refund v0.1-23
0
Monthly downloads
Regression with Functional Data
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.
Functions in refund
| Name | Description | |
| DTI | Diffusion Tensor Imaging: tract profiles and outcomes | |
| Predict.matrix.pcre.random.effect | mgcv-style constructor for prediction of PC-basis functional random effects | |
| Predict.matrix.fpc.smooth | mgcv-style constructor for prediction of FPC terms | |
| af | Construct an FGAM regression term | |
| DTI2 | Diffusion Tensor Imaging: more fractional anisotropy profiles and outcomes | |
| Xt_siginv_X | Internal computation function | |
| Predict.matrix.pi.smooth | Predict.matrix method for pi basis | |
| Predict.matrix.peer.smooth | mgcv-style constructor for prediction of PEER terms | |
| PEER.Sim | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function | |
| Predict.matrix.dt.smooth | Predict.matrix method for dt basis | |
| coef.pffr | Get estimated coefficients from a pffr fit | |
| cmdscale_lanczos | Faster multi-dimensional scaling | |
| cd4 | Observed CD4 cell counts | |
| af_old | Construct an FGAM regression term | |
| amc | Additive model with constraints | |
| expand.call | Return call with all possible arguments | |
| f_sum | Sum computation 1 | |
| fbps | Sandwich smoother for matrix data | |
| f_trace | Trace computation | |
| bayes_fosr | Bayesian Function-on-scalar regression | |
| ffpcplot | Plot PC-based function-on-function regression terms | |
| ccb.fpc | Corrected confidence bands using functional principal components | |
| fgam | Functional Generalized Additive Models | |
| fosr2s | Two-step function-on-scalar regression | |
| create.prep.func | Construct a function for preprocessing functional predictors | |
| coefficients.pfr | Extract coefficient functions from a fitted pfr-object | |
| fosr.vs | Function-on Scalar Regression with variable selection | |
| fpca.lfda | Longitudinal Functional Data Analysis using FPCA | |
| fpca.sc | Functional principal components analysis by smoothed covariance | |
| coefboot.pffr | Simple bootstrap CIs for pffr | |
| ffpc | Construct a PC-based function-on-function regression term | |
| ff | Construct a function-on-function regression term | |
| f_sum2 | Sum computation 2 | |
| fosr.perm | Permutation testing for function-on-scalar regression | |
| fosr | Function-on-scalar regression | |
| lf | Construct an FLM regression term | |
| lf.vd | Construct a VDFR regression term | |
| fpca2s | Functional principal component analysis by a two-stage method | |
| gibbs_mult_fpca | Multilevel FoSR using a Gibbs sampler and FPCA | |
| gibbs_cs_wish | Cross-sectional FoSR using a Gibbs sampler and Wishart prior | |
| fpca.ssvd | Smoothed FPCA via iterative penalized rank one SVDs. | |
| getTF | Get recognized transformation function | |
| gibbs_cs_fpca | Cross-sectional FoSR using a Gibbs sampler and FPCA | |
| fpca.face | Functional principal component analysis with fast covariance estimation | |
| fpc | Construct a FPC regression term | |
| pcre | pffr-constructor for functional principal component-based functional random intercepts. | |
| peer | Construct a PEER regression term in a pfr formula | |
| lpeer | Longitudinal Functional Models with Structured Penalties | |
| gasoline | Octane numbers and NIR spectra of gasoline | |
| fpcr | Functional principal component regression | |
| mfpca.sc | Multilevel functional principal components analysis by smoothed covariance | |
| lf_old | Construct an FLM regression term | |
| pffr.check | Some diagnostics for a fitted pffr model | |
| plot.pfr | Plot a pfr object | |
| model.matrix.pffr | Obtain model matrix for a pffr fit | |
| predict.fbps | Prediction for fast bivariate P-spline (fbps) | |
| pffrGLS | Penalized function-on-function regression with non-i.i.d. residuals | |
| ols_cs | Cross-sectional FoSR using GLS | |
| pco_predict_preprocess | Make predictions using pco basis terms | |
| plot.fosr.vs | Plot for Function-on Scalar Regression with variable selection | |
| plot.fosr | Default plotting of function-on-scalar regression objects | |
| plot.fpcr | Default plotting for functional principal component regression output | |
| 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 | |
| smooth.construct.fpc.smooth.spec | Basis constructor for FPC terms | |
| smooth.construct.pco.smooth.spec | Principal coordinate ridge regression | |
| f_sum4 | Sum computation 2 | |
| lpfr | Longitudinal penalized functional regression | |
| qq.pffr | QQ plots for pffr model residuals | |
| smooth.construct.pcre.smooth.spec | mgcv-style constructor for PC-basis functional random effects | |
| pwcv | Pointwise cross-validation for function-on-scalar regression | |
| vb_mult_wish | Multilevel FoSR using Variational Bayes and Wishart prior | |
| plot.lpeer | Plotting of estimated regression functions obtained through lpeer() | |
| vis.pfr | Visualization of PFR objects | |
| smooth.construct.peer.smooth.spec | Basis constructor for PEER terms | |
| pfr_plot.gam | Local version of plot.gam | |
| predict.fosr.vs | Prediction for Function-on Scalar Regression with variable selection | |
| pfr_old | Penalized Functional Regression (old version) | |
| vis.fgam | Visualization of FGAM objects | |
| sff | Construct a smooth function-on-function regression term | |
| quadWeights | Compute quadrature weights | |
| summary.pfr | Summary for a pfr fit | |
| smooth.construct.dt.smooth.spec | Domain Transformation basis constructor | |
| predict.pffr | Prediction for penalized function-on-function regression | |
| re | Random effects constructor for fgam | |
| lofocv | Leave-one-function-out cross-validation | |
| gls_cs | Cross-sectional FoSR using GLS | |
| gibbs_mult_wish | Multilevel FoSR using a Gibbs sampler and Wishart prior | |
| vb_cs_fpca | Cross-sectional FoSR using Variational Bayes and FPCA | |
| peer_old | Functional Models with Structured Penalties | |
| pfr | Penalized Functional Regression | |
| plot.pffr | Plot a pffr fit | |
| pffrSim | Simulate example data for pffr | |
| refund-internal | Internal functions for the refund package | |
| plot.peer | Plotting of estimated regression functions obtained through peer() | |
| refund-package | Regression with Functional Data | |
| pffr | Penalized flexible functional regression | |
| predict.fgam | Prediction from a fitted FGAM model | |
| predict.pfr | Prediction from a fitted pfr model | |
| predict.fosr | Prediction from a fitted bayes_fosr model | |
| sofa | SOFA (Sequential Organ Failure Assessment) Data | |
| vb_cs_wish | Cross-sectional FoSR using Variational Bayes and Wishart prior | |
| summary.pffr | Summary for a pffr fit | |
| vb_mult_fpca | Multilevel FoSR using Variational Bayes and FPCA | |
| smooth.construct.pi.smooth.spec | Parametric Interaction basis constructor | |
| print.summary.pffr | Print method for summary of a pffr fit | |
| smooth.construct.pss.smooth.spec | P-spline constructor with modified 'shrinkage' penalty | |
| No Results! | ||
Last month downloads
Details
| Type | Package |
| Date | 2020-12-03 |
| License | GPL (>= 2) |
| LazyLoad | yes |
| LazyData | true |
| Repository | CRAN |
| Collate | 'Omegas.R' 'af.R' 'af_old.R' 'amc.R' 'ccb.fpc.R' 'create.prep.func.R' 'coefficients.pfr.R' 'dt_basis.R' 'irreg2mat.R' 'fbps.R' 'fgam.R' 'fosr.R' 'fosr.perm.R' 'fosr.perm.fit.R' 'fosr.perm.test.R' 'fosr.vs.R' 'fosr2s.R' 'fpc.R' 'fpca2s.R' 'fpca.sc.R' 'fpca.face.R' 'fpca.ssvd.R' 'fpcr.R' 'fpcr.setup.R' 'lf.R' 'lf_old.R' 'lf.vd.R' 'lofocv.R' 'lpeer.R' 'lpfr.R' 'quadWeights.R' 'lw.test.R' 'osplinepen2d.R' 'parse.predict.pfr.R' 'peer.R' 'peer_old.R' 'pffr-ff.R' 'pffr-ffpc.R' 'pffr-methods.R' 'pffr-pcre.R' 'pffr-robust.R' 'pffr-sff.R' 'pffr-utilities.R' 'pffr.R' 'pfr.R' 'pfr_old.R' 'pi_basis.R' 'plot.fosr.R' 'plot.fosr.perm.R' 'plot.fosr.vs.R' 'plot.fpcr.R' 'plot.lpeer.R' 'plot.peer.R' 'plot.pfr.R' 'poridge.R' 'postprocess.pfr.R' 'predict.fgam.R' 'predict.fosr.R' 'predict.pfr.R' 'predict.pfr_old.R' 'preprocess.pfr.R' 'pspline.setting.R' 'pwcv.R' 'summary.pfr.R' 're.R' 'rlrt.pfr.R' 'vis.fgam.R' 'predict.fosr.vs.R' 'CD4-data.R' 'DTI-data.R' 'DTI2-data.R' 'PEER.Sim-data.R' 'gasoline-data.R' 'vis.pfr.R' 'GLS_CS.R' 'Gibbs_CS_FPCA.R' 'Gibbs_CS_Wish.R' 'Gibbs_Mult_FPCA.R' 'Gibbs_Mult_Wish.R' 'OLS_CS.R' 'VB_CS_FPCA.R' 'VB_CS_Wish.R' 'VB_Mult_FPCA.R' 'VB_Mult_Wish.R' 'XtSiginvX.R' 'bayes_fosr.R' 'f_sum.R' 'f_sum2.R' 'f_sum4.R' 'f_trace.R' 'mfpca.sc.R' 'fpca.lfda.R' 'predict.fbps.R' 'select_knots.R' |
| RoxygenNote | 7.1.1 |
| NeedsCompilation | no |
| Packaged | 2020-12-04 14:36:34 UTC; juliawrobel |
| Date/Publication | 2020-12-04 16:00:03 UTC |
| imports | boot , fda , gamm4 , ggplot2 , grpreg , lattice , lme4 , magic , MASS , Matrix , methods , mgcv (>= 1.8-23) , nlme , pbs , RLRsim , splines , stats |
| depends | R (>= 3.00.0) |
| suggests | RColorBrewer , reshape2 , testthat |
| Contributors | Sonja Greven, Jaroslaw Harezlak, Mathew W McLean, David Lawrence Miller, Jeff Goldsmith, Philip Reiss, Lan Huo, Fabian Scheipl, Bruce Swihart, Luo Xiao, Lei Huang, Ciprian Crainiceanu, Jonathan Gellar, So Young Park, Ana-Maria Staicu, Madan Kundu, Yakuan Chen, Chongzhi Di |
Include our badge in your README
[](http://www.rdocumentation.org/packages/refund)