Package: pooling 1.1.2

pooling: Fit Poolwise Regression Models

Functions for calculating power and fitting regression models in studies where a biomarker is measured in "pooled" samples rather than for each individual. Approaches for handling measurement error follow the framework of Schisterman et al. (2010) <doi:10.1002/sim.3823>.

Authors:Dane R. Van Domelen

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pooling.pdf |pooling.html✨
pooling/json (API)

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

Peer review:

Bug tracker:https://github.com/vandomed/pooling/issues

Datasets:
  • dat_cond_logreg - Dataset for Examples in cond_logreg
  • dat_p_gdfa - Dataset for Examples in p_gdfa
  • dat_p_linreg_yerrors - Dataset for Examples in p_linreg_yerrors
  • dat_p_ndfa - Dataset for Examples in p_ndfa
  • pdat1 - Dataset for Examples in p_dfa_xerrors and p_logreg_xerrors
  • pdat2 - Dataset for Examples in p_dfa_xerrors2 and p_logreg_xerrors2
  • simdata - Dataset for a Paper Under Review

On CRAN:

assay-modelingbiomarkersefficiencyepidemiologymaximum-likelihoodmeasurement-errorpooling

3.59 score 78 scripts 200 downloads 23 exports 94 dependencies

Last updated 5 years agofrom:45132212f4. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winNOTENov 15 2024
R-4.5-linuxNOTENov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:cond_logregform_poolsp_dfa_xerrorsp_dfa_xerrors2p_gdfap_gdfa_constantp_gdfa_nonconstantp_linreg_yerrorsp_logregp_logreg_xerrorsp_logreg_xerrors2p_ndfap_ndfa_constantp_ndfa_nonconstantplot_dfaplot_dfa2plot_gdfaplot_ndfapoolcost_tpoolcushion_tpoolpower_tpoolvar_ttest_pe

Dependencies:base64encbitopsbslibcachemclicolorspacecommonmarkcpp11crayoncubaturedata.tabledatawizardDBIdigestdplyrdvmiscevaluatefansifarverfastmapfontawesomefsgenericsggplot2ggrepelgluegtablehighrhtmltoolshttpuvinsightisobandjquerylibjsonlitekableExtraknitrlabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimeminqamitoolsmunsellmvtnormnlmenumDerivpillarpkgconfigpracmapromisespurrrR6rappdirsrattlerbenchmarkRColorBrewerRcppRcppArmadillorlangrmarkdownrpartrpart.plotrstudioapisassscalesshinysjlabelledsourcetoolsstringistringrsurveysurvivalsvglitesystemfontstabtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunXMLxml2xtableyaml

Readme and manuals

Help Manual

Help pageTopics
Conditional Logistic Regression with Measurement Error in One Covariatecond_logreg
Dataset for Examples in cond_logregdat_cond_logreg
Dataset for Examples in p_gdfadat_p_gdfa
Dataset for Examples in p_linreg_yerrorsdat_p_linreg_yerrors
Dataset for Examples in p_ndfadat_p_ndfa
Created a Pooled Dataset from a Subject-Specific Oneform_pools
Discriminant Function Approach for Estimating Odds Ratio with Normal Exposure Measured in Pools and Potentially Subject to Errorsp_dfa_xerrors
Discriminant Function Approach for Estimating Odds Ratio with Gamma Exposure Measured in Pools and Potentially Subject to Errorsp_dfa_xerrors2
Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errorsp_gdfa
Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errors (Constant Odds Ratio Version)p_gdfa_constant
Gamma Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Multiplicative Lognormal Errors (Non-constant Odds Ratio Version)p_gdfa_nonconstant
Linear Regression of Y vs. Covariates with Y Measured in Pools and (Potentially) Subject to Additive Normal Errorsp_linreg_yerrors
Poolwise Logistic Regressionp_logreg
Poolwise Logistic Regression with Normal Exposure Subject to Errorsp_logreg_xerrors
Poolwise Logistic Regression with Gamma Exposure Subject to Errorsp_logreg_xerrors2
Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errorsp_ndfa
Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errors (Constant Odds Ratio Version)p_ndfa_constant
Normal Discriminant Function Approach for Estimating Odds Ratio with Exposure Measured in Pools and Potentially Subject to Additive Normal Errors (Non-constant Odds Ratio Version)p_ndfa_nonconstant
Dataset for Examples in p_dfa_xerrors and p_logreg_xerrorspdat1
Dataset for Examples in p_dfa_xerrors2 and p_logreg_xerrors2pdat2
Plot Log-OR vs. X for Normal Discriminant Function Approachplot_dfa
Plot Log-OR vs. X for Gamma Discriminant Function Approachplot_dfa2
Plot Log-OR vs. X for Gamma Discriminant Function Approachplot_gdfa
Plot Log-OR vs. X for Normal Discriminant Function Approachplot_ndfa
Visualize Total Costs for Pooling Design as a Function of Pool Sizepoolcost_t
Visualize T-test Power for Pooling Design as Function of Processing Error Variancepoolcushion_t
Fit Poolwise Regression Modelspooling-package pooling
Visualize T-test Power for Pooling Designpoolpower_t
Visualize Ratio of Variance of Each Pooled Measurement to Variance of Each Unpooled Measurement as Function of Pool Sizepoolvar_t
Dataset for a Paper Under Reviewsimdata
Test for Underestimated Processing Error Variance in Pooling Studiestest_pe