Get coefficients estimated by maximum likelihood into a stargazer table

Question

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?

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?

An example:

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? 

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?

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 

Answer 1

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="") 

Answer 2

You need to first instantiate a dummy lm object, then dress it up:

#... model2.lm = lm(y ~ ., data.frame(y=runif(5), beta=runif(5), scale=runif(5), degrees.freedom=runif(5))) model2.lm$coefficients <- model2$par model2.lm$fitted.values <- model2$par["const"] + model2$par["beta"]*df$x model2.lm$residuals <- df$y - model2.lm$fitted.values stargazer(model2.lm, se = list(model2.coefs$se), summary=FALSE, type='text')  # =============================================== #                         Dependent variable:     #                     --------------------------- #                                  y              # ----------------------------------------------- # const                        10.127***          #                               (0.680)           #                                                 # beta                         1.995***           #                               (0.024)           #                                                 # scale                        3.836***           #                               (0.393)           #                                                 # degrees.freedom              3.682***           #                               (1.187)           #                                                 # ----------------------------------------------- # Observations                    200             # R2                             0.965            # Adjusted R2                    0.858            # Residual Std. Error       75.581 (df = 1)       # F Statistic              9.076 (df = 3; 1)      # =============================================== # Note:               *p<0.1; **p<0.05; ***p<0.01 

(and then of course make sure the remaining summary stats are correct)