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.
Usage
# S3 method for class 'clmm'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)Arguments
- x
A
clmmobject returned fromordinal::clmm().- 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.- 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:
Note
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::clmm(), ordinal::confint.clm()
Other ordinal tidiers:
augment.clm(),
augment.polr(),
glance.clm(),
glance.clmm(),
glance.polr(),
glance.svyolr(),
tidy.clm(),
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 <- clmm(rating ~ temp + contact + (1 | judge), data = wine)
# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 6 × 6
#> term estimate std.error statistic p.value coef.type
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1|2 -1.62 0.682 -2.38 1.74e- 2 intercept
#> 2 2|3 1.51 0.604 2.51 1.22e- 2 intercept
#> 3 3|4 4.23 0.809 5.23 1.72e- 7 intercept
#> 4 4|5 6.09 0.972 6.26 3.82e-10 intercept
#> 5 tempwarm 3.06 0.595 5.14 2.68e- 7 location
#> 6 contactyes 1.83 0.513 3.58 3.44e- 4 location
tidy(fit, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 6 × 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 -1.62 0.682 -2.38 1.74e- 2 -2.75 -0.501 intercept
#> 2 2|3 1.51 0.604 2.51 1.22e- 2 0.520 2.51 intercept
#> 3 3|4 4.23 0.809 5.23 1.72e- 7 2.90 5.56 intercept
#> 4 4|5 6.09 0.972 6.26 3.82e-10 4.49 7.69 intercept
#> 5 temp… 3.06 0.595 5.14 2.68e- 7 2.08 4.04 location
#> 6 cont… 1.83 0.513 3.58 3.44e- 4 0.992 2.68 location
tidy(fit, conf.int = TRUE, exponentiate = TRUE)
#> # A tibble: 6 × 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 0.197 0.682 -2.38 1.74e- 2 0.0518 0.751 intercept
#> 2 2|3 4.54 0.604 2.51 1.22e- 2 1.39 14.8 intercept
#> 3 3|4 68.6 0.809 5.23 1.72e- 7 14.1 335. intercept
#> 4 4|5 441. 0.972 6.26 3.82e-10 65.5 2965. intercept
#> 5 temp… 21.4 0.595 5.14 2.68e- 7 6.66 68.7 location
#> 6 cont… 6.26 0.513 3.58 3.44e- 4 2.29 17.1 location
glance(fit)
#> # A tibble: 1 × 5
#> edf AIC BIC logLik nobs
#> <dbl> <dbl> <dbl> <logLik> <dbl>
#> 1 7 177. 193. -81.56541 72
# ...and again with another model specification
fit2 <- clmm(rating ~ temp + (1 | judge), nominal = ~contact, data = wine)
#> Warning: unrecognized control elements named ‘nominal’ ignored
tidy(fit2)
#> # A tibble: 5 × 6
#> term estimate std.error statistic p.value coef.type
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1|2 -2.20 0.613 -3.59 0.000333 intercept
#> 2 2|3 0.545 0.476 1.15 0.252 intercept
#> 3 3|4 2.84 0.607 4.68 0.00000291 intercept
#> 4 4|5 4.48 0.751 5.96 0.00000000256 intercept
#> 5 tempwarm 2.67 0.554 4.81 0.00000147 location
glance(fit2)
#> # A tibble: 1 × 5
#> edf AIC BIC logLik nobs
#> <dbl> <dbl> <dbl> <logLik> <dbl>
#> 1 6 189. 203. -88.73882 72