I'm doing research into understand the influential factors within a logistic regression model I've built in R using the glm() function.

From my research, it seems that using the summary() function to summarize the model is a popular method to identify which variables are significant. What I can't seem to find however is a description of what the summary function is doing to determine the significance codes (eg. the *) for each variable. This answer states that the significance codes are simply categorizations of the p-value, but I don't really understand that.

Is there anyone out there that could maybe help me understand how R computes this?


2 Answers 2


Firstly, the z or t value (depending on what family you run) is the coefficient divided by the standard error. The p value is then derived from the normal or t distributions using this z or t value.

The stars don't really add much in my view. You will see underneath the table of coefficients that there is a line which starts 'Signif. codes'. This gives the key. So a coefficient marked *** is one whose p value < 0.001. One whose coefficient is marked ** is p < 0.01. And so on.

For example (taken from https://stats.idre.ucla.edu/r/dae/logit-regression/):

mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
mydata$rank <- factor(mydata$rank)
mylogit <- glm(admit ~ gre + gpa + rank, data = mydata, family = "binomial")

Gives the following output:

glm(formula = admit ~ gre + gpa + rank, family = "binomial", 
    data = mydata)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6268  -0.8662  -0.6388   1.1490   2.0790  

             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -3.989979   1.139951  -3.500 0.000465 ***
gre          0.002264   0.001094   2.070 0.038465 *  
gpa          0.804038   0.331819   2.423 0.015388 *  
rank2       -0.675443   0.316490  -2.134 0.032829 *  
rank3       -1.340204   0.345306  -3.881 0.000104 ***
rank4       -1.551464   0.417832  -3.713 0.000205 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 499.98  on 399  degrees of freedom
Residual deviance: 458.52  on 394  degrees of freedom
AIC: 470.52

Number of Fisher Scoring iterations: 4

You can see that gre has a p value = 0.038. This has one asterisk by it because that is < 0.05. rank4 has a p value = 0.0002 and so has three asterisks because this is < 0.001.

I just use the asterisks to quickly scan the table but I never look at them beyond that.


If you want to know right away which variables (independent variables, IVs) impacts your dependent varibale (DV) the most, you could use the following:

install.packages("caret") # run install only if you've never installed it before
fit<-lm(DV~IV1+IV2+IV3, data=mydata)
varImp(fit, scale = FALSE)

IV1     -4.3
IV2     7.65
IV3     12.37

The IV with the highest value has the most impact. Make sure you don't use 2 IVs with a high correlation between themselves to avoid multicollinearity.

  • 1
    $\begingroup$ This is helpful, but doesn't answer the question: How is the p-value calculated? $\endgroup$
    – Mark White
    Jun 22, 2017 at 13:24

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.