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 kappa
tidy(x, ...)

Arguments

x

A kappa object returned from psych::cohen.kappa().

...

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

Note that confidence level (alpha) for the confidence interval cannot be set in tidy. Instead you must set the alpha argument to psych::cohen.kappa() when creating the kappa object.

See also

Value

A tibble::tibble() with columns:

conf.high

The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.low

The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

estimate

The estimated value of the regression term.

type

Either `weighted` or `unweighted`.

Examples

library(psych)
#> #> Attaching package: ‘psych’
#> The following object is masked from ‘package:gamlss’: #> #> cs
#> The following object is masked from ‘package:ordinal’: #> #> income
#> The following object is masked from ‘package:lavaan’: #> #> cor2cov
#> The following object is masked from ‘package:car’: #> #> logit
#> The following objects are masked from ‘package:ggplot2’: #> #> %+%, alpha
#> The following object is masked from ‘package:mclust’: #> #> sim
rater1 = 1:9 rater2 = c(1, 3, 1, 6, 1, 5, 5, 6, 7) ck <- cohen.kappa(cbind(rater1, rater2)) tidy(ck)
#> # A tibble: 2 x 4 #> type estimate conf.low conf.high #> <chr> <dbl> <dbl> <dbl> #> 1 unweighted 0 -0.185 0.185 #> 2 weighted 0.678 0.430 0.926
# graph the confidence intervals library(ggplot2) ggplot(tidy(ck), aes(estimate, type)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high))