Hello,
I have specified the following model (a varying intercepts model for a reaction time (see figure for the sample data distribution) as a function of condition = [baseline, treatment1, treatment2]) with the bambi package:
b_model = Model(data)
results = b_model.fit(‘reaction_time ~ 1 + condition’, random = [‘1|subject_id’],
samples = 3000, tune = 2000)
As the title says: is there a possibility to model the response variable (in my case a reaction time) as lognormal (or any other right skewed option)? I have seen the possibility to specify a family, but can’t understand how to do it.
Thanks
Not sure if lognormal likelihood is implemented in bambi, @aloctavodia ?
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Hi @tankenot
You can specify a family by passing the argument family to fit, the available families are “gaussian”, “bernoulli” and “poisson”.
If your variable y is lognormal distributed then log(y) is normal distributed, then you can transform your variable y and keep the bambi model the same. Even if your data is not really lognormal taking the log can help with right skewed data.
@aloctavodia
Hi,
I’ve fit my model using the log(y) which is fine.
- Since I’m interested in comparing the means for baseline, treatment 1 and treatment 2, is there any way I can back transform my model parameters to obtain posterior distributions on my original scale (in seconds since I’m working with reaction times)? Modelling reaction time as normally distributed, I would obtain the difference: baseline vs. treatment 1 as the slope1 coefficient and baseline vs. treatment 2 as the slope2 coefficient.
Thanks
Sorry for the delay, yes you can apply the inverse of the (natural) logarithm function, that is you can do exp(coeff).
Thanks @aloctavodia. Would you say the same applies if the transform would be inverse instead (i.e., that if the y is transformed as (y)^(-1) the coefficients can be obtained by (coeff)^(-1)?
In general you can apply one function to the response variable and then its inverse to back-transform the parameters.
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