search for: f_4

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

...^2; f3 = 4*p*s^3*(1-s) + 4*(1-p)*t^3*(1-t); f4 = p*s^4 + (1-p)*t^4; The polynomial f_i represents the probability of seeing i successes. Suppose we repeat this experiment 1000 times, and u_i is the number of times we saw i successes. The likelihood of this event is f_0^u_0*f_1^u_1*f_2^u_2*f_3^u_3*f_4^u_4, and we seek to find those parameter values for s,t,p which maximize the likelihood. My Singular package has as input the 5 polynomials and a data vector u. For the particular example of u = (3,5,7,11,13). The output are the following four roots (p,s,t) and the corresponding Likelihoood value:...

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but