# GLM.jl LogisticRegression errors: matrix is not positive definite; Cholesky factorization failed

**URL:** <https://discourse.julialang.org/t/glm-jl-logisticregression-errors-matrix-is-not-positive-definite-cholesky-factorization-failed/28981>\
**Category:** Statistics\
**Tags:** question, glm\
**Created:** [September 20, 2019, 4:37am UTC](https://discourse.julialang.org/t/glm-jl-logisticregression-errors-matrix-is-not-positive-definite-cholesky-factorization-failed/28981 "2019-09-20T04:37:02Z")\
**Posts on this page:** 1\
**Showing post:** 13

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**Author:** ![dmbates](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmbates/32/44_2.png) [@dmbates](https://discourse.julialang.org/u/dmbates)\
**Post date:** [June 8, 2022, 2:12pm UTC](https://discourse.julialang.org/t/glm-jl-logisticregression-errors-matrix-is-not-positive-definite-cholesky-factorization-failed/28981/13 "2022-06-08T14:12:28Z")

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A similar problem reported in [LoadError: PosDefException: matrix is not positive definite; Cholesky factorization failed · Issue #611 · JuliaStats/MixedModels.jl · GitHub](https://github.com/JuliaStats/MixedModels.jl/issues/611) turned out to be due to scaling of the response and model matrix - because of weights in that case. That example was a linear mixed model (LMM). With generalized linear mixed models (GLMMs) the problem is even more likely because of the iteratively re-weighted least squares (IRLS) algorithm and its penalized version (PIRLS). Obtaining reasonable starting estimates to avoid this is not easy.

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