Dear users,
I am trying to show the equation (including coefficients from the model
estimates) for a gam model but do not understand how to.
Slide 7 from one of the authors presentations (gam-theory.pdf URL:
http://people.bath.ac.uk/sw283/mgcv/) shows a general equation
log{E(yi )} = ?+ ?xi + f (zi ) .
What I would like to do is put my model coefficients and present the
equation used. I am an ecologist not a statistician - and have no access to
statistical advice.
How would I use values from the model below to complete the equation:
log{E(yi )} = ?+ ?xi + f (zi )?
Many thanks,
Darren
## from the help
library(mgcv)
set.seed(0) ## simulate some data...
dat <- gamSim(1,n=400,dist="normal",scale=2)
b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
summary(b)
I have tried searching help forum with "gam equation" I was not able
to
find an answer from the 18 threads returned.
Also tried looking at "?predict.gam" and simply
"predict.gam" however my
understanding of R is not sufficient to understand the details to be able to
answer my question.
I checked Simon Woods excellent presentations
(http://www.maths.bath.ac.uk/~sw283/) however if the answer is there I am
too stupid to see it. In truth the content assumes a level of basic
understanding that I don't have. Unfortunately I do not have access to the
book ( Wood S.N. (2006) Generalized Additive Models: An Introduction with R.
Chapman and Hall/CRC Press.) or finances necessary to get it.
Any help would be much appreciated,
R version 2.10.1 (2009-12-14)
i386-pc-mingw32
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United
Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mgcv_1.6-1
loaded via a namespace (and not attached):
[1] grid_2.10.1 lattice_0.17-26 Matrix_0.999375-33 nlme_3.1-96
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Gavin Simpson
2010-Mar-05 12:07 UTC
[R] which coefficients for a gam(mgcv) model equation?
On Thu, 2010-03-04 at 14:49 -0800, doon75 wrote:> Dear users, > I am trying to show the equation (including coefficients from the model > estimates) for a gam model but do not understand how to. > Slide 7 from one of the authors presentations (gam-theory.pdf URL: > http://people.bath.ac.uk/sw283/mgcv/) shows a general equation > log{E(yi )} = ?+ ?xi + f (zi ) .That would be the model for the Gamma GAM.> What I would like to do is put my model coefficients and present the > equation used. I am an ecologist not a statistician - and have no access to > statistical advice. > How would I use values from the model below to complete the equation: > log{E(yi )} = ?+ ?xi + f (zi )? > Many thanks, > Darren > > ## from the help > library(mgcv) > set.seed(0) ## simulate some data... > dat <- gamSim(1,n=400,dist="normal",scale=2) > b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat) > summary(b)coef(b) will give you the coefficients for the smooth terms + the intercept for that model. ?gamObject describes thus a little. The coefficients for the smooth/spline terms here are of length 9 each, and each set of 9 coefficients pertains to an f(), so the model in the R code you gave would be something like E(y_i) ~ alpha + f_1(x0_i) + f_2(x1_i) + f_3(x2_i) +f_4(x3_i) or E(y_i) = alpha + f_1(x0_i) + f_2(x1_i) + f_3(x2_i) +f_4(x3_i) + e, where e ~ N(0, sigma) The first number returned by coef(b) is the intercept term, the alpha above. The next 9 numbers are the coefficients for the spline (f_1) fitted to the variable x0, and so on. These coefficients relate to the set of basis functions used to represent the spline for each covariate (x0, x1 etc). Depending on why you want to write out the equation, you may not need the detail provide at the level of the coefficients. Are the equations above sufficient for your needs? I'm an ecologist too and rarely do I need to think about the models I am working with at the level of the coefficients for the basis functions, but your needs may be different. If so, perhaps you could provide more details on why you want the equation for the model? HTH G> > I have tried searching help forum with "gam equation" I was not able to > find an answer from the 18 threads returned. > Also tried looking at "?predict.gam" and simply "predict.gam" however my > understanding of R is not sufficient to understand the details to be able to > answer my question. > I checked Simon Woods excellent presentations > (http://www.maths.bath.ac.uk/~sw283/) however if the answer is there I am > too stupid to see it. In truth the content assumes a level of basic > understanding that I don't have. Unfortunately I do not have access to the > book ( Wood S.N. (2006) Generalized Additive Models: An Introduction with R. > Chapman and Hall/CRC Press.) or finances necessary to get it. > Any help would be much appreciated, > > R version 2.10.1 (2009-12-14) > i386-pc-mingw32 > > locale: > [1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United > Kingdom.1252 > [3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C > [5] LC_TIME=English_United Kingdom.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] mgcv_1.6-1 > > loaded via a namespace (and not attached): > [1] grid_2.10.1 lattice_0.17-26 Matrix_0.999375-33 nlme_3.1-96-- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
Darren Norris
2010-Mar-11 16:22 UTC
[R] which coefficients for a gam(mgcv) model equation?
I have spent a few days trying to figure this from the reply out but am still
stuck!
I need the equation to reply to a request from a referee that was to: "show
the specific estimating equation associated with the fitted line".
the model I am running is (I hope the data frame is not necessary as I think
I am just not getting some basic concept, but it can be provided off list):
gam1<-gam(LR~s(Property_lg),data=property) ## use default family
gaussian(link = "identity")
coef(gam1) returns:
(Intercept) s(Property_lg).1 s(Property_lg).2 s(Property_lg).3
s(Property_lg).4 s(Property_lg).5
44.1777350 -9.4673457 -1.5743877 0.5658906
2.2219434 0.4118942
s(Property_lg).6 s(Property_lg).7 s(Property_lg).8 s(Property_lg).9
2.4477335 -0.6590291 14.6142365 3.4184510
so is the "estimating equation":
E(y_i) ~ 44.1777 + f_1(-9.467) + f_2(-1.574) + f_3(0.565) +f_4(2.221) +
f_5(0.411) + f_6(2.447) + f_7(-0.659) +f_8(14.614)+f_9(3.418)
>From the ?predict? function I know the fitted value at x = 0 (intercept) is
-25.5256255 and at x = 1 is -1.3417508. How do I calculate these values from
the ?estimating equation? above?
For x=1, I am doing the calculation below which is obviously incorrect, but
how do I calculate the predicted values by hand, I must be missing something
incredibly obvious?
44.1777 + (1*-9.467) + (1*-1.574) + (1*0.565) + (1*2.221) + (1*0.411) +
(1*2.447) + (1*-1*0.659) + (1*4.614)+ (1*3.418)
Many thanks for any further guidance,
Darren
coef(b)
will give you the coefficients for the smooth terms + the intercept for
that model. ?gamObject describes thus a little.
The coefficients for the smooth/spline terms here are of length 9 each,
and each set of 9 coefficients pertains to an f(), so the model in the R
code you gave would be something like
E(y_i) ~ alpha + f_1(x0_i) + f_2(x1_i) + f_3(x2_i) +f_4(x3_i)
or
E(y_i) = alpha + f_1(x0_i) + f_2(x1_i) + f_3(x2_i) +f_4(x3_i) + e, where
e ~ N(0, sigma)
If so, perhaps you could provide more details on why you want the
equation for the model?
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