linmod {fda} | R Documentation |
A functional dependent variable is approximated by a single functional covariate, and the covariate can affect the dependent variable for all values of its argument. The regression function is a bivariate function.
linmod(xfdobj, yfdobj, wtvec=rep(1,nrep), xLfdobj=int2Lfd(2), yLfdobj=int2Lfd(2), xlambda=0, ylambda=0)
xfdobj |
a functional data object for the covariate |
yfdobj |
a functional data object for the dependent variable |
wtvec |
a vector of weights for each observation. |
xLfdobj |
either a nonnegative integer or a linear differential operator object. This operator is applied to the regression function's first argument. |
yLfdobj |
either a nonnegative integer or a linear differential operator object. This operator is applied to the regression function's second argument. |
xlambda |
a smoothing parameter for the first argument of the regression function. |
ylambda |
a smoothing parameter for the second argument of the regression function. |
a named list of length 3 with the following entries:
alphafd |
the intercept functional data object. |
regfd |
a bivariate functional data object for the regression function. |
yhatfd |
a functional data object for the approximation to the dependent variable defined by the linear model, if the dependent variable is functional. Otherwise the matrix of approximate values. |
fRegress
#See the prediction of precipitation using temperature as #the independent variable in the analysis of the daily weather #data.