# 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?