| allestimates {allestimates} | R Documentation |
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