The following errors: using DataFrames, GLM df = DataFrame(x1=[1,2,3,4], x2=[1,2,3,4], y=[1,1,0,0]) mdl = glm(@formula(y~x1+x2), df, Binomial(), LogitLink()) predict(mdl, df[:, [:x1, :x2]]) ERROR: LoadError: PosDefException: matrix is not positive definite; Cholesky factorization failed. On …

This is mistaken. Estimation of nonlinear models does entail solving first order conditions, numerically, rather than analytically, as can be done for linear models. With perfect collinearity, there is more than one solution to the first order conditions, and the matrix of second derivatives is sing…

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

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spinkney September 20, 2019, 2:31pm 10

This is a great answer. For such situations - with the consistency caveat applied - I’ve applied the Moore-Penrose Inverse with success…

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

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