Regression with Functional Data
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Documentation for package ‘refund’ version 0.1-35
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A B C D E F G L M O P Q R S V X
-- A --
| af | Construct an FGAM regression term |
| af_old | Construct an FGAM regression term |
-- B --
| bayes_fosr | Bayesian Function-on-scalar regression |
-- C --
| ccb.fpc | Corrected confidence bands using functional principal components |
| cd4 | Observed CD4 cell counts |
| cmdscale_lanczos | Faster multi-dimensional scaling |
| coef.pffr | Get estimated coefficients from a pffr fit |
| coef.pfr | Extract coefficient functions from a fitted pfr-object |
| coefboot.pffr | Simple bootstrap CIs for pffr |
| coefficients.pfr | Extract coefficient functions from a fitted pfr-object |
| content | The CONTENT child growth study |
| COVID19 | The US weekly all-cause mortality and COVID19-associated deaths in 2020 |
| create.prep.func | Construct a function for preprocessing functional predictors |
-- D --
| DTI | Diffusion Tensor Imaging: tract profiles and outcomes |
| DTI2 | Diffusion Tensor Imaging: more fractional anisotropy profiles and outcomes |
-- E --
| expand.call | Return call with all possible arguments |
-- F --
| fbps | Sandwich smoother for matrix data |
| ff | Construct a function-on-function regression term |
| ffpc | Construct a PC-based function-on-function regression term |
| ffpcplot | Plot PC-based function-on-function regression terms |
| fgam | Functional Generalized Additive Models |
| fitted.pffr | Obtain residuals and fitted values for a pffr models |
| fosr | Function-on-scalar regression |
| fosr.perm | Permutation testing for function-on-scalar regression |
| fosr.perm.fit | Permutation testing for function-on-scalar regression |
| fosr.perm.test | Permutation testing for function-on-scalar regression |
| fosr.vs | Function-on Scalar Regression with variable selection |
| fosr2s | Two-step function-on-scalar regression |
| fpc | Construct a FPC regression term |
| fpca.face | Functional principal component analysis with fast covariance estimation |
| fpca.lfda | Longitudinal Functional Data Analysis using FPCA |
| fpca.sc | Functional principal components analysis by smoothed covariance |
| fpca.ssvd | Smoothed FPCA via iterative penalized rank one SVDs. |
| fpca2s | Functional principal component analysis by a two-stage method |
| fpcr | Functional principal component regression |
| f_sum | Sum computation 1 |
| f_sum2 | Sum computation 2 |
| f_sum4 | Sum computation 2 |
| f_trace | Trace computation |
-- G --
| gasoline | Octane numbers and NIR spectra of gasoline |
| gibbs_cs_fpca | Cross-sectional FoSR using a Gibbs sampler and FPCA |
| gibbs_cs_wish | Cross-sectional FoSR using a Gibbs sampler and Wishart prior |
| gibbs_mult_fpca | Multilevel FoSR using a Gibbs sampler and FPCA |
| gibbs_mult_wish | Multilevel FoSR using a Gibbs sampler and Wishart prior |
| gls_cs | Cross-sectional FoSR using GLS |
-- L --
| lf | Construct an FLM regression term |
| lf.vd | Construct a VDFR regression term |
| lf_old | Construct an FLM regression term |
| lpeer | Longitudinal Functional Models with Structured Penalties |
| lpfr | Longitudinal penalized functional regression |
-- M --
| mfpca.face | Multilevel functional principal components analysis with fast covariance estimation |
| mfpca.sc | Multilevel functional principal components analysis by smoothed covariance |
| model.matrix.pffr | Obtain model matrix for a pffr fit |
-- O --
| ols_cs | Cross-sectional FoSR using GLS |
-- P --
| pco | Principal coordinate ridge regression |
| pco_predict_preprocess | Make predictions using pco basis terms |
| pcre | pffr-constructor for functional principal component-based functional random intercepts. |
| peer | Construct a PEER regression term in a 'pfr' formula |
| PEER.Sim | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function |
| peer_old | Functional Models with Structured Penalties |
| pffr | Penalized flexible functional regression |
| pffr.check | Some diagnostics for a fitted pffr model |
| pffrGLS | Penalized function-on-function regression with non-i.i.d. residuals |
| pffrSim | Simulate example data for pffr |
| pfr | Penalized Functional Regression |
| pfr_old | Penalized Functional Regression (old version) |
| plot.fosr | Default plotting of function-on-scalar regression objects |
| plot.fosr.perm | Permutation testing for function-on-scalar regression |
| plot.fosr.vs | Plot for Function-on Scalar Regression with variable selection |
| plot.fpcr | Default plotting for functional principal component regression output |
| plot.lpeer | Plotting of estimated regression functions obtained through 'lpeer()' |
| plot.peer | Plotting of estimated regression functions obtained through 'peer()' |
| plot.pffr | Plot a pffr fit |
| plot.pfr | Plot a pfr object |
| poridge | Principal coordinate ridge regression |
| predict.fbps | Prediction for fast bivariate _P_-spline (fbps) |
| predict.fgam | Prediction from a fitted FGAM model |
| predict.fosr | Prediction from a fitted bayes_fosr model |
| predict.fosr.vs | Prediction for Function-on Scalar Regression with variable selection |
| Predict.matrix.dt.smooth | Predict.matrix method for dt basis |
| Predict.matrix.fpc.smooth | mgcv-style constructor for prediction of FPC terms |
| Predict.matrix.pco.smooth | Principal coordinate ridge regression |
| Predict.matrix.pcre.random.effect | mgcv-style constructor for prediction of PC-basis functional random effects |
| Predict.matrix.peer.smooth | mgcv-style constructor for prediction of PEER terms |
| Predict.matrix.pi.smooth | Predict.matrix method for pi basis |
| predict.pffr | Prediction for penalized function-on-function regression |
| predict.pfr | Prediction from a fitted pfr model |
| print.summary.pffr | Print method for summary of a pffr fit |
| pwcv | Pointwise cross-validation for function-on-scalar regression |
-- Q --
| Q | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function |
| qq.pffr | QQ plots for pffr model residuals |
| quadWeights | Compute quadrature weights |
-- R --
| re | Random effects constructor for fgam |
| residuals.pffr | Obtain residuals and fitted values for a pffr models |
| rlrt.pfr | Likelihood Ratio Test and Restricted Likelihood Ratio Test for inference of functional predictors |
-- S --
| sff | Construct a smooth function-on-function regression term |
| smooth.construct.dt.smooth.spec | Domain Transformation basis constructor |
| smooth.construct.fpc.smooth.spec | Basis constructor for FPC terms |
| smooth.construct.pco.smooth.spec | Principal coordinate ridge regression |
| smooth.construct.pcre.smooth.spec | mgcv-style constructor for PC-basis functional random effects |
| smooth.construct.peer.smooth.spec | Basis constructor for PEER terms |
| smooth.construct.pi.smooth.spec | Parametric Interaction basis constructor |
| smooth.construct.pss.smooth.spec | P-spline constructor with modified 'shrinkage' penalty |
| sofa | SOFA (Sequential Organ Failure Assessment) Data |
| summary.pffr | Summary for a pffr fit |
| summary.pfr | Summary for a pfr fit |
-- V --
| vb_cs_fpca | Cross-sectional FoSR using Variational Bayes and FPCA |
| vb_cs_wish | Cross-sectional FoSR using Variational Bayes and Wishart prior |
| vb_mult_fpca | Multilevel FoSR using Variational Bayes and FPCA |
| vb_mult_wish | Multilevel FoSR using Variational Bayes and Wishart prior |
| vis.fgam | Visualization of FGAM objects |
| vis.pfr | Visualization of PFR objects |
-- X --
| Xt_siginv_X | Internal computation function |