allestimates: Effect estimates from models with all possible combinations...

In allestimates: Effect Estimates from All Models

Effect estimates from models with all possible combinations of variables

Description

To assess treatment effects in clinical trials and risk factors in bio-medical and epidemiological research, we use regression coefficients, odds ratios or hazard ratios as effect estimates. allestimates allows users to quickly obtain effect estimates from models with all possible combinations of a list of variables specified by users. all_lm for linear regression, all_glm for logistic regression, all_speedglm using speedlm as a faster alternative of all_glm, and all_cox for Cox Proportional Hazards Models. Users can further use those values in a returned list of results. all_plot draws scatter plots with all effect estimate values against p values, as Stata confall command (Wang Z (2007) <doi:10.1177/1536867X0700700203>). Those plots divide estimates into four categories:

Details

• positive and significant: left-top quarter

• negative and significant: left-bottom quarter

• positive and non-significant: right-top quarter

• negative and non-significant: right-bottom quarter

all_plot2 draws multiple plots. Each of those plots indicates whether a specific variable is included or not included in models. Those effect estimates help users better understand confounding effects, uncertainty of their estimates, as well as inappropriately including variables in the models. This is a tool for calculating and exploring effect estimates from all possible models. Interpretation of the results should be in the context of other analyses and biological knowledge.

Examples

? all_speedglm
? all_glm
? all_cox
? all_lm
? all_plot
? all_plot2

allestimates documentation built on March 31, 2023, 5:28 p.m.