glm.cmp | R Documentation |
Fit COM-Poisson regression using maximum likelihood estimation. Zero-Inflated COM-Poisson can be fit by specifying a regression for the overdispersion parameter.
glm.cmp( formula.lambda, formula.nu = ~1, formula.p = NULL, data = NULL, init = NULL, fixed = NULL, control = NULL, ... )
formula.lambda |
regression formula linked to |
formula.nu |
regression formula linked to |
formula.p |
regression formula linked to |
data |
An optional data.frame with variables to be used with regression formulas. Variables not found here are read from the envionment. |
init |
A data structure that specifies initial values. See the helper function get.init. |
fixed |
A data structure that specifies which coefficients should remain fixed in the maximum likelihood procedure. See the helper function get.fixed. |
control |
A control data structure. See the helper function
get.control. If |
... |
other arguments, such as |
The COM-Poisson regression model is
y_i ~ CMP(lambda_i, nu_i), log lambda_i = x_i^T beta, log nu_i = s_i^T gamma.
The Zero-Inflated COM-Poisson regression model assumes that y_i is 0 with probability p_i or y_i^* with probability 1 - p_i, where
y_i^* ~ CMP(lambda_i, nu_i), log lambda_i = x_i^T beta, log nu_i = s_i^T gamma, logit p_i = w_i^T zeta.
glm.cmp
produces an object of either class cmpfit
or
zicmpfit
, depending on whether zero-inflation is used in the model.
From this object, coefficients and other information can be extracted.
Kimberly Sellers, Thomas Lotze, Andrew Raim
Kimberly F. Sellers & Galit Shmueli (2010). A Flexible Regression Model for Count Data. Annals of Applied Statistics, 4(2), 943-961.
Kimberly F. Sellers and Andrew M. Raim (2016). A Flexible Zero-Inflated Model to Address Data Dispersion. Computational Statistics and Data Analysis, 99, 68-80.
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