Fit Poolwise Regression Models

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Documentation for package ‘pooling’ version 1.1.1

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pooling-package Fit Poolwise Regression Models
dat1 Dataset with Simulated (Y, C) Values for Examples in dfa_xerrors and logreg_xerrors
dat1_xtilde Dataset with Simulated Xtilde Values for Examples in dfa_xerrors and logreg_xerrors
dfa_xerrors Discriminant Function Approach for Estimating Odds Ratio with Normal Exposure Subject to Measurement Error
gamma_constantscale Fit Constant-Scale Gamma Model for Y vs. Covariates
lognormal Fit Lognormal Regression for Y vs. Covariates
logreg_xerrors Logistic Regression with Normal Exposure Subject to Errors
pdat1 Dataset with Simulated (Y, Xtilde, C) Values for Examples in p_dfa_xerrors and p_logreg_xerrors
pdat2 Dataset with Simulated (Y, Xtilde) Values for Examples in p_dfa_xerrors2 and p_logreg_xerrors2
pdat2_c Dataset with Simulated C Values for Examples in p_dfa_xerrors2 and p_logreg_xerrors2
plot_dfa Plot Log-OR vs. X for Normal Discriminant Function Approach
plot_dfa2 Plot Log-OR vs. X for Gamma Discriminant Function Approach
poolcost_t Visualize Total Costs for Pooling Design as a Function of Pool Size
pooling Fit Poolwise Regression Models
poolpower_t Visualize T-test Power for Pooling Design
poolvar_t Visualize Ratio of Variance of Each Pooled Measurement to Variance of Each Unpooled Measurement as Function of Pool Size
p_dfa_xerrors Discriminant Function Approach for Estimating Odds Ratio with Normal Exposure Measured in Pools and Subject to Errors
p_dfa_xerrors2 Discriminant Function Approach for Estimating Odds Ratio with Gamma Exposure Measured in Pools and Subject to Errors
p_logreg Poolwise Logistic Regression
p_logreg_xerrors Poolwise Logistic Regression with Normal Exposure Subject to Errors
p_logreg_xerrors2 Poolwise Logistic Regression with Gamma Exposure Subject to Errors
test_pe Test for Underestimated Processing Error Variance in Pooling Studies