> Hi everyone,
> I am fitting a bivariate smoothing model by using gam.
> But I got an error message like this:
> "Error in eigen(hess1, symmetric = TRUE) : 0 x 0 matrix"
- this is a known problem in mgcv 1.3-20 (an optimizer fails to cope with
convergence in one step). It's fixed in 1.3-21, whi
Hello,
it's really difficult for anyone to make a constructive response based
on your message. The problem could be in:
1) the function you fit (which one is it?, and which package?)
2) the arguments that you supplied (what did you tell it to do?)
3) the data that you gave it (what are they?)
T
Hi everyone,
I am fitting a bivariate smoothing model by using gam.
But I got an error message like this:
"Error in eigen(hess1, symmetric = TRUE) : 0 x 0 matrix"
If anyone know how to figure it out, pleaselet me know.
Thanks very much.
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> Warning in eval(expr, envir, enclos) : non-integer #successes in a
> binomial glm!
- one way of specifying a logistic regression model is to supply the
observed proportion of sucesses as the response variable (e.g. y) and the
binomial n as the weights. The warning is complaining that y/n is
non
I am trying to use R to do a weighted GAM with PA (presence/random) as the
response variable (Y, which is a 0 or a 1) and ASPECT (values go from
0-3340), DEM (from 1500-3300), HLI (from 0-5566), PLAN (from -3 to 3),
PROF (from -3 to 3), SLOPE (from 100-500) and TRI (from 0-51) as
predictor variable
