Tidy a(n) iZIP model object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for izip
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

# S3 method for izip
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

# S3 method for tsizip
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x	an object class 'tsizip' object, obtained from a call to tsglm.izip
conf.int	Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level	The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
exponentiate	Logical indicating whether or not to exponentiate the the coefficient estimates.
...	other arguments passed to or from other methods (currently unused).

Value

A tibble::tibble() with columns:

term

The name of the regression term.

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

statistic

The value of a test statistic to use in a hypothesis that the regression term is non-zero.

p.value

The two-sided p-value associated with the observed statistic based on asymptotic normality.

conf.low

Lower bound on the confidence interval for the estimate.

conf.high

Upper bound on the confidence interval for the estimate.

A tibble::tibble() with columns:

term

The name of the regression term.

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

statistic

The value of a test statistic to use in a hypothesis that the regression term is non-zero.

p.value

The two-sided p-value associated with the observed statistic based on asymptotic normality.

conf.low

Lower bound on the confidence interval for the estimate.

conf.high

Upper bound on the confidence interval for the estimate.

Examples

data(bioChemists)
M_bioChem <- glm.izip(art ~ ., data = bioChemists)
tidy(M_bioChem)
#> # A tibble: 6 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   0.325    0.118       2.74  6.10e- 3
#> 2 femWomen     -0.229    0.0630     -3.63  2.84e- 4
#> 3 marMarried    0.159    0.0706      2.25  2.44e- 2
#> 4 kid5         -0.190    0.0463     -4.09  4.24e- 5
#> 5 phd           0.0101   0.0303      0.334 7.38e- 1
#> 6 ment          0.0247   0.00217    11.4   7.26e-30
data(bioChemists)
M_bioChem <- glm.izip(art ~ ., data = bioChemists)
tidy(M_bioChem)
#> # A tibble: 6 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   0.325    0.118       2.74  6.10e- 3
#> 2 femWomen     -0.229    0.0630     -3.63  2.84e- 4
#> 3 marMarried    0.159    0.0706      2.25  2.44e- 2
#> 4 kid5         -0.190    0.0463     -4.09  4.24e- 5
#> 5 phd           0.0101   0.0303      0.334 7.38e- 1
#> 6 ment          0.0247   0.00217    11.4   7.26e-30