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.
Arguments
- x
A
clmobject returned fromordinal::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"confidence intervals, passed to thetypeargument ofordinal::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
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
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()
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
# 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
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 NA NA
#> 2 2|3 1.14 0.510 2.24 2.48e- 2 NA NA
#> 3 3|4 3.38 0.638 5.29 1.21e- 7 NA NA
#> 4 4|5 4.94 0.751 6.58 4.66e-11 NA NA
#> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 1.20 3.52
#> 6 contactyes 1.35 0.660 2.04 4.13e- 2 0.284 2.47
#> 7 tempwarm:co… 0.360 0.924 0.389 6.97e- 1 -1.17 1.89
#> # ℹ 1 more variable: coef.type <chr>
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
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1|2 0.244 0.545 -2.59 9.66e- 3 0.0837 0.710
#> 2 2|3 3.14 0.510 2.24 2.48e- 2 1.16 8.52
#> 3 3|4 29.3 0.638 5.29 1.21e- 7 8.38 102.
#> 4 4|5 140. 0.751 6.58 4.66e-11 32.1 610.
#> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 2.58 40.2
#> 6 contactyes 3.85 0.660 2.04 4.13e- 2 1.05 14.0
#> 7 tempwarm:co… 1.43 0.924 0.389 6.97e- 1 0.234 8.76
#> # ℹ 1 more variable: coef.type <chr>
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