Thanks a lot for David and Simon! Got it~
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In addition to Simon Wood's reading of the methods I am adding a minor
note on terminology:
What was reported was the ratio in risk per a difference equal to the
interquartile range (of the predictor variable), and the 95% CI for
such an estimate. They were not reporting a 95% CI for the
Isn't the point here that they used smooths for the other terms but a linear
effect (or several) for (lagged) visibility. So you do have a beta in this
case, and the gam summary would tell you what it is estimated to be and what
the associated standard error is. The 'coef' and 'vcov' functions a
In one of the papers...
We developed core models with a generalized additive Poisson regression
allowing for over-dispersion in the model (Wood, 2006). For each mortality
outcome, variations in seasonality, trends, mean temperature, and mean
humidity of current and previous days (lag 0–1) were fi
On Feb 23, 2011, at 7:32 AM, clc wrote:
Thanks a lot. In many papers, they reported the changes and 95% ci
in the
interquartile range, how can I obtain such figure using gam? Thanks!
I suppose I could be wrong (not being an avid user of GAMs), but
reporting a 95% CI for the interquartil
Thanks a lot. In many papers, they reported the changes and 95% ci in the
interquartile range, how can I obtain such figure using gam? Thanks!
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On Feb 22, 2011, at 11:34 PM, clc wrote:
Hi i am doing an environmental research
The equation is as follow:
gam(y1 ~ x1 + s(x2) + s(x3) + s(x4), family = gaussian, fit = true)
I would like to obtain the beta coefficient and 95CI of x4 (or
s(x4)), what
should I do?
You should re-think y
Hi i am doing an environmental research
The equation is as follow:
gam(y1 ~ x1 + s(x2) + s(x3) + s(x4), family = gaussian, fit = true)
I would like to obtain the beta coefficient and 95CI of x4 (or s(x4)), what
should I do?
Thanks,
Lung
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