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I am exploring the use of GWmodel to run some GWR regressions. I set it up and tried to run some sample regressions using the gwr.basic function, but ran into the following error,

Error in t(X * W.i) : dims [product 4659] do not match the length of object [0]

This is what I did,

gwr.basic(Y~X+I(X^2), data = spdf,bw = bw.gwr.1, kernel = "gausssian", adaptive = TRUE, F123.test = TRUE)

And here is the summary of my input data set, spdf

**summary(spdf)
    Object of class SpatialPointsDataFrame
    Coordinates:
              min       max
    Long 95.71586 103.70657
    Lat  40.12491  42.89963
    Is projected: NA 
    proj4string : [NA]
    Number of points: 1553
    Data attributes:
         Y             X    
     Min.   : 51.33   Min.   :100  
     1st Qu.:196.19   1st Qu.:115  
     Median :224.74   Median :123  
     Mean   :216.66   Mean   :123  
     3rd Qu.:245.70   3rd Qu.:131  
     Max.   :346.72   Max.   :150** 

What may be happening?

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I believe that you need to set the argument "longlat=TRUE". Since your data is in geographic coordinates it is likely that the kernel is being incorrectly defined. You also may want to explicitly specify the data slot "data = spdf@data".

Please use caution with specification of the GWR method in anything other than exploratory analysis of nonstationarity. It is a somewhat suspect method and there are a number of papers that indicate the incorrectness of this method in both type I & II error and severe bias in coefficients. That said, it can be a great exploratory tool, just not an inferential one.

  • Thanks a lot for the quick comment Jeff, can you point me to some of those papers. Also my problem is such that I am trying to learn the relationship between Y and X, but this relationship is general a function of spatial coordinates, I have data from a wide variety of spatial coordinates, what may be a good way to approach this problem? Also on your other note now I do gwr.res <- gwr.basic(Yield~Days+I(Days^2), data = spdf,bw = bw.gwr.1, kernel = "gausssian", adaptive = TRUE, F123.test = TRUE, longlat=T) I still get the same error :( – ganesh reddy Nov 21 '14 at 18:34
  • Per my second recommendation, try to specify the @data slot for your data argument. I will add some references to my original post. As far as model options, I would highly recommend a mixed effects model. You can specify a spatially lagged y, an autocovariance/autocorrelation term or a polynomial of [y,x] as the random effect. This will decorrelate the error and reveal the underlying linear relationship. Keep in mind that GWR is specifically a model for second order effects (nonstationarity) and if you are after a first order effect it is quite inappropriate, invariant of its validity. – Jeffrey Evans Nov 21 '14 at 18:44
  • I tried the suggestion and got, Error in gwr.basic(Y ~ X + I(X^2), data = spdf@data, bw = bw.gwr.1, : Given regression data must be Spatial*DataFrame – ganesh reddy Nov 21 '14 at 18:54
  • I am not sure if I follow what you are saying, the relationship that Y and X have, I mean the coefficients of say a quadratic curve are functions of space, I think a GWR is one of the most suitable ways? Maybe I am missing something, but if I were to approach the problem I would approach it in a very similar way, I mean to say that an algorithm that gives more weight to the neighboring points, hence building a robust local model, at the same time gives some weight to further off points. What do you think? – ganesh reddy Nov 21 '14 at 18:58
  • I would remove the inhibit interpretation of X^2. Often you have to parse a formula when writing a function and perhaps the I() is not being interpreted correctly in the funciton. – Jeffrey Evans Nov 21 '14 at 23:38

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