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.
A clm object returned from ordinal::clm().
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE.
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.
Whether to use "profile" or "Wald" confidence
intervals, passed to the type argument of ordinal::confint.clm().
Defaults to "profile".
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. Two exceptions here are:
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.
tidy, ordinal::clm(), ordinal::confint.clm()
Other ordinal tidiers:
augment.clm(),
augment.polr(),
glance.clm(),
glance.clmm(),
glance.polr(),
glance.svyolr(),
tidy.clmm(),
tidy.polr(),
tidy.svyolr()
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.
# load libraries for models and data
library(ordinal)
# fit model
fit <- clm(rating ~ temp * contact, data = wine)
# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 7 × 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 × 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 × 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 × 6
#> edf AIC BIC logLik df.residual nobs
#> <int> <dbl> <dbl> <logLik> <dbl> <dbl>
#> 1 7 187. 203. -86.4162 65 72
augment(fit, type.predict = "prob")
#> # A tibble: 72 × 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
#> # ℹ 62 more rows
augment(fit, type.predict = "class")
#> # A tibble: 72 × 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
#> # ℹ 62 more rows
# ...and again with another model specification
fit2 <- clm(rating ~ temp, nominal = ~contact, data = wine)
tidy(fit2)
#> # A tibble: 9 × 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 × 6
#> edf AIC BIC logLik df.residual nobs
#> <int> <dbl> <dbl> <logLik> <dbl> <dbl>
#> 1 9 190. 211. -86.20855 63 72