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refund (version 0.1-9)

Regression with Functional Data

Description

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

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Version

Install

install.packages('refund')

Monthly Downloads

4,424

Version

0.1-9

License

GPL (>= 2)

Maintainer

Lei Huang

Last Published

December 6th, 2013

Functions in refund (0.1-9)

Additive model with constraints

Sandwich smoother for matrix data

Diffusion Tensor Imaging: more fractional anisotropy profiles and outcomes

Likelihood Ratio Test and Restricted Likelihood Ratio Test for inference of functional predictors

Permutation testing for function-on-scalar regression

Penalized function-on-function regression with non-i.i.d. residuals

Obtain fitted values for a pffr fit

model.matrix.pffr

Obtain model matrix for a pffr fit

Construct an FGAM regression term

Two-step function-on-scalar regression

Simple bootstrap CIs for pffr

Construct a smooth function-on-function regression term

Permutation test for wavelet-domain scalar-on-function regression

Default plotting of function-on-scalar regression objects

Default plotting for wavelet-domain scalar-on-function regression

Get estimated coefficients from a pffr fit

Principal component regression and partial least squares in the wavelet domain

Smoothed FPCA via iterative penalized rank one SVDs.

Prediction method for generalized elastic net in the wavelet domain

Functional principal component analysis with fast covariance estimation

smooth.construct.pcre.smooth.spec

mgcv-style constructor for PC-basis functional random effects

print.summary.pffr

Print method for summary of a pffr fit

Pointwise cross-validation for function-on-scalar regression

Regression with Functional Data

Functional Models with Structured Penalties

Plot PC-based function-on-function regression terms

pffr-constructor for functional principal component-based functional random effects.

Diffusion Tensor Imaging: tract profiles and outcomes

Longitudinal penalized functional regression

Construct an FLM regression term

Prediction from a fitted FGAM model

Functional principal component analysis by a two-stage method

Corrected confidence bands using functional principal components

Plotting of estimated regression functions obtained through peer()

Plotting of estimated regression functions obtained through lpeer()

Penalized functional regression and Longitudinal penalized functional regression

Generalized elastic net in the wavelet domain

Plot a pffr fit

Construct a PC-based function-on-function regression term

Observed CD4 cell counts

Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function

Prediction for penalized function-on-function regression

Summary for a pffr fit

Leave-one-function-out cross-validation

Default plotting for functional principal component regression output

Return call with all possible arguments

Functional Generalized Additive Models

Construct a function-on-function regression term

Prediction for penalized functional regression

refund-internal

Internal functions for the refund package

Obtain residuals for a pffr fit

Function-on-scalar regression

Functional principal components analysis by smoothed covariance

Longitudinal Functional Models with Structured Penalties

Visualization of FGAM objects

Penalized function-on-function regression

Octane numbers and NIR spectra of gasoline

Simulate example data for pffr

smooth.construct.pss.smooth.spec

P-spline constructor with modified 'shrinkage' penalty

Functional principal component regression