[英]stargazer - user supplied coefficients and SE
I am using the stargazer
package for regression outputs in R. I have a customized estimation procedure that does not result in a model object but only a vector of coefficients and standard errors. 我正在将stargazer
软件包用于R中的回归输出。我有一个自定义的估计过程,该过程不会产生模型对象,而只会产生系数和标准误差的向量。 Is there a way I can supply these to stargazer
and get a nicely formatted output table? 有什么办法可以将它们提供给stargazer
并获得格式正确的输出表?
Example: 例:
dep.var <- "foo"
regressors <- c("bar", "baz", "xyz")
vec.coeffs <- c(1.2, 2.3, 3.4)
vec.se <- c(0.1, 0.1, 0.3)
Output should look akin to: 输出应类似于:
===============================================
Dependent variable:
---------------------------
foo
-----------------------------------------------
bar 1.200***
(0.100)
baz 2.300***
(0.100)
xyz 3.400***
(0.300)
-----------------------------------------------
Here's one suggestion: the main idea is to make a fake lm
object, and then apply custom coefficients, SEs, etc. to the stargazer
output: 这是一个建议:主要思想是制作一个假的lm
对象,然后将自定义系数,SE等应用到stargazer
输出中:
d <- as.data.frame(matrix(rnorm(10 * 4), nc = 4))
names(d) <- c(dep.var, regressors)
f <- as.formula(paste(dep.var, "~ 0 +", paste(regressors, collapse = "+")))
p <- lm(f, d)
stargazer(p, type = "text",
coef = list(vec.coeffs),
se = list(vec.se),
t = list(vec.coeffs / vec.se),
omit.stat = "all")
# =================================
# Dependent variable:
# ---------------------------
# foo
# ---------------------------------
# bar 1.200***
# (0.100)
# baz 2.300***
# (0.100)
# xyz 3.400***
# (0.300)
# =================================
# =================================
# Note: *p<0.1; **p<0.05; ***p<0.01
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.