Create Regression Tables for Publication
| .variable | .stat | Model 1 | Model 2 | Model 3 | |
|---|---|---|---|---|---|
| 1 | (Intercept) | Estimate | 37.885*** | 38.752*** | 34.496*** |
| 2 | Std Err | [2.074] | [1.787] | [7.441] | |
| 5 | cyl | Estimate | -2.876*** | -0.942* | -0.762 |
| 6 | Std Err | [0.322] | [0.551] | [0.635] | |
| 9 | wt | Estimate | -3.167*** | -2.973*** | |
| 10 | Std Err | [0.741] | [0.818] | ||
| 13 | hp | Estimate | -0.018 | -0.021 | |
| 14 | Std Err | [0.012] | [0.013] | ||
| 17 | drat | Estimate | 0.818 | ||
| 18 | Std Err | [1.387] | |||
| 21 | N | 32 | 32 | 32 | |
| 22 | R2 | 0.726 | 0.843 | 0.845 | |
| 23 | adj R2 | 0.717 | 0.826 | 0.822 | |
| 24 | AIC | 169.306 | 155.477 | 157.067 |
outreg summarizes regression outcomes into a coefficient table in data.frame format. Currently, outreg supports the following model fit objects:
lm: linear regressionglm: logistic regression, poisson regression, etcsurvreg: survival regression, tobit regression, etcivreg: instrument variable regression
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)
#> .variable .stat Model 1 Model 2 Model 3
#> 1 (Intercept) Estimate 37.885*** 38.752*** 34.496***
#> 2 Std Err [2.074] [1.787] [7.441]
#> 5 cyl Estimate -2.876*** -0.942* -0.762
#> 6 Std Err [0.322] [0.551] [0.635]
#> 9 wt Estimate -3.167*** -2.973***
#> 10 Std Err [0.741] [0.818]
#> 13 hp Estimate -0.018 -0.021
#> 14 Std Err [0.012] [0.013]
#> 17 drat Estimate 0.818
#> 18 Std Err [1.387]
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067Install from the CRAN repository.
install.packages('outreg')Or the recent version from the github repository.
devtools::install_github('kota7/outreg')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)
#> .variable .stat Model 1 Model 2 Model 3
#> 1 (Intercept) Estimate 37.885*** 38.752*** 34.496***
#> 2 Std Err [2.074] [1.787] [7.441]
#> 5 cyl Estimate -2.876*** -0.942* -0.762
#> 6 Std Err [0.322] [0.551] [0.635]
#> 9 wt Estimate -3.167*** -2.973***
#> 10 Std Err [0.741] [0.818]
#> 13 hp Estimate -0.018 -0.021
#> 14 Std Err [0.012] [0.013]
#> 17 drat Estimate 0.818
#> 18 Std Err [1.387]
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067If regression list is named, the names are used as column names.
outreg(setNames(fitlist, c('small', 'medium', 'large')))
#> .variable .stat small medium large
#> 1 (Intercept) Estimate 37.885*** 38.752*** 34.496***
#> 2 Std Err [2.074] [1.787] [7.441]
#> 5 cyl Estimate -2.876*** -0.942* -0.762
#> 6 Std Err [0.322] [0.551] [0.635]
#> 9 wt Estimate -3.167*** -2.973***
#> 10 Std Err [0.741] [0.818]
#> 13 hp Estimate -0.018 -0.021
#> 14 Std Err [0.012] [0.013]
#> 17 drat Estimate 0.818
#> 18 Std Err [1.387]
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067You 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))
#> .variable .stat Model 1 Model 2 Model 3
#> 1 (Intercept) Estimate 37.885 38.752 34.496
#> 4 p Value 0.000*** 0.000*** 0.000***
#> 5 cyl Estimate -2.876 -0.942 -0.762
#> 8 p Value 0.000*** 0.098 0.240
#> 9 wt Estimate -3.167 -2.973
#> 12 p Value 0.000*** 0.001**
#> 13 hp Estimate -0.018 -0.021
#> 16 p Value 0.140 0.118
#> 17 drat Estimate 0.818
#> 20 p Value 0.560
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067outreg(fitlist, constlast = TRUE)
#> .variable .stat Model 1 Model 2 Model 3
#> 1 cyl Estimate -2.876*** -0.942* -0.762
#> 2 Std Err [0.322] [0.551] [0.635]
#> 5 wt Estimate -3.167*** -2.973***
#> 6 Std Err [0.741] [0.818]
#> 9 hp Estimate -0.018 -0.021
#> 10 Std Err [0.012] [0.013]
#> 13 drat Estimate 0.818
#> 14 Std Err [1.387]
#> 17 (Intercept) Estimate 37.885*** 38.752*** 34.496***
#> 18 Std Err [2.074] [1.787] [7.441]
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067outreg(fitlist, robust = TRUE)
#> .variable .stat Model 1 Model 2 Model 3
#> 1 (Intercept) Estimate 37.885*** 38.752*** 34.496***
#> 2 Std Err [2.528] [2.017] [6.085]
#> 5 cyl Estimate -2.876*** -0.942* -0.762
#> 6 Std Err [0.359] [0.481] [0.527]
#> 9 wt Estimate -3.167*** -2.973***
#> 10 Std Err [0.664] [0.756]
#> 13 hp Estimate -0.018** -0.021**
#> 14 Std Err [0.008] [0.009]
#> 17 drat Estimate 0.818
#> 18 Std Err [1.014]
#> 21 N 32 32 32
#> 22 R2 0.726 0.843 0.845
#> 23 adj R2 0.717 0.826 0.822
#> 24 AIC 169.306 155.477 157.067counts <- 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)
#> .variable .stat Model 1 Model 2
#> 1 (Intercept) Estimate 3.045*** 3.045***
#> 2 Std Err [0.126] [0.171]
#> 6 outcome2 Estimate -0.454** -0.454**
#> 7 Std Err [0.202] [0.202]
#> 11 outcome3 Estimate -0.293 -0.293
#> 12 Std Err [0.193] [0.193]
#> 16 treatment2 Estimate 0.000
#> 17 Std Err [0.200]
#> 21 treatment3 Estimate -0.000
#> 22 Std Err [0.200]
#> 26 N 9 9
#> 27 AIC 52.761 56.761fitlist3 <- 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)
#> .variable .stat Model 1 Model 2 Model 3
#> 1 (Intercept) Estimate -2.139*** -1.780*** -1.760***
#> 2 Std Err [0.189] [0.198] [0.198]
#> 6 agegp.L Estimate 2.882*** 3.005*** 2.996***
#> 7 Std Err [0.644] [0.652] [0.654]
#> 11 agegp.Q Estimate -1.629*** -1.338** -1.350**
#> 12 Std Err [0.583] [0.591] [0.592]
#> 16 agegp.C Estimate 0.151 0.153 0.134
#> 17 Std Err [0.443] [0.449] [0.451]
#> 21 agegp^4 Estimate 0.218 0.064 0.071
#> 22 Std Err [0.302] [0.309] [0.310]
#> 26 agegp^5 Estimate -0.178 -0.194 -0.213
#> 27 Std Err [0.189] [0.195] [0.196]
#> 31 tobgp.L Estimate 0.594*** 0.638***
#> 32 Std Err [0.194] [0.197]
#> 36 tobgp.Q Estimate 0.065 0.029
#> 37 Std Err [0.188] [0.196]
#> 41 tobgp.C Estimate 0.157 0.156
#> 42 Std Err [0.187] [0.198]
#> 46 alcgp.L Estimate 1.492*** 1.371***
#> 47 Std Err [0.199] [0.211]
#> 51 alcgp.Q Estimate -0.227 -0.149
#> 52 Std Err [0.180] [0.196]
#> 56 alcgp.C Estimate 0.255 0.228
#> 57 Std Err [0.159] [0.182]
#> 61 tobgp.L:alcgp.L Estimate -0.704*
#> 62 Std Err [0.411]
#> 66 tobgp.Q:alcgp.L Estimate 0.122
#> 67 Std Err [0.420]
#> 71 tobgp.C:alcgp.L Estimate -0.292
#> 72 Std Err [0.429]
#> 76 tobgp.L:alcgp.Q Estimate 0.129
#> 77 Std Err [0.389]
#> 81 tobgp.Q:alcgp.Q Estimate -0.445
#> 82 Std Err [0.392]
#> 86 tobgp.C:alcgp.Q Estimate -0.052
#> 87 Std Err [0.395]
#> 91 tobgp.L:alcgp.C Estimate -0.161
#> 92 Std Err [0.367]
#> 96 tobgp.Q:alcgp.C Estimate 0.048
#> 97 Std Err [0.362]
#> 101 tobgp.C:alcgp.C Estimate -0.139
#> 102 Std Err [0.358]
#> 106 N 88 88 88
#> 107 AIC 298.593 225.454 236.964library(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)
#> .variable .stat Model 1 Model 2
#> 1 (Intercept) Estimate 6.831*** 6.732***
#> 2 Std Err [0.429] [0.424]
#> 6 ph.ecog Estimate -0.326*** -0.324***
#> 7 Std Err [0.086] [0.086]
#> 11 age Estimate -0.008 -0.006
#> 12 Std Err [0.007] [0.007]
#> 16 Log(scale) Estimate -0.304***
#> 17 Std Err [0.062]
#> 21 Log(scale) sex=1 Estimate -0.244***
#> 22 Std Err [0.079]
#> 26 Log(scale) sex=2 Estimate -0.423***
#> 27 Std Err [0.107]
#> 31 N 227 227
#> 32 AIC 2284.215 2284.504fitlist5 <- 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)
#> .variable .stat Model 1 Model 2
#> 1 (Intercept) Estimate -2.227 15.145
#> 2 Std Err [2.060] [16.079]
#> 6 age Estimate -0.129
#> 7 Std Err [0.219]
#> 11 quant Estimate -0.046
#> 12 Std Err [0.058]
#> 16 Log(scale) Estimate 1.783*** 1.718***
#> 17 Std Err [0.309] [0.310]
#> 21 N 20 20
#> 22 AIC 62.984 65.880library(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)
#> .variable .stat OLS IV1 IV2
#> 1 (Intercept) Estimate 10.342*** 9.895*** 10.116***
#> 2 Std Err [1.023] [1.059] [1.210]
#> 5 log(rprice) Estimate -1.407*** -1.277*** -0.892**
#> 6 Std Err [0.251] [0.263] [0.397]
#> 9 log(rincome) Estimate 0.344 0.280 -0.489
#> 10 Std Err [0.235] [0.239] [0.608]
#> 13 N 48 48 48
#> 14 R2 0.433 0.429 0.274
#> 15 adj R2 0.408 0.404 0.241
#> 16 AIC -19.680
#> 17 Wu-Hasuman stat 3.068 3.553
#> 18 WuHausman_pv 0.087 0.037
#> 19 Sargan stat 0.333 0.076
#> 20 Sargan_pv 0.564 0.783
#> 21 log(rprice) Weak instr stat 244.734 195.613
#> 22 Weak instr p-value 0.000 0.000
#> 23 log(rincome) Weak instr stat 7.767
#> 24 Weak instr p-value 0.000