Stargazer produces very nice latex tables for lm (and other) objects.  Suppose I&#39;ve fit a model by maximum likelihood.  I&#39;d like stargazer to produce a lm-like table for my estimates.  How can I do this?

Although it&#39;s a bit hacky, one way might be to create a &quot;fake&quot; 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 &lt;- 200
    df &lt;- data.frame(x=runif(N, 0, 50))
    df$y &lt;- 10 + 2 * df$x + 4 * rt(N, 4)  # True params
    plot(df$x, df$y)
    
    model1 &lt;- lm(y ~ x, data=df)
    stargazer(model1, title=&quot;A Model&quot;)  # I&#39;d like to produce a similar table for the model below
    
    ll &lt;- function(params) {
        ## Log likelihood for y ~ x + student&#39;s t errors
        params &lt;- 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 &lt;- optim(par=c(const=5, beta=1, scale=3, degrees.freedom=5), lower=c(-Inf, -Inf, 0.1, 0.1),
                    fn=ll, method=&quot;L-BFGS-B&quot;, control=list(fnscale=-1), hessian=TRUE)
    model2.coefs &lt;- data.frame(coefficient=names(model2$par), value=as.numeric(model2$par),
                               se=as.numeric(sqrt(diag(solve(-model2$hessian)))))
    
    stargazer(model2.coefs, title=&quot;Another Model&quot;, 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 &quot;custom&quot; model estimated by maximum likelihood, as above?

Here are my feeble attempts at dressing up my optim output as a lm object:

    model2.lm &lt;- list()  # Mimic an lm object
    class(model2.lm) &lt;- c(class(model2.lm), &quot;lm&quot;)
    model2.lm$rank &lt;- model1$rank  # Problematic?
    model2.lm$coefficients &lt;- model2$par
    names(model2.lm$coefficients)[1:2] &lt;- names(model1$coefficients)
    model2.lm$fitted.values &lt;- model2$par[&quot;const&quot;] + model2$par[&quot;beta&quot;]*df$x
    model2.lm$residuals &lt;- df$y - model2.lm$fitted.values
    model2.lm$model &lt;- df
    model2.lm$terms &lt;- model1$terms  # Problematic?
    summary(model2.lm)  # Not working



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Get coefficients estimated by maximum likelihood into a stargazer table