Tidy a(n) clmm object

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 class 'clmm'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

x

A clmm object returned from ordinal::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 pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

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 tempwarm       3.06     0.595      5.14 2.68e- 7    2.08      4.04  location 
#> 6 contactyes     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 tempwarm     21.4       0.595      5.14 2.68e- 7   6.66      68.7   location 
#> 6 contactyes    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