Smooth the mean function of sparse data
Do a smoothing of the mean function for sparse data that is either given as a list or as a matrix with NAs. The smooth is done by basis expansion with the functional basis "type"; if !(lambda == 0) then the second derivative is penalized (int2Lfd(2)).
smooth.sparse.mean(data, time ,rng = c(0, 1), type = "", nbasis = NULL,
knots = NULL, norder = NULL, lambda = NULL)data |
a matrix object or list – If the set is supplied as a matrix object, the rows must correspond to argument values and columns to replications, and it will be assumed that there is only one variable per observation. If y is a three-dimensional array, the first dimension corresponds to argument values, the second to replications, and the third to variables within replications. – If it is a list, each element must be a matrix object, the rows correspond to argument values per individual. First column corresponds to time points and followins columns to argument values per variable. |
time |
Array with time points where data was taken. length(time) == ncol(data) |
rng |
an array of length 2 containing the lower and upper boundaries for the rangeval of argument values |
type |
Type of basisfd for smoothing the mean estimate function. "bspline", "fourier", "exp", "const" or "mon" |
nbasis |
An integer variable specifying the number of basis functions |
knots |
a vector specifying the break points if type == "bspline" |
norder |
an integer specifying the order of b-splines if type == "bspline" |
lambda |
a nonnegative real number specifying the amount of smoothing to be applied to the estimated functional parameter |
a functional data object containing a smooth of the mean.
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