3

I have a series of linear models and I'd like to report the standardized coefficients for each. However, when I print the models in stargazer, it looks like stargazer automatically prints the significance stars for the standardized coefficients as if they were unstandardized coefficients. You can see how the differences emerge below.

Is it statistically wrong to print the significance stars based on the unstandardized values? How is this done in stargazer? Thanks!

#load libraries
library(stargazer)
library(lm.beta)
#fake data
var1<-rnorm(100, mean=10, sd=5)
var2<-rnorm(100, mean=5, sd=2)
var3<-rnorm(100, mean=2, sd=3)
var4<-rnorm(100, mean=5, sd=1)
df<-data.frame(var1, var2, var3, var4)
#model with unstandardized betas
model1<-lm(var1~var2+var3+var4, data=df)
#Standardized betas
model1.beta<-lm.beta(model1)
#print
stargazer(model1, model1.beta, type='text')
spindoctor
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2 Answers2

5

Stargazer does not automatically know it should look for the standardized coefficients in the second model. lm.beta just add standardzied coefficients to the lm.object. So it is still an lm.object, so it extracts the coefficients as per usual (from model1.beta$coefficients. Use the coef = argument to specify the specific coefficients you want to use: coef = list(model1$coefficients, model1.beta$standardized.coefficients)

> stargazer(model1, model1.beta, 
            coef = list(model1$coefficients, 
            model1.beta$standardized.coefficients),
            type='text')

==========================================================
                                  Dependent variable:     
                              ----------------------------
                                          var1            
                                   (1)            (2)     
----------------------------------------------------------
var2                              0.135          0.048    
                                 (0.296)        (0.296)   

var3                              -0.088        -0.044    
                                 (0.205)        (0.205)   

var4                              -0.190        -0.030    
                                 (0.667)        (0.667)   

Constant                         10.195**        0.000    
                                 (4.082)        (4.082)   

----------------------------------------------------------
Observations                       100            100     
R2                                0.006          0.006    
Adjusted R2                       -0.025        -0.025    
Residual Std. Error (df = 96)     5.748          5.748    
F Statistic (df = 3; 96)          0.205          0.205    
==========================================================
Note:                          *p<0.1; **p<0.05; ***p<0.01
paqmo
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1

A big thank you to paqmo for the answer. I would just add that to get the correct p-values for the standardized solution, you need to add another line detailing which p-values to use:


    stargazer(model1, model1.beta, 
    coef = list(model1$coefficients, 
    model1.beta$standardized.coefficients),
    p = list (coef(summary(model1))[,4], coef(summary(model1.beta))[,5]),
    type='text')

Also, generally, Stargazer sometimes does not work with longer model names and gives the warning Error in if (is.na(s)) { : the condition has length > 1

Thus, I would recommend to keep your models' names short (especially if you want Stargazer to display a few of them).

Marina
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