This function attempts to return, or compute, p-values of hurdle and zero-inflated models.

```
# S3 method for zcpglm
p_value(model, component = c("all", "conditional", "zi", "zero_inflated"), ...)
# S3 method for zeroinfl
p_value(
model,
component = c("all", "conditional", "zi", "zero_inflated"),
method = NULL,
verbose = TRUE,
...
)
```

## Arguments

model |
A statistical model. |

component |
Should all parameters, parameters for the conditional model,
or for the zero-inflated part of the model be returned? Applies to models
with zero-inflated component. `component` may be one of `"conditional"` ,
`"zi"` , `"zero-inflated"` , `"dispersion"` or `"all"`
(default). May be abbreviated. |

... |
Arguments passed down to `standard_error_robust()` when confidence intervals or p-values based on robust standard errors should be computed. Only available for models where `method = "robust"` is supported. |

method |
If `"robust"` , and if model is supported by the sandwich or clubSandwich packages, computes p-values based on robust covariance matrix estimation. |

verbose |
Toggle warnings and messages. |

## Value

A data frame with at least two columns: the parameter names and the
p-values. Depending on the model, may also include columns for model
components etc.

## Examples

```
if (require("pscl", quietly = TRUE)) {
data("bioChemists")
model <- zeroinfl(art ~ fem + mar + kid5 | kid5 + phd, data = bioChemists)
p_value(model)
p_value(model, component = "zi")
}
#> Parameter p Component
#> 5 zero_(Intercept) 0.0446362 zero_inflated
#> 6 zero_kid5 0.4146050 zero_inflated
#> 7 zero_phd 0.0248504 zero_inflated
```