kshedden / CondReg.jl

Conditional logistic regression in Julia

Conditional regression models in Julia

The CondReg.jl Julia package implements conditional logistic and conditional Poisson regression. These are techniques for regression analysis with grouped data, where each group has a distinct and unknown intercept.

Usage:

First we generate grouped Bernoulli data with group-specific random intercepts, and fit a logistic regression model using conditional logistic regression.

using CondReg, DataFrames, StatsModels, StableRNGs, Distributions
import StatsBase

rng = StableRNG(123)

n = 500 # sample size
m = 50 # number of groups
id = StatsBase.sample(rng, 1:m, n, replace=true)
sort!(id)
u = 0.25*randn(rng, m) # group intercepts
x1 = randn(rng, n) # first covariate
x2 = randn(rng, n) # second covariate

# A linear predictor, with group intercepts
lp = x1 + u[id]

# A vector of probabilites
pr = 1 ./ (1 .+ exp.(-lp))

# Vector of binary outcomes
y = rand.(rng, Binomial.(1, pr))

# da is a DataFrame containing y, x1, x2, and id.
da = DataFrame(y=y, x1=x1, x2=x2, id=id)

# Fit a conditional logit model
m1 = fit(ConditionalLogitModel, @formula(y ~ x1 + x2), da, da[:, :id])

Next we generate data from a Poisson model with group-specific intercepts and fit a model using conditional Poisson regression.

# A vector of expected values
ev = exp.(lp)
y = rand.(rng, Poisson.(ev))
da[:, :y] = y

# Fit a conditional Poisson model
m2 = fit(ConditionalPoissonModel, @formula(y ~ x1 + x2), da, da[:, :id])

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