R-universe search: "errors-in-variables"

Showing 9 of total 9 results (show query)

jbytecode

eive:An Algorithm for Reducing Errors-in-Variable Bias in Simple and Multiple Linear Regressions

Performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.

Maintained by Mehmet Hakan Satman. Last updated 2 years ago.

compact-genetic-algorithm errors-in-variables linear-regression cpp

44.6 match 1 stars 2.70 score 6 scripts

osorensen

hdme:High-Dimensional Regression with Measurement Error

Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

Maintained by Oystein Sorensen. Last updated 2 years ago.

cpp

11.0 match 8 stars 5.08 score 30 scripts

maeveupton

reslr:Modelling Relative Sea Level Data

The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, temporal splines (Upton et al 2023) <arXiv:2301.09556>, spatio-temporal splines (Upton et al 2023) <arXiv:2301.09556> and generalised additive models (Upton et al 2023) <arXiv:2301.09556>. This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models.

Maintained by Maeve Upton. Last updated 1 years ago.

jags cpp

9.7 match 4 stars 5.23 score 28 scripts

r-forge

GPArotation:GPA Factor Rotation

Gradient Projection Algorithm Rotation for Factor Analysis. See '?GPArotation.Intro' for more details.

Maintained by Paul Gilbert. Last updated 2 months ago.

4.0 match 1 stars 12.66 score 1.1k scripts 362 dependents

jrlockwood

eivtools:Measurement Error Modeling Tools

This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression. It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.

Maintained by J.R. Lockwood. Last updated 3 years ago.

jags cpp

11.5 match 2.26 score 18 scripts

davidsleonard

leiv:Bivariate Linear Errors-In-Variables Estimation

Estimate the slope and intercept of a bivariate linear relationship by calculating a posterior density that is invariant to interchange and scaling of the coordinates.

Maintained by David Leonard. Last updated 10 years ago.

9.6 match 2.00 score 3 scripts

cran

BivRegBLS:Tolerance Interval and EIV Regression - Method Comparison Studies

Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.

Maintained by Bernard G Francq. Last updated 5 years ago.

6.7 match 1.63 score 43 scripts

pasturm

bfsl:Best-Fit Straight Line

How to fit a straight line through a set of points with errors in both coordinates? The 'bfsl' package implements the York regression (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the intercept, slope and standard errors for the best-fit straight line to independent points with (possibly correlated) normally distributed errors in both x and y. Other commonly used errors-in-variables methods, such as orthogonal distance regression, geometric mean regression or Deming regression are special cases of the 'bfsl' solution.

Maintained by Patrick Sturm. Last updated 3 years ago.

1.5 match 3 stars 3.18 score 10 scripts

johnlawrence1

SurvDisc:Discrete Time Survival and Longitudinal Data Analysis

Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.

Maintained by John Lawrence. Last updated 7 years ago.

1.3 match 1.00 score 6 scripts