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 svyolr tidy( x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, p.values = FALSE, ... )

x | A |
---|---|

conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |

conf.level | The confidence level to use for the confidence interval
if |

exponentiate | Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to |

p.values | Logical. Should p-values be returned,
based on chi-squared tests from |

... | Additional arguments. Not used. Needed to match generic
signature only. |

In `broom 0.7.0`

the `coefficient_type`

column was renamed to
`coef.type`

, and the contents were changed as well. Now the contents
are `coefficient`

and `scale`

, rather than `coefficient`

and `zeta`

.

Calculating p.values with the `dropterm()`

function is the approach
suggested by the MASS package author
https://r.789695.n4.nabble.com/p-values-of-plor-td4668100.html. This
approach is computationally intensive so that p.values are only
returned if requested explicitly. Additionally, it only works for
models containing no variables with more than two categories. If this
condition is not met, a message is shown and NA is returned instead of
p-values.

Other ordinal tidiers:
`augment.clm()`

,
`augment.polr()`

,
`glance.clmm()`

,
`glance.clm()`

,
`glance.polr()`

,
`glance.svyolr()`

,
`tidy.clmm()`

,
`tidy.clm()`

,
`tidy.polr()`

A `tibble::tibble()`

with columns:

Upper bound on the confidence interval for the estimate.

Lower bound on the confidence interval for the estimate.

The estimated value of the regression term.

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

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

The standard error of the regression term.

The name of the regression term.

library(MASS) fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) tidy(fit, exponentiate = TRUE, conf.int = TRUE)#> #>#> # A tibble: 8 x 7 #> term estimate std.error statistic conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 InflMedium 1.76 0.105 5.41 1.44 2.16 coefficient #> 2 InflHigh 3.63 0.127 10.1 2.83 4.66 coefficient #> 3 TypeApartment 0.564 0.119 -4.80 0.446 0.712 coefficient #> 4 TypeAtrium 0.693 0.155 -2.36 0.511 0.940 coefficient #> 5 TypeTerrace 0.336 0.151 -7.20 0.249 0.451 coefficient #> 6 ContHigh 1.43 0.0955 3.77 1.19 1.73 coefficient #> 7 Low|Medium 0.609 0.125 -3.97 NA NA scale #> 8 Medium|High 2.00 0.125 5.50 NA NA scale#> # A tibble: 1 x 7 #> edf logLik AIC BIC deviance df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 8 -1740. 3495. 3539. 3479. 1673 1681