Progression Models for Repeated Measures
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Documentation for package ‘pmrm’ version 0.0.2
Help Pages
| AIC.pmrm_fit | Akaike information criterion (AIC) |
| BIC.pmrm_fit | Bayesian information criterion (BIC) |
| coef.pmrm_fit | Treatment effect parameters |
| confint.pmrm_fit | Confidence intervals of parameters |
| deviance.pmrm_fit | Deviance |
| fitted.pmrm_fit | Fitted values |
| glance.pmrm_fit | Glance at a PMRM. |
| logLik.pmrm_fit | Extract the log likelihood. |
| plot.pmrm_fit | Plot a fitted PMRM. |
| pmrm_estimates | Parameter estimates and confidence intervals |
| pmrm_marginals | Marginal means |
| pmrm_model_decline_nonproportional | Fit the non-proportional decline model. |
| pmrm_model_decline_proportional | Fit the proportional decline model. |
| pmrm_model_slowing_nonproportional | Fit the non-proportional slowing model. |
| pmrm_model_slowing_proportional | Fit the proportional slowing model. |
| pmrm_simulate_decline_nonproportional | Simulate non-proportional decline model. |
| pmrm_simulate_decline_proportional | Simulate proportional decline model. |
| pmrm_simulate_slowing_nonproportional | Simulate non-proportional slowing model. |
| pmrm_simulate_slowing_proportional | Simulate proportional slowing model. |
| predict.pmrm_fit | Predict new outcomes |
| print.pmrm_fit | Print a fitted PMRM. |
| residuals.pmrm_fit | 'pmrm' residuals. |
| summary.pmrm_fit | Summarize a PMRM. |
| tidy.pmrm_fit | Tidy a fitted PMRM. |
| VarCorr.pmrm_fit | Estimated covariance matrix |
| vcov.pmrm_fit | Treatment effect parameter covariance matrix |