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 epi.2by2
tidy(x, parameters = c("moa", "stat"), ...)

## Arguments

x |
A `epi.2by2` object produced by a call to `epiR::epi.2by2()` |

parameters |
Return measures of association (`moa` ) or test statistics (`stat` ),
default is `moa` (measures of association) |

... |
Additional arguments. Not used. Needed to match generic
signature only. **Cautionary note:** Misspelled arguments will be
absorbed in `...` , where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Details

The tibble has a column for each of the measures of association
or tests contained in `massoc`

when `epiR::epi.2by2()`

is called.

## See also

## Value

A `tibble::tibble()`

with columns:

conf.highUpper bound on the confidence interval for the estimate.

conf.lowLower bound on the confidence interval for the estimate.

dfDegrees of freedom used by this term in the model.

p.valueThe two-sided p-value associated with the observed statistic.

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

termThe name of the regression term.

estimateEstimated measure of association

## Examples

#> Package epiR 2.0.19 is loaded

#> Type help(epi.about) for summary information

#> Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses

#>

#> # A tibble: 13 x 4
#> term estimate conf.low conf.high
#> <chr> <dbl> <dbl> <dbl>
#> 1 PR.strata.wald 4.01 1.43 11.2
#> 2 PR.strata.taylor 4.01 1.43 11.2
#> 3 PR.strata.score 4.01 1.49 10.8
#> 4 OR.strata.wald 4.03 1.43 11.3
#> 5 OR.strata.cfield 4.03 NA NA
#> 6 OR.strata.score 4.03 1.49 10.9
#> 7 OR.strata.mle 4.02 1.34 14.4
#> 8 ARisk.strata.wald 0.448 0.0992 0.797
#> 9 ARisk.strata.score 0.448 0.142 0.882
#> 10 PARisk.strata.wald 0.176 -0.0225 0.375
#> 11 PARisk.strata.piri 0.176 0.0389 0.314
#> 12 AFRisk.strata.wald 0.750 0.301 0.911
#> 13 PAFRisk.strata.wald 0.542 0.0361 0.782