Questions tagged [brms]
brms is an R package interfacing stan for Bayesian analysis
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Is multiple imputation in multiple membership models with three levels possible?
I have been looking into multiple-membership models with brms and have seen that it is possible to use this framework for ordinal outcomes. We are working with some education data which has school ...
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Convert a simple phylogenetic model example from BRMS to INLA
I am trying to convert the simple phylogenetic model described here in BRMS into INLA.
Although I think I have implemented the model in INLA correctly, I can't seem to figure out how to translate the ...
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Deriving Bayesian Credible Intervals for AUC using R brms
I am trying to estimate the posterior distribution for the AUC of a predictive biomarker using R brms. However, whenever I calculate the AUC using the posterior distribution of the model parameters, ...
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Does brms automatically standardise data and/or coefficients?
I'm running some regression models in R using brms and lme4.
When I run a Bayesian model:
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How can I sample from a shifted and scaled Student-t distribution with a specified mean and sd in R?
I'm currently building some Bayesian models with the brms package and the default intercept prior is student_t(3, 0, 6.3) and so ...
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Interaction between two factors as Random effects in mixed model in R
I would like to know how to write random effects of two interacting factors. For example, I have 6 species which were planted in 48 plots and replicated in two blocks. There are in total 48 ...
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103
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How to interpret the evidence ratio using BRMS in R?
I am running a frequentist multi-level meta-analysis. However a reviewer has requested a bayesian alternative, so I can provide Bayes Factors.
My summary of my code is as follows:
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Hierachical Bayesian modelling using brms: how to insert a prior that reflects cut-offs of Reaction Times distribution?
I am running a hierarchical Bayesian model using brms on reaction times (RTs) of a GoNogo task. The predictors are categorical and include the 3 stimuli/condition that participants observed and the 2 ...
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Bayesian GLM where the response variable is count classes
Description of data
I have to analyze some data where the response variable is the counts of number of insects observed feeding on a bait at many time points. The treatments are three different types ...
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brms: prior of categorical predictors in Multilevel Bayesian model
I want to run a Bayesian Multilevel model on reaction times using two categorical factors (conditionStimuli = 3 levels; sequenceTrials = 2 levels). Initially, I run the model with default priors:
<...
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fit a model with a skewed distribution response with brms R package
I have some basics with STAN, and would like to move to the brms package for data analysis. I would like to correlate a metric to several variable (6 traits) and I also included 2 random effects. I ...
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How to improve model accuracy fitted by extreme skewness distribution in brm()
I first fit a bayesian model by brm() in R. Because I’m not very familiar with Bayesian frameworks, I didn't specify priors distribution. My data ranges from -1~12 ...
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How can I combine credible intervals when there are differences in slope terms from interactions?
Let's say I run this model with a continuous by categorical interaction where Species has 3 levels.
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Can I Use the loo function to help me choose between a Poisson and Bernouilli distribution in bayesian
I have two models exactly similar, but I’m using a Poisson distribution for one and a Bernoulli distribution for the other. Can I trust the information coming out of loo to help me choose?
The ...
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Bayesian Regression: Taking the mean over posterior estimates
I am running a Bayesian regression with a relatively large dataset and many repetitions. I would like to calculate the predicted values and graph them together with the actual value. However, my ...
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fitting multilevel models for data that is both multiple membership and repeated measures
I have a longitudinal dataset with 5 repeated measures, where individuals are nested within counties and may have moved to a new county during the study period, e.g.,
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Analysing repeated movement trajectories - is a GP the right approach?
I have some data where I have multiple conditions per subject (humans, in this case), who made repeated movements under these conditions. I'm interested in the variability of these movements.
The data ...
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Modelling time both as a fixed effect and as part of an autocorrelation structure?
I want to build a model to assess whether a species is declining in three different national parks.
My dependent variable is count data of the species and I have date, park, season and food as ...
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Help in understanding zero inflated neg binomial model summary
I'm writing this topic because I would need to get some more information about model conversion in brms (zero-inflated_negbinomial) model.
Let's say I have this model result :
Where I want to model ...
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115
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Difference in fitting to right censored data between MLE and Bayesian method
I am fitting a Weibull curve to right censored data. I am doing it by general MLE method using Survival::survreg() as well as Bayesian method using brms::brm.
I am pretty sure that I am getting the ...
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74
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How to interpret incidence rate ratio for an interaction term in a negative binomial regression in R?
I am trying to interpret the output of a brm() zero_inflated_negbinomial model in R. I would ...
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66
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missing data with longitudinal data
I have some longitudinal data that I want to model using multilevel modeling in brms. I have two time points. I have missing values on the second time point of the outcome. But otherwise complete data....
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Interpolating curve equation from model data
I need to define an equation to represent a series of points from a model. As they are predictions from an already fit non-linear regression, noise shouldn't really be an issue. I have seen many ...
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Interpreting non linear brms output - estimates of posterior cooefficient and user supplied formula
I am a bit confused about how to approximate the equation from a nonlinear model constructed in brms, and was hoping someone could explain it to me.
Say I have the below model:
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Is there any way to plot conditional effects on their original scale?
I fit an GLMM with brms effect with normalized continuous covariates. How can I plot conditional effect of one of my continuous covariates in its rescaled (original) scale?
I am using ...
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Default Priors for Intercept and Standard Deviations in R package brms
The only resource I found explaining the default priors in brms is its manual (newest version, updated 03/14/2021) for function set_prior().
For the intercept, the manual does not specify how the ...
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114
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Differing posterior predictive checks for logistic binomial model with and without addition-terms
[apologies for cross posting]
I’m fitting a logistic binomial model where the response variable is the sum of how many times a target picture was looked at during a certain time period out of how many ...
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136
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How to write a multilevel GLM poisson model in mathematical notation?
I have constructed a Poisson GLM (modeling it Bayesian in brms) as a part of a social network analysis, predicting counts of outgroup/ingroup connections over two different cohorts/years of a study ...
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binomial mixed model random effects structure
I am having difficulty with the structure of a binomial mixed effects model. I'm using brms, but my question is more about model design than bayesian modeling so I hope to get some good insights from ...
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392
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Plot predict probabilities wiht ggplot2 and brms [closed]
I'm a PhD student in psycholinguistics and I'm having trouble in modeling some ordinal data. I have an ordinal response (completely disagree, disagree, neutral, agree, completely agree) and two ...
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103
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How do I get around this "Argument 'coef' may not be specified when using boundaries."
I have a model, the brms code is given below. It is a system of equations (I am estimating demand for two categories of goods). Economic theory tells me that the intercepts have to be restricted to ...
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Bayesian GLM/ANOVA in brms -- what is the "equivalent" to frequentist post-hoc? Does it matter? [duplicate]
I'm running a one-way Bayesian GLM (in brms in R) between three groups (Class), using a heteroskedasticity adjustment:
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Method for Predicting Longitudinal Diagnostic Switching and Instability
Context
Within my field (neuropsychology), there is a well-known issue for some individuals to have very unstable diagnoses overtime. My area of interest is in dementia where the ideal diagnostic ...
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