Diagnostic Tools for Logistic and Conditional Logistic Regression


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Documentation for package ‘CLRtools’ version 0.1.0

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check_coef_change Assess Coefficient Change After Variable Removal
check_coef_significant Check Significance of Excluded Variables
check_interactions Check Pairwise Interactions in Logistic Regression
coeff.OR Compute Odds Ratios for Logistic and Conditional Logistic Regression Models
compare_bayesm Posterior Predictive Check for Multiple Bayesian Models
compare_bayesm_by_predictor Compare Bayesian Models by Predictor Using Posterior Predictive Simulations
compare_models_loo Compare Bayesian Models Using PSIS-LOO
confidence.interval Compute Wald-Based Confidence Intervals for Logit and Predicted Probability
cov.patterns Extract Unique Covariate Patterns from a Logistic Regression Model
cutpoints Table with Sensitivity and Specificity at Different Cutpoints
delta.coefficient Delta-beta hat percentage: Change in Coefficients when Adding a Variable
diagnosticplots_class Diagnostic Plots for Model Discrimination
diagnostic_bayes Generate MCMC Diagnostic Plots for a Bayesian Model
discordant.pairs Count Discordant Pairs in Matched Case-Control Data
DRtest Deviance Residuals Test (HL Test)
glow11m GLOW11M dataset
glow500 GLOW500 dataset
logit_prob_plot Plot Predicted Probabilities from a Logistic Model
osius_rojek Osius and Rojek Goodness-of-Fit Test for Logistic Regression
rcv_measures Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J < n
residuals_clog Model Diagnostic for Conditional Logistic Regression
residuals_logistic Model Diagnostic for Logistic Regression Models
r_measures Model Fit Evaluation: R^2-like Measures for Logistic Regression Models with J=n
stukels_test Stukel’s Test for Logistic Regression Model Fit
summarize_results Summarize Bayesian Logistic Regression Model Results
univariable.clogmodels Univariable Conditional Logistic Regression Models
univariable.models Univariable Logistic Regression Summary Table