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, ...)

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 |

... | 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`

.

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 scaleglance(fit)#> # 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