kumulant

Log

@Serializable
@SerialName(value = "Log")
data object Log : Link(source)

mu = exp(eta). Poisson likelihood; expects y >= 0.

Functions

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open override fun curvature(eta: Double): Double

Second derivative of the per-observation negative log-likelihood at eta.

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open override fun invMean(eta: Double): Double

Inverse link: maps the linear predictor eta to the response mean.

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open override fun loss(eta: Double, y: Double): Double

mu - y * eta = exp(eta) - y * eta; drops the log(y!) constant.

curvature

open override fun curvature(eta: Double): Double(source)

Second derivative of the per-observation negative log-likelihood at eta.

invMean

open override fun invMean(eta: Double): Double(source)

Inverse link: maps the linear predictor eta to the response mean.

loss

open override fun loss(eta: Double, y: Double): Double(source)

mu - y * eta = exp(eta) - y * eta; drops the log(y!) constant.