fda (version 1.2.3)

fRegress.stderr: Compute Standard errors of Coefficient Functions Estimated by Functional Regression Analysis

Description

Function fRegress carries out a functional regression analysis of the concurrent kind, and estimates a regression coefficient function corresponding to each independent variable, whether it is scalar or functional. This function uses the list that is output by fRegress to provide standard error functions for each regression function. These standard error functions are pointwise, meaning that sampling standard deviation functions only are computed, and not sampling covariances.

Usage

fRegress.stderr(fRegressList, y2cMap, SigmaE)

Arguments

fRegressList

the named list of length six that is returned from a call to function fRegress.

y2cMap

a matrix that contains the linear transformation that takes the raw data values into the coefficients defining a smooth functional data object. Typically, this matrix is returned from a call to function smooth.basis that generates the depende

SigmaE

either a matrix or a bivariate functional data object according to whether the dependent variable is scalar or functional, respectively. This object has a number of replications equal to the length of the dependent variable object. It contains an estimat

Value

• a named list of length 3 containing:
• betastderrlist
  a list object of length the number of independent variables. Each member contains a functional parameter object for the standard error of a regression function.
• bvar
  a symmetric matrix containing sampling variances and covariances for the matrix of regression coefficients for the regression functions. These are stored column-wise in defining BVARIANCE.
• c2bMap
  a matrix containing the mapping from response variable coefficients to coefficients for regression coefficients.

See Also

fRegress, fRegress.CV

Examples

#See the weather data analyses in the file \code{daily.ssc} for
#examples of the use of function \code{fRegress.stderr}.