Hi,
I've compared observed and predicted and they match 100%.
For 90% probability of occurrence:
table(can>0,fitted(can3.gam)>0.9)
FALSE TRUE
FALSE 23 0
TRUE 0 125
So i guess it is a valid result ..... but very unexpected for me.
Thank you again for all the help,
Monica
> Date: Mon, 31 Mar 2008 09:30:01 -0400
> From: [EMAIL PROTECTED]
> To: [EMAIL PROTECTED]
> CC: r-help@r-project.org
> Subject: Re: [R] unexpected GAM result - at least for me!
>
> On 3/31/2008 9:01 AM, Monica Pisica wrote:
>> Thanks Duncan.
>>
>> Yes i do have variation in the lidar metrics (be, ch, crr, and home)
>> although i have a quite high correlation between ch and home. But even
>> if i eliminate one metric (either ch or home) i end up with a deviation
>> of 99.99. The species has values of 0 and 1 since i try to predict
>> presence / absence.
>>
>> Do you think it is still a valid result?
>
> I repeat: look at the data. Compare the observed and predicted. That's
> the only way to know whether this is reasonable or not.
>
> If you're getting reasonable predictions, then it's a valid fit. (The
> tests and approximations used in the reported p-values may not be at all
> valid. I don't know what the requirements are for those in a GAM, but
> if you're getting a perfect fit, then they probably aren't being met.)
>
> Duncan Murdoch
>
>
>>
>> Thanks again,
>>
>> Monica
>>
>>> Date: Mon, 31 Mar 2008 08:47:48 -0400
>>> From: [EMAIL PROTECTED]
>>> To: [EMAIL PROTECTED]
>>> CC: r-help@r-project.org
>>> Subject: Re: [R] unexpected GAM result - at least for me!
>>>
>>> On 3/31/2008 8:34 AM, Monica Pisica wrote:
>>>>
>>>> Hi
>>>>
>>>>
>>>> I am afraid i am not understanding something very fundamental....
>> and does not matter how much i am looking into the book "Generalized
>> Additive Models" of S. Wood i still don't understand my result.
>>>>
>>>> I am trying to model presence / absence (presence = 1, absence = 0)
>> of a species using some lidar metrics (i have 4 of these). I am using
>> different models and such .... and when i used gam i got this very weird
>> (for me) result which i thought it is not possible - or i have no idea
>> how to interpret it.
>>>>
>>>>> can3.gam <- gam(can>0~s(be)+s(crr)+s(ch)+s(home), family = 'binomial')
>>>>> summary(can3.gam)
>>>> Family: binomial
>>>> Link function: logit
>>>> Formula:
>>>> can> 0 ~ s(be) + s(crr) + s(ch) + s(home)
>>>> Parametric coefficients:
>>>> Estimate Std. Error z value Pr(>|z|)
>>>> (Intercept) 85.39 162.88 0.524 0.6
>>>> Approximate significance of smooth terms:
>>>> edf Est.rank Chi.sq p-value
>>>> s(be) 1.000 1 0.100 0.751
>>>> s(crr) 3.929 8 0.380 1.000
>>>> s(ch) 6.820 9 0.396 1.000
>>>> s(home) 1.000 1 0.314 0.575
>>>> R-sq.(adj) = 1 Deviance explained = 100%
>>>> UBRE score = -0.81413 Scale est. = 1 n = 148
>>>>
>>>> Is this a perfect fit with no statistical significance, an
>> over-estimating or what???? It seems that the significance of the
>> smooths terms is "null". Of course with such a model i predict perfectly
>> presence / absence of species.
>>>>
>>>> Again, i hope you don't mind i'm asking you this. Any explanation
>> will be very much appreciated.
>>>
>>> Look at the data. You can get a perfect fit to a logistic regression
>>> model fairly easily, and it looks as though you've got one. (In fact,
>>> the huge intercept suggests that all predictions will be 1. Do you
>>> actually have any variation in the data?)
>>>
>>> Duncan Murdoch
>>
>>
>> In a rush? Get real-time answers with Windows Live Messenger.
>>
>
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