This function is used to calculate the estimates for \(\mu(t), F_i(x,s,t)'s\) based on the object obtained from cv.nonlinear.
getcoef.nonlinear(fit.cv, n.x.grid = 50)
the object obtained from cv.nonlinear.
the number of grid points of \(x\). The estimated \(F_i(x,s,t)\) is calculated in a three-dimensional grid of \((x,s,t)\). The grid points of \(s\) and \(t\) are the observation points of \(X_i(s)\) and \(Y(t)\) used in cv.nonlinear, respectively. The grid of \(x\) includes n.x.grid equally spaced values between the minimum and maximum of all the discretely observed values of \(X_i(s)\). Default of n.x.grid is 50.
a list containing
the vector of estimated values of \(\mu(t)\) at the observation points of the response function.
a list of length \(p\), the number of functional predictors. Its \(i\)-th element is a three dimensional array with estimated values of \(F_i(x,s,t)\) on the three-dimensional grid X.grid[[i]]*t.x.list[[i]]*t.y (see below).
a list of length \(p\). Its \(i\)-th element is the vector of grid points for \(x\) and includes n.x.grid equally spaced values between the minimum and maximum of all the discretely observed values of \(X_i(s)\).
one of the arguments in cv.nonlinear, specifying the list of the vectors of obesrvation points for \(X_i(s)\), \(1\le i\le p\).
one of the arguments in cv.nonlinear, specifying the vector of obesrvation points of the response curve \(Y(t)\).
%% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ...
# NOT RUN {
#See the examples in cv.nonlinear().
# }
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