This is a work-in-progress to explore how to design Stata-like regression modelling tools for R, namely those that allow plug-and-play variance-covariance estimation procedures and also to provide arguments to modelling functions in data
-formula
order (rather than the traditional formula
-data
order) thus enabling easy use in data analysis pipelines via %>%
.
Contributions and feedback are welcome on GitHub.
options(width = 120) knitr::opts_chunk$set(comment = "", warning = FALSE, message = FALSE, echo = TRUE, tidy = TRUE, size="small", fig.width = 10, fig.height = 10)
In addition to plug-and-play variance-covariance procedures, the reg()
function also provides pretty print methods.
library("reggie") # reg reg(ChickWeight, weight ~ Time + Diet) # reg reg(ChickWeight, weight ~ Time + Diet, vcov_type = "const") # reg, vce(robust) reg(ChickWeight, weight ~ Time + Diet, vcov_type = "HC0") # reg, vce(boot) reg(ChickWeight, weight ~ Time + Diet, vcov_type = "boot") # reg, vce(cluster Chick) reg(ChickWeight, weight ~ Time + Diet, vcov_cluster = ~ Chick) # bootstrap, cluster(Chick) reps(5000): reg #reg(ChickWeight, weight ~ Time + Diet, vcov_cluster = ~ Chick, vcov_type = "boot") # DOESN'T CURRENTLY WORK, BUT WHY? # svy: reg library("survey") data(api) dstrat <- svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) reg(dstrat, api00 ~ ell + meals + mobility)
The "model" object class contains the underlying model object as its model
argument, and methods for various commonly used generic functions (coef()
, vcov()
, plot()
, terms()
, predict()
) are provided that behave like those operations on a standard modelling object.
This package is not yet on CRAN. To install the latest development version you can pull a potentially unstable version directly from GitHub:
if (!require("remotes")) { install.packages("remotes") } remotes::install_github("leeper/reggie")
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