这是MWE:
library(pscl)
data("bioChemists", package = "pscl")
fm_pois <- glm(art ~ ., data = bioChemists, family = poisson)
fm_qpois <- glm(art ~ ., data = bioChemists, family = quasipoisson)
fm_nb <- glm.nb(art ~ ., data = bioChemists)
fm_zinb <- zeroinfl(art ~ . | 1, data = bioChemists, dist = "negbin")
library(stargazer)
stargazer(
fm_pois, fm_qpois, fm_nb, fm_zinb
, type = "text"
)
=============================================================================
Dependent variable:
-----------------------------------------------------------
art
Poisson glm: quasipoisson negative zero-inflated
link = log binomial count data
(1) (2) (3) (4)
-----------------------------------------------------------------------------
femWomen -0.225*** -0.225*** -0.216*** -0.216***
(0.055) (0.074) (0.073) (0.073)
marMarried 0.155** 0.155* 0.150* 0.150*
(0.061) (0.083) (0.082) (0.082)
kid5 -0.185*** -0.185*** -0.176*** -0.176***
(0.040) (0.054) (0.053) (0.053)
phd 0.013 0.013 0.015 0.015
(0.026) (0.036) (0.036) (0.036)
ment 0.026*** 0.026*** 0.029*** 0.029***
(0.002) (0.003) (0.003) (0.003)
Constant 0.305*** 0.305** 0.256* 0.256*
(0.103) (0.139) (0.137) (0.139)
-----------------------------------------------------------------------------
Observations 915 915 915 915
Log Likelihood -1,651.056 -1,561.958 -1,560.959
theta 2.264*** (0.271)
Akaike Inf. Crit. 3,314.113 3,135.917
=============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
我正在寻找像这样的多列输出:
=============================================================================
Dependent variable:
-----------------------------------------------------------
art
Poisson Negative Binomial
Poisson QuasiPoisson NB ZINB
(1) (2) (3) (4)
-----------------------------------------------------------------------------
femWomen -0.225*** -0.225*** -0.216*** -0.216***
(0.055) (0.074) (0.073) (0.073)
marMarried 0.155** 0.155* 0.150* 0.150*
(0.061) (0.083) (0.082) (0.082)
kid5 -0.185*** -0.185*** -0.176*** -0.176***
(0.040) (0.054) (0.053) (0.053)
phd 0.013 0.013 0.015 0.015
(0.026) (0.036) (0.036) (0.036)
ment 0.026*** 0.026*** 0.029*** 0.029***
(0.002) (0.003) (0.003) (0.003)
Constant 0.305*** 0.305** 0.256* 0.256*
(0.103) (0.139) (0.137) (0.139)
-----------------------------------------------------------------------------
Observations 915 915 915 915
Log Likelihood -1,651.056 -1,561.958 -1,560.959
theta 2.264*** (0.271)
Akaike Inf. Crit. 3,314.113 3,135.917
=============================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
Poisson前两列和后两列的单词Negative Binomial.Poisson,Quasi Poisson,Negative Binomial和Zero Inflated Negative Binomial.我找到了这个链接,但无法弄清楚如何获得这个.
像尼克·肯尼迪（Nick Kennedy）一样，我认为这stargazer不能直接产生您想要的输出。
因此，这里是一种解决方法：将stargazer表保存在一个对象中，然后手动添加所需的行。我在这里对此进行了硬编码；稍加努力，应该可以将文本自动居中于各列上方。请注意，我略微更改了您的stargazer通话以隐藏（错误的）型号名称。
library(pscl)
library(stargazer)
data("bioChemists", package = "pscl")
fm_pois <- glm(art ~ ., data = bioChemists, family = poisson)
fm_qpois <- glm(art ~ ., data = bioChemists, family = quasipoisson)
fm_nb <- glm.nb(art ~ ., data = bioChemists)
fm_zinb <- zeroinfl(art ~ . | 1, data = bioChemists, dist = "negbin")
byLine <-
do.call("c",
strsplit(
capture.output(
stargazer(fm_pois, fm_qpois, fm_nb, fm_zinb,
type = "text", model.names = FALSE)
),
"\n"))
result <- append(
byLine,
c(
" Poisson Negative Binomial",
"",
" Poisson QuasiPoisson NB ZINB"
),
after = c(4, 5, 6))
cat(paste(result, collapse = "\n"))
# ==================================================================
# Dependent variable:
# ------------------------------------------------
# art
# Poisson Negative Binomial
#
# Poisson QuasiPoisson NB ZINB
# (1) (2) (3) (4)
# ------------------------------------------------------------------
# femWomen -0.225*** -0.225*** -0.216*** -0.216***
# (0.055) (0.074) (0.073) (0.073)
#
# marMarried 0.155** 0.155* 0.150* 0.150*
# (0.061) (0.083) (0.082) (0.082)
#
# kid5 -0.185*** -0.185*** -0.176*** -0.176***
# (0.040) (0.054) (0.053) (0.053)
#
# phd 0.013 0.013 0.015 0.015
# (0.026) (0.036) (0.036) (0.036)
#
# ment 0.026*** 0.026*** 0.029*** 0.029***
# (0.002) (0.003) (0.003) (0.003)
#
# Constant 0.305*** 0.305** 0.256* 0.256*
# (0.103) (0.139) (0.137) (0.139)
#
# ------------------------------------------------------------------
# Observations 915 915 915 915
# Log Likelihood -1,651.056 -1,561.958 -1,560.959
# theta 2.264*** (0.271)
# Akaike Inf. Crit. 3,314.113 3,135.917
# ==================================================================
# Note: *p<0.1; **p<0.05; ***p<0.01