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15.4 Negative-binomial-2-log generalized linear model (negative binomial regression)

Stan also supplies a single function for a generalized linear model with negative binomial likelihood and log link function, i.e. a function for a negative binomial regression. This provides a more efficient implementation of negative binomial regression than a manually written regression in terms of a negative binomial likelihood and matrix multiplication.

15.4.1 Probability mass function

If xRnm,αRn,βRm,ϕR+, then for yNn, NegBinomial2LogGLM(y | x,α,β,ϕ)=1inNegBinomial2(yi | exp(αi+xiβ),ϕ).

15.4.2 Sampling statement

y ~ neg_binomial_2_log_glm(x, alpha, beta, phi)

Increment target log probability density with neg_binomial_2_log_glm_lupmf(y | x, alpha, beta, phi).
Available since 2.19

15.4.3 Stan functions

real neg_binomial_2_log_glm_lpmf(int y | matrix x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.23

real neg_binomial_2_log_glm_lupmf(int y | matrix x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25

real neg_binomial_2_log_glm_lpmf(int y | matrix x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.23

real neg_binomial_2_log_glm_lupmf(int y | matrix x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25

real neg_binomial_2_log_glm_lpmf(array[] int y | row_vector x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.23

real neg_binomial_2_log_glm_lupmf(array[] int y | row_vector x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25

real neg_binomial_2_log_glm_lpmf(array[] int y | row_vector x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.23

real neg_binomial_2_log_glm_lupmf(array[] int y | row_vector x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25

real neg_binomial_2_log_glm_lpmf(array[] int y | matrix x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.18

real neg_binomial_2_log_glm_lupmf(array[] int y | matrix x, real alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25

real neg_binomial_2_log_glm_lpmf(array[] int y | matrix x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi.
Available since 2.18

real neg_binomial_2_log_glm_lupmf(array[] int y | matrix x, vector alpha, vector beta, real phi)
The log negative binomial probability mass of y given log-location alpha + x * beta and inverse overdispersion parameter phi dropping constant additive terms.
Available since 2.25