# Extracting model params/equivalent of broom tidiers in julia?
**URL:** https://discourse.julialang.org/t/extracting-model-params-equivalent-of-broom-tidiers-in-julia/92560
**Category:** General Usage
**Tags:** r, statsmodels, modelling
**Created:** [January 5, 2023, 5:04pm UTC](https://discourse.julialang.org/t/extracting-model-params-equivalent-of-broom-tidiers-in-julia/92560 "2023-01-05T17:04:22Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@jonfoong](https://discourse.julialang.org/u/jonfoong)
#### Post date: [January 5, 2023, 5:04pm UTC](https://discourse.julialang.org/t/extracting-model-params-equivalent-of-broom-tidiers-in-julia/92560/1 "2023-01-05T17:04:22Z")
Hi I am new to Julia and am using it to run mixed models since it’s so much faster. However I’ve no experience with Julia and it feels like online support is scarce. I would like help with extracting model parameters but not sure how to.
In R this is a trivial task with broom tidiers, which gets me everything I want incl random effects, confints, etc. Is there a way to do this in julia?
```julia
using MixedModels
using RData
path = joinpath(dirname(pwd()), "liberalism_conjoint\\saved\\df_reduced_julia.rds")
df = load(path)
m1 = fit(MixedModel, @formula(prefer ~ 1 + X1+(1+X1|country)), df)
```
---
### Author:  [@palday](https://discourse.julialang.org/u/palday)
#### Post date: [January 6, 2023, 8:13pm UTC](https://discourse.julialang.org/t/extracting-model-params-equivalent-of-broom-tidiers-in-julia/92560/2 "2023-01-06T20:13:14Z")
`coeftable`, `coef`, `coefnames`, `stderror`, `ranef`, `raneftables`, etc. are all mentioned in [the docs](https://juliastats.org/MixedModels.jl/stable/api/) and have docstrings for online help. There currently isn’t a `confint` method defined (though that will likely change soon); instead you can compute the shortest coverage interval with [`shortestcovint`](https://juliastats.org/MixedModels.jl/stable/api/#MixedModels.shortestcovint) on the results of [the parametric bootstrap](https://juliastats.org/MixedModels.jl/stable/bootstrap/).
In addition to MixedModels itself, there are a number of related packages with their own documentation, e.g. [MixedModelsSim](https://repsychling.github.io/MixedModelsSim.jl/stable/), [MixedModelsExtras](https://palday.github.io/MixedModelsExtras.jl/stable/), and [MixedModelsMakie](https://palday.github.io/MixedModelsMakie.jl/stable/api/#MixedModelsMakie.jl-API). For MixedModelsMakie, you’ll need to load an appropriate Makie backend, e.g. CairoMakie or GLMakie.
There is also [JellyMe4](https://github.com/palday/JellyMe4.jl/) which provides RCall support for MixedModels, enabling you to move models back and forth between Julia’s MixedModels and R’s lme4, which gives you access to the entire R ecosystem.
Finally, @dmbates, @kliegl and I regularly teach a course on MixedModels. The materials are hosted under the RePsychLing organization on GitHub: [RePsychLing · GitHub](https://github.com/orgs/RePsychLing/repositories?q=smlp&type=all&language=&sort=)