Odds Ratios Instead of Logits in Stargazer() Latex Output

Odds ratios instead of logits in stargazer() LaTeX output

As per symbiotic comment in 2014, more recent versions of ''stargazer'' have the options ''apply.*'' for ''coef'' ''se'' ''t'' ''p'' and ''ci'' allowing the direct transformation of these statistics.

apply.coef a function that will be applied to the coefficients.
apply.se a function that will be applied to the standard errors.
apply.t a function that will be applied to the test statistics.
apply.p a function that will be applied to the p-values.
apply.ci a function that will be applied to the lower and upper bounds of the confidence intervals.

Meaning you can directly use...

stargazer(model, 
          apply.coef = exp,
          apply.se   = exp)

EDIT : I have noticed however that simply exponentiating the CIs does not give what you would expect.

EDIT : You can obtain the correct CIs using the method described here.

Stargazer for reporting odds ratios in logit report wrong significance stars

generally you should strive to provide a minimal working example so we can reproduce your results - in this case it would be great to have data and the code you use to produce your basic.logit.model.

To your question, apply.coef only transforms your coefficients but not the standard errors as your results show. stargazer calculates the significance level using these untransformed SEs which results in non-significant coefficients.

To avoid this, provide stargazer with the custom p-values (those from the original model) using the p argument.

This should work for you

p.values <- list(summary(basic.logit.model)$coefficients[,4]

stargazer(basic.logit.model, 
  type="html",
  apply.coef = OR,
  p = p.values,
  column.labels = c("Base"),
  dep.var.labels.include = FALSE,
  digits=2, out=("basic_model_only.htm"))

How do I add confidence intervals to odds ratios in stargazer table?

You can use the ci.custom argument to feed stargazer a list with custom confidence intervals (first column is the lower bound, second column is the upper bound). In your example, all you have to do is call:

stargazer(mylogit, coef = list(OR.vector), ci = T, 
ci.custom = list(CI.vector), single.row = T, type = "text")

Alternatively, you can define your own function to exponentiate values and simply apply this function to your coefficients and/or confidence intervals (using arguments apply.coef and apply.ci):

exponentiate <- function(x) exp(x)

stargazer(mylogit, ci=T, apply.coef=exponentiate, apply.ci=exponentiate, 
single.row = T, type="text")

How do I add coefficients, SE, confidence intervals, and odds ratios in stargazer table?

Stargazer accepts multiple models and appends each to a new row. So, you can make a second model and replace the standard coefficients with odds ratios and pass this to the stargazer call.

tattoo <- read.table("https://ndownloader.figshare.com/files/6920972", 
                  header=TRUE, na.strings=c("unk", "NA"))    

library(mlogit)

Tat<-mlogit.data(tattoo, varying=NULL, shape="wide", choice="size", id.var="date")

ml.Tat<-mlogit(size~1|age+sex+yy, Tat, reflevel="small", id.var="date")
ml.TatOR<-mlogit(size~1|age+sex+yy, Tat, reflevel="small", id.var="date")
ml.TatOR$coefficients <- exp(ml.TatOR$coefficients) #replace coefficents with odds ratios

library(stargazer)
stargazer(ml.Tat, ml.TatOR, ci=c(F,T),column.labels=c("coefficients","odds ratio"),
          type="text",single.row=TRUE, star.cutoffs=c(0.05,0.01,0.001),
          out="table1.txt", digits=4)

The argument ci=c(F,T) suppresses the confidence interval in the first column (so SEs are shown instead) and shows it in the second column. The column.labels argument lets you name the columns.

====================================================================
                                  Dependent variable:               
                   -------------------------------------------------
                                         size                       
                        coefficients              odds ratio        
                            (1)                      (2)            
--------------------------------------------------------------------
large:(intercept)  -444.6032*** (22.1015) 0.0000 (-43.3181, 43.3181)
medium:(intercept) -187.9871*** (11.9584) 0.0000 (-23.4381, 23.4381)
large:age            0.0251*** (0.0041)   1.0254*** (1.0174, 1.0334)
medium:age           0.0080** (0.0026)    1.0081*** (1.0030, 1.0131)
large:sexM           1.3818*** (0.0607)   3.9821*** (3.8632, 4.1011)
medium:sexM          0.7365*** (0.0330)   2.0886*** (2.0239, 2.1534)
large:yy             0.2195*** (0.0110)   1.2455*** (1.2239, 1.2670)
medium:yy            0.0931*** (0.0059)   1.0976*** (1.0859, 1.1093)
--------------------------------------------------------------------
Observations               18,162                   18,162          
R2                         0.0410                   0.0410          
Log Likelihood          -15,882.7000             -15,882.7000       
LR Test (df = 8)       1,357.1140***            1,357.1140***        
====================================================================
Note:                                  *p<0.05; **p<0.01; ***p<0.001

R package stargazer produces two table outputs instead of one

You can use the package called gtsummary to make regression tables.

Replace your stargazer line with this:

library(gtsummary)
tbl_regression(glfit, exponentiate = TRUE,
               intercept = TRUE) %>% 
    add_significance_stars() %>% 
    add_glance_source_note(include = c(AIC, BIC, df.residual,nobs)) %>% 
    modify_caption("Logistic regression on species")

The tbl_regression() creates a basic table and the other functions called after pipes further customize the table.

see here for more details: https://www.danieldsjoberg.com/gtsummary/

How to keep just one variable in stargazer regression output? (oposite of omit)

Instead of using omit, you can use keep to keep only the variables that you need.

stargazer::stargazer(model1, type = "text", keep = 'Dummy1:Dummy2:Dummy3')

================================================
                         Dependent variable:    
                     ---------------------------
                                  Y             
------------------------------------------------
Dummy1:Dummy2:Dummy3           42.430           
                              (35.315)          
                                                
------------------------------------------------
Observations                     100            
R2                              0.145           
Adjusted R2                     0.080           
Residual Std. Error       43.587 (df = 92)      
F Statistic             2.222** (df = 7; 92)    
================================================
Note:                *p<0.1; **p<0.05; ***p<0.01

Odds Ratios Instead of Logits in Stargazer() Latex Output