API

glm(F, D, args...; kwargs...)

Fit a generalized linear model to data. Alias for fit(GeneralizedLinearModel, ...). See fit for documentation.

fit(GeneralizedLinearModel, X, y, d, [l = canonicallink(d)]; <keyword arguments>)

Fit a generalized linear model to data. X and y can either be a matrix and a vector, respectively, or a formula and a data frame. d must be a UnivariateDistribution, and l must be a Link, if supplied.

Keyword Arguments

• dofit::Bool=true: Determines whether model will be fit
• wts::Vector=similar(y,0): Prior frequency (a.k.a. case) weights of observations.

Such weights are equivalent to repeating each observation a number of times equal to its weight. Do note that this interpretation gives equal point estimates but different standard errors from analytical (a.k.a. inverse variance) weights and from probability (a.k.a. sampling) weights which are the default in some other software. Can be length 0 to indicate no weighting (default).

• offset::Vector=similar(y,0): offset added to Xβ to form eta. Can be of

length 0

• verbose::Bool=false: Display convergence information for each iteration
• maxiter::Integer=30: Maximum number of iterations allowed to achieve convergence
• atol::Real=1e-6: Convergence is achieved when the relative change in

deviance is less than max(rtol*dev, atol).

• rtol::Real=1e-6: Convergence is achieved when the relative change in

deviance is less than max(rtol*dev, atol).

• minstepfac::Real=0.001: Minimum line step fraction. Must be between 0 and 1.
• start::AbstractVector=nothing: Starting values for beta. Should have the

same length as the number of columns in the model matrix.

lm(X, y, allowrankdeficient::Bool=false; wts=similar(y, 0))

An alias for fit(LinearModel, X, y, allowrankdeficient)

The arguments X and y can be a Matrix and a Vector or a Formula and a DataFrame.

The keyword argument wts can be a Vector specifying frequency weights for observations. Such weights are equivalent to repeating each observation a number of times equal to its weight. Do note that this interpretation gives equal point estimates but different standard errors from analytical (a.k.a. inverse variance) weights and from probability (a.k.a. sampling) weights which are the default in some other software.

negbin(formula,
       data,
       link;
       initialθ::Real=Inf,
       maxiter::Integer=30,
       atol::Real=1e-6,
       rtol::Real=1.e-6,
       verbose::Bool=false,
       kwargs...)

Fit a negative binomial generalized linear model to data, while simultaneously estimating the shape parameter θ. Extra arguments and keyword arguments will be passed to glm.

Keyword Arguments

• initialθ::Real=Inf: Starting value for shape parameter θ. If it is Inf then the initial value will be estimated by fitting a Poisson distribution.
• maxiter::Integer=30: See maxiter for glm
• atol::Real=1.0e-6: See atol for glm
• rtol::Real=1.0e-6: See rtol for glm
• verbose::Bool=false: See verbose for glm