# which coefficients for a gam(mgcv) model equation?

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## which coefficients for a gam(mgcv) model equation?

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## Re: which coefficients for a gam(mgcv) model equation?

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## Re: 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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## Re: which coefficients for a gam(mgcv) model equation?

 Darren, Sorry for the slow reply. This is probably much to late to be of use to you now. I think that the referee is being unreasonable here. There are many perfectly respectable ways of estimating GAMs for which no explicit expression for the estimated smooth terms is available (See Hastie and Tibshirani's GAM book). mgcv actually  does use smoothers that can be represented explicitly, but it is completely un-illuminating to do so. I've attached a paper describing exactly how the smooths you have used are represented. You could provide the referee with expression (7), and the description of how \delta_i and \alpha_i are obtained from the coefficients actually estimated, but it's not going to help the paper's readers much. best, Simon On Thursday 11 March 2010 16:22, Darren Norris wrote: > 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? -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603  www.maths.bath.ac.uk/~sw283 ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code. tprs.pdf (1M) Download Attachment