glm.cmp {COMPoissonReg} | 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.
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, log p_i = w_i^T zeta.
glm.cmp(formula.lambda, formula.nu = ~ 1, formula.p = NULL, beta.init = NULL, gamma.init = NULL, zeta.init = NULL, ...) ## S3 method for class 'cmp' AIC(object, ..., k = 2) ## S3 method for class 'cmp' BIC(object, ...) ## S3 method for class 'cmp' coef(object, ...) ## S3 method for class 'cmp' deviance(object, ...) ## S3 method for class 'cmp' equitest(object, ...) ## S3 method for class 'cmp' leverage(object, ...) ## S3 method for class 'cmp' logLik(object, ...) ## S3 method for class 'cmp' nu(object, ...) ## S3 method for class 'cmp' parametric_bootstrap(object, reps = 1000, report.period = reps + 1, ...) ## S3 method for class 'cmp' predict(object, newdata = NULL, ...) ## S3 method for class 'cmp' print(x, ...) ## S3 method for class 'cmp' residuals(object, type = c("raw", "quantile"), ...) ## S3 method for class 'cmp' sdev(object, ...) ## S3 method for class 'cmp' summary(object, ...) ## S3 method for class 'cmp' vcov(object, ...) ## S3 method for class 'zicmp' AIC(object, ..., k = 2) ## S3 method for class 'zicmp' BIC(object, ...) ## S3 method for class 'zicmp' coef(object, ...) ## S3 method for class 'zicmp' deviance(object, ...) ## S3 method for class 'zicmp' equitest(object, ...) ## S3 method for class 'zicmp' leverage(object, ...) ## S3 method for class 'zicmp' logLik(object, ...) ## S3 method for class 'zicmp' nu(object, ...) ## S3 method for class 'zicmp' parametric_bootstrap(object, reps = 1000, report.period = reps + 1, ...) ## S3 method for class 'zicmp' predict(object, newdata = NULL, ...) ## S3 method for class 'zicmp' print(x, ...) ## S3 method for class 'zicmp' residuals(object, type = c("raw", "quantile"), ...) ## S3 method for class 'zicmp' sdev(object, ...) ## S3 method for class 'zicmp' summary(object, ...) ## S3 method for class 'zicmp' vcov(object, ...)
formula.lambda |
regression formula linked to |
formula.nu |
regression formula linked to |
formula.p |
regression formula linked to |
beta.init |
initial values for regression coefficients of |
gamma.init |
initial values for regression coefficients of |
zeta.init |
initial values for regression coefficients of |
... |
other model parameters, such as data |
object |
object of type 'cmp' or 'zicmp'. |
x |
object of type 'cmp' or 'zicmp'. |
k |
Penalty per parameter to be used in AIC calculation. |
newdata |
New covariates to be used for prediction. |
type |
Type of residual to be computed. |
reps |
Number of bootstrap repetitions. |
report.period |
Report progress every |
glm.cmp
produces an object of either class 'cmp' or 'zicmp', 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.