Can I use lmBF() for logistic regression?
Hi,
I need to perform a logistic regression with two categorical predictor variables (two levels each). I am trying to figure out whether I can use the lmBF() function from the BayesFactor package to do this. I could not find any information on this in the documentation. Bringing up ?regressionBF in R gives me this information:
The vector of observations y is assumed to be distributed as: y ~ Normal(α 1 + Xβ, σ^2 I).
This suggests to me that binomial ys are not appropriate.
I did go ahead and tried it anyways. And lmBF will happily fit the models and give me results. I just don't know whether they actually mean anything. Specifically, I compared the output of
glmer(y ~ f1 + f2 + f1:f2+ (1|subj) + (1|item), data=data, family = binomial)
with the output of
lmBF(y ~ f1 + f2 + f1:f2, whichRandom = c("subj", "item"), data=data)
and they corresponded quite closely.
I constructed a simpler example (without the random effects etc.) to test whether the outcomes converge. And they seem to:
set.seed(3)
data <- data.frame(y = rbinom(100, 1, .5),
f1 = as.factor(sample(rep(LETTERS[1:2], 50))),
f2 = as.factor(sample(rep(letters[1:2], 50))))
# Traditional log. regression:
m.trad <- glm(y ~ .,family=binomial(link='logit'),data=data)
# Using lmBF:
m.bf <- lmBF(y ~ ., data=data)
chains <- posterior(m.bf, iterations = 10000)
coeff.est <- colMeans(chains)
# Comparing param. estimates for observation in f1 = B and f2 = b
# Trad. glm:
invlogit <- function(X) { 1 / (1+exp(-X)) }
invlogit(sum(coef(m.trad)))
# lmBF:
coeff.est['mu'] + coeff.est['f1-B'] + coeff.est['f2-b']
Can someone put my mind at ease and confirm that I am doing this right and that lmBF does return meaningful parameter estimates (etc.) for binomially distributed ys?
Thanks a lot!
- Florian
Comments
EJ Wagenmakers was visiting my faculty today so I had a chance to ask him in person. Logistic regression is not yet possible but they'll start working on it soon.
That means, what I outlined above should not be used.
https://sincrenete.blogspot.am/2017/07/logistic-regression-explained-and.html I also put here an example of Logistic regression done by R. In a case of any question I am ready. Thanks ))
@EJ is there a time-line for including Bayesian logistic regression to JASP or R BayesFactor?
Not a strict timeline but it is the logical next step
Yeah, but is it the logistical next step?
(my wife advised against this joke, but I am my own man!)
Hello! Is there a way either in JASP or using the BayesFactor package to do a binomial regression model without Random factors like this: Camping~Age+Gender+Explain_num+Order where 'Camping' is binomial?
Dear dontchaknowjo,
I can't speak about the BayesFactor package, however, JASP currently does not support logistic regression in the Bayesian framework. Nevertheless, you can still fit a frequentist logistic regression in the Regression module.
Cheers,
Frantisek