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 cross 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 clm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  conf.type = c("profile", "Wald"), exponentiate = FALSE, ...)

Arguments

x

A clm object returned from ordinal::clm().

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.

conf.type

Whether to use "profile" or "Wald" confidendence intervals, passed to the type argument of ordinal::confint.clm(). Defaults to "profile".

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 FALSE.

...

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

In broom 0.7.0 the coefficient_type column was renamed to coef.type, and the contents were changed as well.

Note that intercept type coefficients correspond to alpha parameters, location type coefficients correspond to beta parameters, and scale type coefficients correspond to zeta parameters.

See also

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

p.value

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

statistic

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

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

library(ordinal) fit <- clm(rating ~ temp * contact, data = wine) tidy(fit)
#> # A tibble: 7 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 location #> 7 tempwarm:contactyes 0.360 0.924 0.389 6.97e- 1 location
tidy(fit, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 7 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 NA NA intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 NA NA intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 NA NA intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 NA NA intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 1.20 3.52 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 0.284 2.47 location #> 7 tempwarm:c… 0.360 0.924 0.389 6.97e- 1 -1.17 1.89 location
tidy(fit, conf.int = TRUE, conf.type = "Wald", exponentiate = TRUE)
#> # A tibble: 7 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 0.244 0.545 -2.59 9.66e- 3 0.0837 0.710 intercept #> 2 2|3 3.14 0.510 2.24 2.48e- 2 1.16 8.52 intercept #> 3 3|4 29.3 0.638 5.29 1.21e- 7 8.38 102. intercept #> 4 4|5 140. 0.751 6.58 4.66e-11 32.1 610. intercept #> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 2.58 40.2 location #> 6 contactyes 3.85 0.660 2.04 4.13e- 2 1.05 14.0 location #> 7 tempwarm:c… 1.43 0.924 0.389 6.97e- 1 0.234 8.76 location
glance(fit)
#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 7 187. 203. -86.4 65 72
augment(fit, type.predict = "prob")
#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <dbl> #> 1 2 cold no 0.562 #> 2 3 cold no 0.209 #> 3 3 cold yes 0.435 #> 4 4 cold yes 0.0894 #> 5 4 warm no 0.190 #> 6 4 warm no 0.190 #> 7 5 warm yes 0.286 #> 8 5 warm yes 0.286 #> 9 1 cold no 0.196 #> 10 2 cold no 0.562 #> # … with 62 more rows
augment(fit, type.predict = "class")
#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <fct> #> 1 2 cold no 2 #> 2 3 cold no 2 #> 3 3 cold yes 3 #> 4 4 cold yes 3 #> 5 4 warm no 3 #> 6 4 warm no 3 #> 7 5 warm yes 4 #> 8 5 warm yes 4 #> 9 1 cold no 2 #> 10 2 cold no 2 #> # … with 62 more rows
fit2 <- clm(rating ~ temp, nominal = ~ contact, data = wine) tidy(fit2)
#> # A tibble: 9 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2.(Intercept) -1.32 0.562 -2.35 0.0186 intercept #> 2 2|3.(Intercept) 1.25 0.475 2.63 0.00866 intercept #> 3 3|4.(Intercept) 3.55 0.656 5.41 0.0000000625 intercept #> 4 4|5.(Intercept) 4.66 0.860 5.42 0.0000000608 intercept #> 5 1|2.contactyes -1.62 1.16 -1.39 0.164 intercept #> 6 2|3.contactyes -1.51 0.591 -2.56 0.0105 intercept #> 7 3|4.contactyes -1.67 0.649 -2.58 0.00985 intercept #> 8 4|5.contactyes -1.05 0.897 -1.17 0.241 intercept #> 9 tempwarm 2.52 0.535 4.71 0.00000250 location
glance(fit2)
#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 9 190. 211. -86.2 63 72