The degree of roughness of an estimated function is controlled by a smoothing parameter $lambda$ that directly multiplies the penalty. However, it can be difficult to interpret or choose this value, and it is often easier to determine the roughness by choosing a value that is equivalent of the degrees of freedom used by the smoothing procedure. This function converts a multipler $lambda$ into a degrees of freedom value.
lambda2df(argvals, basisobj, wtvec=rep(1, n), Lfdobj=NULL, lambda=0)the equivalent degrees of freedom value.
a vector containing the argument values used in the smooth of the data.
the basis object used in the smoothing of the data.
the weight vector, if any, that was used in the smoothing of the data.
the linear differential operator object used to defining the roughness penalty employed in smoothing the data.
the smoothing parameter to be converted.
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
df2lambda