For a Bayesian logistic regression problem, I have created a posterior predictive distribution. I sample from the predictive distribution and receive thousands of samples of (0,1) for each observation I have. Visualizing the goodness-of-fit is less than interesting, for example:

enter image description here

This plot shows the 10 000 samples + the observed datum point (way in the left one can make out a red line: yea that's the observation). The problem is is that this plot is hardly informative, and I'll have 23 of them, one for each data point.

Is there a better way to visualize the 23 data points plus there posterior samples.

Another attempt:

enter image description here

Another attempt based on the paper here

enter image description here

  • 1
    $\begingroup$ See here for an example where the above data-vis technique works. $\endgroup$ Mar 23, 2013 at 14:52
  • $\begingroup$ That is alot of wasted space IMO! Do you really only have 3 values (below 0.5, above 0.5, and the observation) or is that just an artifact of the example you gave? $\endgroup$
    – Andy W
    Mar 25, 2013 at 12:33
  • $\begingroup$ It's in fact worse: I have 8500 0s and 1500 1s. The graph just pushes these values to make a connected histogram. But I agree: lots of wasted space. Really, for each data point I can reduce it to a proportion (ex 8500/10000 ) and a observation (either 0 or 1) $\endgroup$ Mar 25, 2013 at 13:10
  • $\begingroup$ So you have 23 data points, and how many predictors? And is your posterior predictive distrubtion for new data points or for the 23 you used to fit the model? $\endgroup$ Mar 31, 2013 at 5:09
  • $\begingroup$ Your updated plot is close to what I was going to suggest. What is the x-axis representing though? It appears you have some points super-imposed - which with only 23 seems unnecessary. $\endgroup$
    – Andy W
    Mar 31, 2013 at 13:57

4 Answers 4


I have a feeling your not quite giving up all the goods to your situation, but given what we have in front of us lets consider the utility of a simple dot-plot to display the information.

Dot Plot

The only real thing to not here (that aren't perhaps default behaviors) are:

  • I utilized redundant encodings, shape and color, to discriminate between the observed values of no defects and defects. With such simple information, placing a dot on the graph is not necessary. Also you have a problem when the point is near the middle values, it takes more look-up to see if the observed value is either zero or one.
  • I sorted the graphic according to observed proportion.

Sorting is the real kicker for dot-plots like these. Sorting by values of proportion here helps easily uncover high residual observations. Having a system where you can easily sort by values either contained in the plot or in external characteristics of the cases is the best way to get the bang for your buck.

This advice extends to continuous observations as well. You could color/shape the points according to whether the residual is negative or positive, and then size the point according to the absolute (or squared) residual. This is IMO not necessary here though because of the simplicity of the observed values.

  • 1
    $\begingroup$ I do like this solution and content, I'm just waiting on other submissions. Thanks Andy. $\endgroup$ Apr 1, 2013 at 18:17
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    $\begingroup$ @Cam.Davidson.Pilon - I'm waiting on other submissions too! Because your model only has one predictor - sorting by the predicted proportion of defects would be synonymous as sorting by temperature (assuming a monotonic effect - as it appears in your graph). Perhaps someone will come along though with another solution that effectively allows one to see both the predicted proportion and original temperature (or something completely different). This display is good for seeing bad predictions, but is not very good for things like seeing non-linear effects. $\endgroup$
    – Andy W
    Apr 1, 2013 at 18:42
  • 1
    $\begingroup$ I'm happy to award the bounty to you. Sorting is the key to presenting it, and the paper linked from your previous post is what I'll be using. Thanks! $\endgroup$ Apr 5, 2013 at 3:55

The usual way to visualising the fit of a Bayesian logistic regression model with one predictor is to plot the predictive distribution together with the corresponding proportions. (Please, let me know if I understood your question)

An example using the popular Bliss' data set.

enter image description here

Code Below in R:


# Beetle data

ni = c(59, 60, 62, 56, 63, 59, 62, 60) # Number of individuals
no = c(6, 13, 18, 28, 52, 53, 61, 60) # Observed successes
dose = c(1.6907, 1.7242, 1.7552, 1.7842, 1.8113, 1.8369, 1.8610, 1.8839) # dose

dat = cbind(dose,ni,no)

ns = length(dat[,1])

# Log-posterior using a uniform prior on the parameters

logpost = function(par){
var = dat[,3]*log(plogis(par[1]+par[2]*dat[,1])) + (dat[,2]-dat[,3])*log(1-plogis(par[1]+par[2]*dat[,1]))

if( par[1]>-100000 ) return( sum(var) )
else return(-Inf)

# Metropolis-Hastings
N = 60000

samp <- metrop(logpost, scale = .35, initial = c(-60,33), nbatch = N)


burnin = 10000
thinning = 50

ind = seq(burnin,N,thinning)

mu1p =   samp$batch[ , 1][ind]

mu2p =   samp$batch[ , 2][ind]

# Visual tool

points = no/ni
# Predictive dose-response curve
DRL <- function(d) return(mean(plogis(mu1p+mu2p*d)))
DRLV = Vectorize(DRL)

v <- seq(1.55,2,length.out=55)
FL = DRLV(v)

  • $\begingroup$ @Cam.Davidson.Pilon I am sorry, my reputation does not allow me to include plots. But the idea is to plot the whole dose-response curve together with the observed proportions. $\endgroup$
    – Cerberis
    Apr 2, 2013 at 11:40
  • $\begingroup$ I've added the picture. You assume a different structure for the data in which the OP's does not directly extend to your example. The OP's data would be like if your ni = 23 and no = 7 and each of the 23 individuals has a different dose. You could make a similar plot for the OP's data though, (points are either placed at 0 or 1 on the Y axis, and you plot the function). See some examples of similar plots for logistic regression in the references I give on this answer. $\endgroup$
    – Andy W
    Apr 2, 2013 at 12:24
  • $\begingroup$ @AndyW ah the papers you link are quite useful! I'll have to take a closer look at those to see if I can apply them. $\endgroup$ Apr 2, 2013 at 14:14

I am responding to a request for alternative graphical techniques that show how well simulated failure events match observed failure events. The question arose in "Probabilistic Programming and Bayesian Methods for Hackers " found here. Here's my graphical approach:

Simulated vs Observed O-Ring Failures

Code found here.

  • $\begingroup$ Interesting -- can you offer any arguments on why to use this technique? Thanks for sharing! $\endgroup$ Nov 23, 2013 at 18:08
  • $\begingroup$ This is a probabilistic, not a deterministic result. Therefore, I looked for a representation that conveyed several things: 1) the range of observed and predicted events; 2: the probability distribution of the predicted failures; 3) the probability distribution of predicted non-failures; and 4) ranges where failure is more likely, non-failure is more likely, and ranges where failure and non-failure likelihoods overlap. This graph shows does all that to my eyes. $\endgroup$
    – user35216
    Dec 3, 2013 at 20:35
  • $\begingroup$ A few more additions/clarifications: 1) the temperature range of observed and predicted events; 5) actual observed failures and non-failures $\endgroup$
    – user35216
    Dec 3, 2013 at 20:48

I arrive here after reading "Probabilistic Programming and Bayesian Methods for Hackers". Disclaimer not sure if I'm committing a crime here, but as we have a "prediction" model with a classification problem, one way to evaluate and visualize its goodness is to apply some standard ML classification eval plots. I usually use AUC and AUCPR plots, but there's also DET curve as well. we base these plots on the posterior_probability given by the sampling procedure.

roc roc pr and det curves

Another alternative (the one that I like the most) is the "precision-recall threshold curves" you can clearly see here how "good" your prediction probability is, given a different probability threshold. ( I think is the closest to the one that is in the book and publication)

precision recall threshold

if you also define some probability threshold you can evaluate the confusion matrix among other metrics. At least for me, it is clearer when I have the AUC score, but again, maybe I'm getting too ahead of myself using this method for a bayesian model.

code here


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