Package: MECfda 0.1.0

MECfda: Scalar-on-Function Regression with Measurement Error Correction

Solve scalar-on-function linear models, including generalized linear mixed effect model and quantile linear regression model, and bias correction estimation methods due to measurement error. Details about the measurement error bias correction methods, see Luan et al. (2023) <doi:10.48550/arXiv.2305.12624>, Tekwe et al. (2022) <doi:10.1093/biostatistics/kxac017>, Zhang et al. (2023) <doi:10.5705/ss.202021.0246>, Tekwe et al. (2019) <doi:10.1002/sim.8179>.

Authors:Heyang Ji [aut, cre, ctb, dtc], Ufuk Beyaztas [ctb, rev], Nicolas Escobar-Velasquez [com], Yuanyuan Luan [ctb], Xiwei Chen [ctb], Mengli Zhang [ctb], Roger Zoh [ths], Lan Xue [ths], Carmen Tekwe [ths]

MECfda_0.1.0.tar.gz
MECfda_0.1.0.zip(r-4.5)MECfda_0.1.0.zip(r-4.4)MECfda_0.1.0.zip(r-4.3)
MECfda_0.1.0.tgz(r-4.4-any)MECfda_0.1.0.tgz(r-4.3-any)
MECfda_0.1.0.tar.gz(r-4.5-noble)MECfda_0.1.0.tar.gz(r-4.4-noble)
MECfda_0.1.0.tgz(r-4.4-emscripten)MECfda_0.1.0.tgz(r-4.3-emscripten)
MECfda.pdf |MECfda.html✨
MECfda/json (API)

# Install 'MECfda' in R:
install.packages('MECfda', repos = c('https://jihx1015.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jihx1015/mecfda/issues

Datasets:

On CRAN:

3.90 score 1 scripts 57 downloads 19 exports 67 dependencies

Last updated 28 days agofrom:6de3f6cfe3. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winOKOct 25 2024
R-4.5-linuxOKOct 25 2024
R-4.4-winOKOct 25 2024
R-4.4-macOKOct 25 2024
R-4.3-winOKOct 25 2024
R-4.3-macOKOct 25 2024

Exports:%>%basis2funbspline_basisbspline_basis_expansionbspline_seriesbsplineSeries2funextractCoeffc.betafcQRfcRegressionfourier_basis_expansionFourier_seriesFourierSeries2funfunctional_variableME.fcLR_IVME.fcQR_CLSME.fcQR_IV.SIMEXME.fcRegression_MEMplot

Dependencies:ashbitopsbootcliclustercolorspacecorpcordeSolvedplyrfansifarverfdafdsFNNgenericsggplot2glmegluegssgtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelme4locfitmagrittrMASSMatrixMatrixModelsmclustmgcvminqamulticoolmunsellmvtnormnlmenloptrpcaPPpillarpkgconfigplyrpracmaquantregR6rainbowRColorBrewerRcppRcppEigenRCurlreshaperlangscalesSparseMstringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr

MECfda: An R package for bias correction due to measurement error in functional and scalar covariates in scalar-on-function regression models

Ji, Heyang, Beyaztas, Ufuk, Escobar, Nicolas, Luan, Yuanyuan, Chen, Xiwei, Zhang, Mengli, Zoh, Roger, Xue, Lan, Tekwe, Carmen

Rendered fromMECfda.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-10-25
Started: 2024-06-29

Readme and manuals

Help Manual

Help pageTopics
From the summation series of a functional basis to function valuebasis2fun basis2fun,bspline_series,numeric-method basis2fun,Fourier_series,numeric-method
B-splines basis expansion for functional variable databspline_basis_expansion bspline_basis_expansion,functional_variable,integer-method
b-spline basisbspline_basis bspline_basis-class
b-splines summation series.bspline_series bspline_series-class
Compute the value of the Fourier summation series at certain points.bsplineSeries2fun bsplineSeries2fun,bspline_series,numeric-method
Extract dimensionality of functional data.dim,functional_variable-method
Method of class Fourier_series to extract Fourier coefficientsextractCoef extractCoef,Fourier_series-method
Extract the value of coefficient parameter functionfc.beta fc.beta,fcQR-method fc.beta,fcRegression-method
Solve quantile regression models with functional covariate(s).fcQR
Solve linear models with functional covariate(s)fcRegression
Fourier basis expansion for functional variable datafourier_basis_expansion fourier_basis_expansion,functional_variable,integer-method
s4 class of Fourier summation seriesFourier_series Fourier_series-class
Compute the value of the Fourier summation seriesFourierSeries2fun FourierSeries2fun,Fourier_series,numeric-method
Function-valued variable data.functional_variable functional_variable-class
Bias correction method of applying linear regression to one functional covariate with measurement error using instrumental variable.ME.fcLR_IV
Bias correction method of applying quantile linear regression to dataset with one functional covariate with measurement error using corrected loss score method.ME.fcQR_CLS
Bias correction method of applying quantile linear regression to dataset with one functional covariate with measurement error using instrumental variable.ME.fcQR_IV.SIMEX
Use UP_MEM or MP_MEM substitution to apply (generalized) linear regression with one functional covariate with measurement error.ME.fcRegression_MEM
Simulated dataMECfda.data.sim.0.0
Simulated dataMECfda.data.sim.0.1
Simulated dataMECfda.data.sim.0.2
Simulated dataMECfda.data.sim.0.3
Simulated dataMECfda.data.sim.1.0
Simulated dataMECfda.data.sim.1.1
Simulated dataMECfda.data.sim.1.2
Simulated dataMECfda.data.sim.1.3
Plot b-splines baisi summation series.plot,bspline_series-method
Plot Fourier basis summation series.plot,Fourier_series-method
Predicted values based on fcQR objectpredict.fcQR
Predicted values based on fcRegression objectpredict.fcRegression