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Dear Mr. Simon Wood, dear list members, I am trying to fit a similar model with gam from mgcv compared to what I did with BayesX, and have discovered the relatively new possibility of incorporating user-defined matrices for quadratic penalties on parametric terms using the "paraPen" argument. This was really a very good idea! However, I would like to constraint the coefficients belonging to one penalty matrix to sum to zero. So I would like to have the same centering constraint on user-defined penalized coefficient groups like it is implemented for the spline smoothing...

I have a data assimilation problem that might be amenable to the use of GAMS, but I am not sure how feasible it is to implement. I was told the R mailing list was a great resource. My observations are spatiotemporal salinity in the San Francisco Bay at a number of instruments over a few days. The thing that I want to fit is the initial condition for a salt transport model at the beginning of this

...spline at knot location 0,  ## set it to 0 by dropping...  X <- sm$X[,-3]        ## spline basis  S <- sm$S[[1]][-3,-3] ## spline penalty  off <- y*0 + .6      ## offset term to force curve through (0, .6)  ## fit spline constrained through (0, .6)...  b <- gam(y ~ X - 1 + offset(off),paraPen=list(X=list(S)))  lines(x,predict(b))  ## compare to unconstrained fit...  b.u <- gam(y ~ s(x,k=9),data=dat,knots=knots)  lines(x,predict(b.u))   [[alternative HTML version deleted]]

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.