Hi Simon,

I'm trying to fit a negative binomial gam with no covariates, that therefore  looks at the detection/non-detection data and nothing else. I thought having 1~1 as formula would allow the model to just estimate occurrence without looking at relationship between variables.


On 25 Aug 2012, at 15:18, Simon Wood-4 [via R] wrote:

> 'gam' doesn't know what to do with the model formula '1 ~ 1' (i.e. "one 
> tilde one"). What is it supposed to mean? 'glm'  also does nothing 
> meaningful in this case... 
> 
> ## code to load you data file into 'dat' omitted 
>  > model<-glm(1~1, data=dat) 
>  > model 
> 
> Call:  glm(formula = 1 ~ 1, data = dat) 
> 
> Coefficients: 
> (Intercept) 
>            1 
> 
> Degrees of Freedom: 0 Total (i.e. Null);  0 Residual 
> Null Deviance:        0 
> Residual Deviance: 0     AIC: -Inf 
> 
> What model are you trying to fit? 
> 
> best, 
> Simon 
> 
> 
> On 08/24/2012 10:36 PM, grace wrote:
> 
> > Hi there, 
> > 
> > I'm using presence-absence data in a gam (i.e. 0 or 1 as values) 
> > I am trying to run a gam with 'dummy covariates' i.e. 1~1 
> > unfortunately my model: 
> > * 
> > model<-gam(1~1, data=bats, family=negbin)* 
> > 
> > keeps putting out: 
> > 
> > * 
> > Error in gam(1 ~ 1, data = bats, family = negbin) : 
> >    Not enough (non-NA) data to do anything meaningful* 
> > 
> > Is there a specific reason it would do this? I have tried using various 
> > actions on NAs, na.pass etc, as well as asking it to turn all NAs to 0 but 
> > to no avail. 
> > 
> > I would post the data here but its rather long, basically 28x104 matrix of 
> > 0/1 with headers 'v1:v28' .The summary for the data comes up fine, it's just 
> > the model that isn't working. 
> > However if I run using: 
> > 
> > *model<-gam(v1~v2, data=bats, family=negbin) 
> > * 
> > 
> > it tends to work. 
> > 
> > Any help is seriously appreciated as I have a deadline and have been trying 
> > this for days, 
> > 
> > Thanks! 
> > 
> > 
> > 
> > -- 
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