I'm using a binomial logistic regression to identify if exposure to has_x or has_y impacts the likelihood that a user will click on something. My model is the following:

fit = glm(formula = has_clicked ~ has_x + has_y, 
          family = binomial())

This the output from my model:

glm(formula = has_clicked ~ has_x + has_y, 
    family = binomial(), data = active_domains)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-0.9869  -0.9719  -0.9500   1.3979   1.4233  

                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.504737   0.008847 -57.050  < 2e-16 ***
has_xTRUE -0.056986   0.010201  -5.586 2.32e-08 ***
has_yTRUE  0.038579   0.010202   3.781 0.000156 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 217119  on 164182  degrees of freedom
Residual deviance: 217074  on 164180  degrees of freedom
AIC: 217080

Number of Fisher Scoring iterations: 4

As each coefficient is significant, using this model I'm able to tell what the value of any of these combinations is using the following approach:

predict(fit, data.frame(has_x = TRUE, has_y=TRUE), type = "response")

I don't understand how I can report on the Std. Error of the prediction.

  1. Do I just need to use $1.96*SE$? Or do I need to convert the $SE$ using an approach described here?

  2. If I want to understand the standard-error for both variables how would I consider that?

Unlike this question, I am interested in understanding what the upper and lower bounds of the error are in a percentage. For example, of my prediction shows a value of 37% for True,True can I calculate that this is $+/- 0.3%$ for a $95\% CI$? (0.3% chosen to illustrate my point)


3 Answers 3


Your question may come from the fact that you are dealing with Odds Ratios and Probabilities which is confusing at first. Since the logistic model is a non linear transformation of $\beta^Tx$ computing the confidence intervals is not as straightforward.


Recall that for the Logistic regression model

  • Probability of $(Y = 1)$: $p = \frac{e^{\alpha + \beta_1x_1 + \beta_2 x_2}}{1 + e^{ \alpha + \beta_1x_1 + \beta_2 x_2}}$

  • Odds of $(Y = 1)$: $ \left( \frac{p}{1-p}\right) = e^{\alpha + \beta_1x_1 + \beta_2 x_2}$

  • Log Odds of $(Y = 1)$: $ \log \left( \frac{p}{1-p}\right) = \alpha + \beta_1x_1 + \beta_2 x_2$

Consider the case where you have a one unit increase in variable $x_1$, i.e. $x_1 + 1$, then the new odds are

$$ \text{Odds}(Y = 1) = e^{\alpha + \beta_1(x_1 + 1) + \beta_2x_2} = e^{\alpha + \beta_1 x_1 + \beta_1 + \beta_2x_2 } $$

  • Odds Ratio (OR) are therefore

$$ \frac{\text{Odds}(x_1 + 1)}{\text{Odds}(x_1)} = \frac{e^{\alpha + \beta_1(x_1 + 1) + \beta_2x_2} }{e^{\alpha + \beta_1 x_1 + \beta_2x_2}} = e^{\beta_1} $$

  • Log Odds Ratio = $\beta_1$

  • Relative risk or (probability ratio) = $\frac{ \frac{e^{\alpha + \beta_1x_1 + \beta_1 + \beta_2 x_2}}{1 + e^{ \alpha + \beta_1x_1 + \beta_1 + \beta_2 x_2}}}{ \frac{e^{\alpha + \beta_1x_1 + \beta_2 x_2}}{1 + e^{ \alpha + \beta_1x_1 + \beta_2 x_2}}}$

Interpreting coefficients

How would you interpret the coefficient value $\beta_j$ ? Assuming that everything else remains fixed:

  • For every unit increase in $x_j$ the log-odds ratio increases by $\beta_j$.
  • For every unit increase in $x_j$ the odds ratio increases by $e^{\beta_j}$.
  • For every increase of $x_j$ from $k$ to $k + \Delta$ the odds ratio increases by $e^{\beta_j \Delta}$
  • If the coefficient is negative, then an increase in $x_j$ leads to a decrease in the odds ratio.

Confidence intervals for a single parameter $\beta_j$

Do I just need to use $1.96∗SE$? Or do I need to convert the SE using an approach described here?

Since the parameter $\beta_j$ is estimated using Maxiumum Likelihood Estimation, MLE theory tells us that it is asymptotically normal and hence we can use the large sample Wald confidence interval to get the usual

$$ \beta_j \pm z^* SE(\beta_j)$$

Which gives a confidence interval on the log-odds ratio. Using the invariance property of the MLE allows us to exponentiate to get $$ e^{\beta_j \pm z^* SE(\beta_j)}$$

which is a confidence interval on the odds ratio. Note that these intervals are for a single parameter only.

If I want to understand the standard-error for both variables how would I consider that?

If you include several parameters you can use the Bonferroni procedure, otherwise for all parameters you can use the confidence interval for probability estimates

Bonferroni procedure for several parameters

If $g$ parameters are to be estimated with family confidence coefficient of approximately $1 - \alpha$, the joint Bonferroni confidence limits are

$$ \beta_g \pm z_{(1 - \frac{\alpha}{2g})}SE(\beta_g)$$

Confidence intervals for probability estimates

The logistic model outputs an estimation of the probability of observing a one and we aim to construct a frequentist interval around the true probability $p$ such that $Pr(p_{L} \leq p \leq p_{U}) = .95$

One approach called endpoint transformation does the following:

  • Compute the upper and lower bounds of the confidence interval for the linear combination $x^T\beta$ (using the Wald CI)
  • Apply a monotonic transformation to the endpoints $F(x^T\beta)$ to obtain the probabilities.

Since $Pr(x^T\beta) = F(x^T\beta)$ is a monotonic transformation of $x^T\beta$

$$ [Pr(x^T\beta)_L \leq Pr(x^T\beta) \leq Pr(x^T\beta)_U] = [F(x^T\beta)_L \leq F(x^T\beta) \leq F(x^T\beta)_U] $$

Concretely this means computing $\beta^Tx \pm z^* SE(\beta^Tx)$ and then applying the logit transform to the result to get the lower and upper bounds:

$$[\frac{e^{x^T\beta - z^* SE(x^T\beta)}}{1 + e^{x^T\beta - z^* SE(x^T\beta)}}, \frac{e^{x^T\beta + z^* SE(x^T\beta)}}{1 + e^{x^T\beta + z^* SE(x^T\beta)}},] $$

The estimated approximate variance of $x^T\beta$ can be calculated using the covariance matrix of the regression coefficients using

$$ Var(x^T\beta) = x^T \Sigma x$$

The advantage of this method is that the bounds cannot be outside the range $(0,1)$

There are several other approaches as well, using the delta method, bootstrapping etc.. which each have their own assumptions, advantages and limits.

Sources and info

My favorite book on this topic is "Applied Linear Statistical Models" by Kutner, Neter, Li, Chapter 14

Otherwise here are a few online sources:

Edit October 2021 - New links

  • $\begingroup$ Much of this is about CI for the coefficients which is a fine thing for the OP to know about but are we sure that is what he needs? You later section seems to me more relevant but perhaps the distinctions may be missed if read too quickly? $\endgroup$
    – mdewey
    Commented Jul 7, 2018 at 12:33
  • 4
    $\begingroup$ Yes you are probably right - but understanding odds, log odds and probabilities for log regression is something I struggled with in the past - I hope this post summarises the topic well enough to such that it might help someone in the future. Perhaps I could answer the question more explicitly by providing a CI but we would need the covariance matrix $\endgroup$ Commented Jul 7, 2018 at 13:40
  • $\begingroup$ Can you explain a bit more what do you mean by: "Compute the upper and lower bounds of the confidence interval for the linear combination xTβ (using the Wald CI)" - how exactly do you do this? $\endgroup$ Commented Jun 25, 2020 at 17:18
  • $\begingroup$ Also, could you explain how your method differs than what is suggested in the next answer - i.e. calculating the ci for prediction, and then transforming the prediction. $\endgroup$ Commented Jun 25, 2020 at 17:38
  • 1
    $\begingroup$ Some of the links you shared in the "Sources and Info" section have been deleted. Find below the links to (what I think are) the same same resources in other locations: * fdocuments.net/reader/full/5logreg-beamer-online * mheaton.byu.edu/docs/files/Stat330/InClassAnalyses/7%20-%20CHD/… * jslsoc.sitehost.iu.edu/stata/ci_computations/… $\endgroup$
    – dsesto
    Commented Sep 29, 2021 at 10:31

To get the 95% confidence interval of the prediction you can calculate on the logit scale and then convert those back to the probability scale 0-1. Here is an example using the titanic dataset.


titanic_train$Pclass = factor(titanic_train$Pclass, 
    levels = c(1, 2, 3), labels = c('First', 'Second', 'Third'))

fit = glm(Survived ~ Sex + Pclass, data=titanic_train, 
          family = binomial())

inverse_logit = function(x){

predicted = predict(fit, data.frame(Sex='male', Pclass='First'), 
                    type='link', se.fit=TRUE)

se_high = inverse_logit(predicted$fit + (predicted$se.fit*1.96))
se_low = inverse_logit(predicted$fit - (predicted$se.fit*1.96))
expected = inverse_logit(predicted$fit)

The mean and low/high 95% CI.

> expected
> se_high
> se_low

And the output from just using type='response', which only gives the mean

predict(fit, data.frame(Sex='male', Pclass='First'), type='response')
  • 1
    $\begingroup$ predict(fit, data.frame(Sex='male', Pclass='First'), type='response', se.fit=TRUE) will work. $\endgroup$
    – Tony416
    Commented Sep 9, 2019 at 0:15

The explanations above are very nice and detailed. Here is the simple way to just get the result: Random data frame:

dat <- data.frame(
          trial = factor(c(rep("noise", 340), rep("signal", 340))),
          rating = c(rep(1, 70), rep(2, 67), rep(3, 28), 
                 rep(4, 45), rep(5, 53), rep(6, 77), rep(1, 219), 
                 rep(2, 56), rep(3, 16), rep(4, 24), rep(5, 14), rep(6, 11))

-- >sadly the following edit is not being presented correct: here will be dat-"dollar"-rating and dat-dollar-trial to edit the data frame above

dat$rating <- as.factor(ifelse(dat$rating <= 3, "sure", "not"))
dat$trial <- ifelse(dat$trial == "noise", 0, 1)


my.glm <- glm(trial ~ rating, family = binomial(probit), data = dat)

# 95-CI for all Betas
# 90-CI for all Betas
confint(my.glm, level = 0.90)

# 95-CI of Beta 1
confint(my.glm)[c(2, 4)]
# 90-CI of Beta 1
confint(my.glm, level = 0.90)[c(2, 4)]
  • $\begingroup$ Note that confint on a glm objects uses another method (and arguably more precise) than in the other answers you refer to, namely profile likelihood. $\endgroup$ Commented Sep 19, 2022 at 13:28

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