outreg: Generate Regression Table

In kota7/outreg: Regression Table for Publication

Description

Generate a regression table in data.frame format from a set of model fit objects. Currently supports lm, glm, survreg, and ivreg model outcomes.

Usage

outreg(fitlist, digits = 3L, alpha = c(0.1, 0.05, 0.01),
  bracket = c("se"), starred = c("coef"), robust = FALSE, small = TRUE,
  constlast = FALSE, norepeat = TRUE, displayed = list(), ...)

Arguments

fitlist	list of regression outcomes
digits	number of dicimal places for real numbers
alpha	vector of significance levels to star
bracket	stats to be in brackets
starred	stats to put stars on
robust	if TRUE, robust standard error is used
small	if TRUE, small sample parameter distribution is used
constlast	if TRUE, intercept is moved to the end of coefficient list
norepeat	if TRUE, repeated variable names are replaced by a empty string
displayed	a list of named logicals to customize the stats to display
...	alternative way to specify which stats to display

Details

Use outreg_stat_list to see the available stats names. The stats names are to be used for specifying bracket, starred, and displayed options.

Statistics to include can be chosen by displayed option or by `...`. For example, outreg(fitlist, displayed = list(pv = TRUE)) is identical with outreg(fitlist pv = TRUE), and p values of coefficients are displayed.

Value

regression table in data.frame format

Examples

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

# with custom regression names
outreg(setNames(fitlist, c('small', 'medium', 'large')))

# star on standard errors, instead of estimate
outreg(fitlist, starred = 'se')

# include other stats
outreg(fitlist, pv = TRUE, tv = TRUE, se = FALSE)


# 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)


# instrumental 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.