551.752,
740559.502, 740502.995, 740446, 740389.229, 740371.693, 740428.25,
740484.918, 740541.356, 740549.277, 740474.724, 740418.118, 740370.187,
740354.321, 740410.53, 740467.451, 740523.772, 740522.433, 740474.797,
740400.293, 740343.175, 740336.067, 740392.917, 740449.622, 740506.162,
740495.664, 740448.693, 740382.062, 740325.464, 740318.174, 740430.337,
740488.37, 740477.578, 740429.695, 740373.133, 740325.408, 740631.842,
740688.362, 740744.857, 740726.149, 740695.778, 740621.663, 740613.553,
740670.205, 740726.566, 740733.965, 740660.272, 740620.315, 740594.82,
740651.714, 740708.217, 740690.056, 740659.603, 740575.902, 740576.796,
740558.179), y = c(181644.086, 181620.772, 181605.577, 181590.417,
181568.637, 181586.847, 181604.615, 181622.136, 181565.531, 181547.708,
181530.169, 181512.439, 181490.328, 181513.956, 181531.875, 181547.048,
181508.946, 181491.148, 181473.394, 181455.233, 181436.726, 181452.342,
181475.522, 181492.661, 181451.96, 181434.265, 181416.566, 181398.729,
181382.764, 181397.808, 181418.748, 181436.677, 181395.477, 181360.409,
181342.547, 181327.09, 181341.971, 181359.55, 181374.453, 181546.959,
181528.576, 181510.58, 181497.501, 181507.159, 181530.759, 181490.008,
181472.209, 181453.968, 181432.87, 181456.085, 181468.588, 181433.854,
181415.758, 181397.497, 181384.359, 181393.795, 181420.579, 181376.982,
181363.899), depth_cm = c(-8, -8, -8, -8, -8, -8, -8, -8, -8,
-8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8,
-8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8,
-8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8, -8,
-8, -8), theta_percent = c(23.63, 19.68, 23.81, 22.01, 23.98,
12.8, 14.92, 20.49, 22.59, 24.32, 20.24, 23.03, 12.97, 19.09,
39.2, 12.09, 24.52, 25.57, 25.5, 19.76, 19.17, 21.98, 7.5, 22.75,
17.85, 17.75, 17.95, 26.93, 18.84, 22.95, 23.71, 25.03, 40.69,
9.7, 24.66, 17.43, 16.3, 24.13, 19.98, 23.35, 12.16, 17.24, 14.29,
34.42, 21.84, 25.63, 20.51, 25.87, 24.44, 22.35, 8.57, 21.43,
25.63, 21.56, 21.49, 17.66, 25.61, 24.11, 28.31)), .Names = c("x",
"y", "depth_cm", "theta_percent"), row.names = c("1", "5", "10",
"15", "20", "25", "30", "35", "40", "45", "50", "55", "60", "65",
"70", "75", "80", "85", "90", "95", "100", "105", "109", "114",
"119", "124", "129", "134", "139", "144", "149", "154", "159",
"164", "169", "174", "179", "184", "188", "193", "198", "203",
"208", "213", "218", "223", "228", "233", "238", "243", "248",

"253", "258", "263", "268", "273", "278", "283", "288"), class = 
"data.frame")



# the broken fit for best search
require("automap")
coordinates(sparse) = c("x", "y", "depth_cm")
proj4string(sparse) = CRS("+init=epsg:32119")
v.fit <- autofitVariogram(theta_percent~1, sparse)


There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In getModel(initial_sill - initial_nugget, m, initial_range,  ... :
  An error has occured during variogram fitting. Used:
        nugget: 34.1432533936652
        model:  Exp
        psill:  13.2004974623731
        range:  53.1549477005646
        kappa:  NA

  as initial guess. This particular variogram fit is not taken into 
account.

Gstat error:

Error in if (direct[direct$id == id, "is.direct"] && any(model$psill 
<  :

  missing value where TRUE/FALSE needed

2: In fit.variogram(object, model, fit.sills = fit.sills,  ... :
  value out of range in 'bessel_k'
3 ...


# a quick visual of the data in the field

rescale = function(x, to=c(1,10)) (x - min(x)) * ((max(to) - 
min(to))/(max(x) - min(x)))

require("rgl")
sparse_df=as.data.frame(sparse)

spheres3d(sparse_df$x, sparse_df$y, sparse_df$depth_cm, 
radius=rescale(sparse_df$theta_percent))


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