In kota7/outreg: Regression Table for Publication

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

library(outreg)

outreg

Create Regression Tables for Publication

fitlist <- list(lm(mpg ~ cyl, data = mtcars),
                lm(mpg ~ cyl + wt + hp, data = mtcars),
                lm(mpg ~ cyl + wt + hp + drat, data = mtcars))
knitr::kable(outreg(fitlist))

Overview

outreg summarizes regression outcomes into a coefficient table in data.frame format. Currently, outreg supports the following model fit objects:

• lm : linear regression
• glm : logistic regression, poisson regression, etc
• survreg : survival regression, tobit regression, etc
• ivreg : instrument variable regression

Usage

outreg takes a list of model fit objects as the main input, and returns a data.frame object where a model is represented in a column.

library(outreg)
fitlist <- list(lm(mpg ~ cyl, data = mtcars),
                lm(mpg ~ cyl + wt + hp, data = mtcars),
                lm(mpg ~ cyl + wt + hp + drat, data = mtcars))
outreg(fitlist)

Installation

Install from the CRAN repository.

install.packages('outreg')

Or the recent version from the github repository.

devtools::install_github('kota7/outreg')

Examples

Basic usage

library(outreg)
fitlist <- list(lm(mpg ~ cyl, data = mtcars),
                lm(mpg ~ cyl + wt + hp, data = mtcars),
                lm(mpg ~ cyl + wt + hp + drat, data = mtcars))
outreg(fitlist)

Custom regression names

If regression list is named, the names are used as column names.

outreg(setNames(fitlist, c('small', 'medium', 'large')))

Custom stats and stars

You may choose statistics to display and which stats to put "stars" on, and significance level.

outreg(fitlist, pv = TRUE, se = FALSE, 
       starred = 'pv', alpha = c(0.05, 0.01, 0.001))

Constant term at the bottom

outreg(fitlist, constlast = TRUE)

Heteroskedacity robust standard errors

outreg(fitlist, robust = TRUE)

Poisson regression

counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
fitlist2 <- list(glm(counts ~ outcome, family = poisson()),
                 glm(counts ~ outcome + treatment, family = poisson()))
outreg(fitlist2)

Logistic regression

fitlist3 <- list(glm(cbind(ncases, ncontrols) ~ agegp,
                     data = esoph, family = binomial()),
                 glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp,
                     data = esoph, family = binomial()),
                 glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
                     data = esoph, family = binomial()))
outreg(fitlist3)

Survival regression

library(survival)
fitlist4 <- list(survreg(Surv(time, status) ~ ph.ecog + age,
                         data = lung),
                 survreg(Surv(time, status) ~ ph.ecog + age + strata(sex),
                         data = lung))
outreg(fitlist4)

Tobit regression

fitlist5 <- list(survreg(Surv(durable, durable>0, type='left') ~ 1,
                 data=tobin, dist='gaussian'),
                 survreg(Surv(durable, durable>0, type='left') ~ age + quant,
                 data=tobin, dist='gaussian'))
outreg(fitlist5)

Mix of OLS and instrument variable regression

library(AER)
data("CigarettesSW", package = "AER")
CigarettesSW$rprice <- with(CigarettesSW, price/cpi)
CigarettesSW$rincome <- with(CigarettesSW, income/population/cpi)
CigarettesSW$tdiff <- with(CigarettesSW, (taxs - tax)/cpi)

fitlist6 <- list(OLS = lm(log(packs) ~ log(rprice) + log(rincome),
                          data = CigarettesSW, subset = year == "1995"),
                 IV1 = ivreg(log(packs) ~ log(rprice) + log(rincome) |
                             log(rincome) + tdiff + I(tax/cpi),
                             data = CigarettesSW, subset = year == "1995"),
                 IV2 = ivreg(log(packs) ~ log(rprice) + log(rincome) |
                             log(population) + tdiff + I(tax/cpi),
                             data = CigarettesSW, subset = year == "1995"))
outreg(fitlist6)

kota7/outreg documentation built on May 20, 2019, 1:10 p.m.