Please see help("mgcv-FAQ") number 2
best,
simon
On 06/19/2012 05:27 PM, Stefano Sofia wrote:
Given
model1<- gam(Y ~ X1, family=poisson)
(which is a glm), the regression equation is
Log(Y) = 2.132 + 0.00044 X1
In fact from summary(model1), I read
summary(model1)
Family: poisson
Link function: log
...
Parametric coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.132e+00 1.018e-01 20.949< 2e-16 ***
X1 4.406e-04 5.395e-05 8.165 3.21e-16 ***
...
But if I use
model2<- gam(Y ~ s(X1), family=poisson)
how can I write the regression equation? Summary(model2) is
summary(model2)
Family: poisson
Link function: log
Formula:
Y ~ s(X1)
<environment: 0x43c02e0>
Parametric coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.55268 0.06782 37.64<2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
s(X1) 3.967 4.85 71.1 4.86e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.885 Deviance explained = 87.2%
UBRE score = 0.13791 Scale est. = 1 n = 20
Thank you for your help
Stefano Sofia
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