idw() fails to produce an 'error map' with canonical data set meuse

I am stumped by a failure of idw() to produce valid values for var1.var,
the weighted squared distance. Here is my rather basic script:

data(meuse)
data(meuse.grid)

# 1. Convert Sampled Points (meuse) to SpatialPointsDataFrame (sp
format for gstat)
meuse_sp <- meuse
coordinates(meuse_sp) <- ~x+y
proj4string(meuse_sp) <- CRS("+init=epsg:28992")

# 2. Convert Prediction Grid (meuse.grid) to SpatialPixelsDataFrame
#    (sp format for gstat and prediction)
meuse_grid_sp <- meuse.grid
coordinates(meuse_grid_sp) <- ~x+y
gridded(meuse_grid_sp) <- TRUE # Define it as a regular grid
proj4string(meuse_grid_sp) <- CRS("+init=epsg:28992")

# We use the idw() function directly for IDW
idw_zinc <- idw(log(zinc) ~ 1,           # Interpolate log(zinc) with
a constant mean (~1)
                 meuse_sp,                # Using the sampled data
                 newdata = meuse_grid_sp, # Predicting onto the grid
                 idp = 2.0)               # Inverse Distance Power = 2

head(idw_zinc at data)  var1.pred var1.var
1  6.257014       NA
2  6.399096       NA
3  6.300862       NA
4  6.213336       NA
5  6.647233       NA
6  6.482221       NA

What am I missing here? How can prediction values be produced but the error
values not?


Cheers,

         Jochen

Dr. Jochen Albrecht, GISP (he/him/his)

Department of Geography and Environmental Science
<http://www.geo.hunter.cuny.edu/>

Hunter College CUNY

695 Park Avenue

New York, NY 10065

Member, Board of Directors, GIS Certification Institute
<https://www.gisci.org/>

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