my.samp.cov2d: compute sample covariance for functional data

Description Usage Arguments Details Value

Description

compute sample covariance for functional data

Usage

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my.samp.cov2d(time, x, subject, wt, marginal.knots, n.marginal.knots,
  centered = TRUE, noDiag = TRUE)

Arguments

time

vector of points on the 'time' axis

x

vector of functional observations

subject

vector of integer IDs for each curve

wt

vector of weights. These weights are not used to calculate anything in this function. The wt argument here is necessary for 'bookeeping' purposes for covariance function estimation methods that utilize these weights.

marginal.knots

vector of knot locations on the 'time' (i.e. marginal) axis.

n.marginal.knots

integer specifying the number of knot locations to use on the marginal domain

centered

logical. If FALSE a smoothing spline fit will be computed to estimate the mean functions, and this mean funciton will be subtracted from each curve.

noDiag

logical. If TRUE the diagonal elements of th estimated covariance matrix will be removed.

Details

These weights argument wt is not used to calculate anything in this function. The wt argument here is necessary for 'bookeeping' purposes for covariance function estimation methods that utilize these weights.

Value

A data frame with the sample covariances. The knot locations are included at the end of the data frame.


dan410/sseigfun documentation built on May 14, 2019, 3:34 p.m.