将通过最大似然估计的系数获取到寻星表中 [英] Get coefficients estimated by maximum likelihood into a stargazer table

问题描述

Stargazer为lm(和其他)物体产生非常漂亮的乳胶表.假设我已经以最大可能性拟合了模型.我希望观星者为我的估算出一张类似lm的桌子.我该怎么办?

Stargazer produces very nice latex tables for lm (and other) objects. Suppose I've fit a model by maximum likelihood. I'd like stargazer to produce a lm-like table for my estimates. How can I do this?

尽管有点黑，但一种方法可能是创建一个包含我的估算值的伪" lm对象-我认为，只要summary(my.fake.lm.object)有效，它就可以工作.这容易做到吗?

Although it's a bit hacky, one way might be to create a "fake" lm object containing my estimates -- I think this would work as long as summary(my.fake.lm.object) works. Is that easily doable?

一个例子:

library(stargazer)

N <- 200
df <- data.frame(x=runif(N, 0, 50))
df$y <- 10 + 2 * df$x + 4 * rt(N, 4)  # True params
plot(df$x, df$y)

model1 <- lm(y ~ x, data=df)
stargazer(model1, title="A Model")  # I'd like to produce a similar table for the model below

ll <- function(params) {
    ## Log likelihood for y ~ x + student's t errors
    params <- as.list(params)
    return(sum(dt((df$y - params$const - params$beta*df$x) / params$scale, df=params$degrees.freedom, log=TRUE) -
               log(params$scale)))
}

model2 <- optim(par=c(const=5, beta=1, scale=3, degrees.freedom=5), lower=c(-Inf, -Inf, 0.1, 0.1),
                fn=ll, method="L-BFGS-B", control=list(fnscale=-1), hessian=TRUE)
model2.coefs <- data.frame(coefficient=names(model2$par), value=as.numeric(model2$par),
                           se=as.numeric(sqrt(diag(solve(-model2$hessian)))))

stargazer(model2.coefs, title="Another Model", summary=FALSE)  # Works, but how can I mimic what stargazer does with lm objects?

更准确地说:对于lm对象，观星者可以很好地在表顶部打印因变量，并在对应估计值下方的括号中包含SE，并在表底部具有R ^ 2和观察值.是否有一种(简便的方法)通过最大似然估计的自定义"模型来获得相同的行为，如上所述?

To be more precise: with lm objects, stargazer nicely prints the dependent variable at the top of the table, includes SEs in parentheses below the corresponding estimates, and has the R^2 and number of observations at the bottom of the table. Is there a(n easy) way to obtain the same behavior with a "custom" model estimated by maximum likelihood, as above?

这是我微不足道的尝试，将我的优化输出打扮成lm对象:

Here are my feeble attempts at dressing up my optim output as a lm object:

model2.lm <- list()  # Mimic an lm object
class(model2.lm) <- c(class(model2.lm), "lm")
model2.lm$rank <- model1$rank  # Problematic?
model2.lm$coefficients <- model2$par
names(model2.lm$coefficients)[1:2] <- names(model1$coefficients)
model2.lm$fitted.values <- model2$par["const"] + model2$par["beta"]*df$x
model2.lm$residuals <- df$y - model2.lm$fitted.values
model2.lm$model <- df
model2.lm$terms <- model1$terms  # Problematic?
summary(model2.lm)  # Not working

推荐答案

我只是遇到了这个问题，并且通过在stargazer中使用coef se和omit函数克服了这个问题.例如

I was just having this problem and overcame this through the use of the coef se, and omit functions within stargazer... e.g.

stargazer(regressions, ...
                     coef = list(... list of coefs...),
                     se = list(... list of standard errors...),
                     omit = c(sequence),
                     covariate.labels = c("new names"),
                     dep.var.labels.include = FALSE,
                     notes.append=FALSE), file="")

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