Tidy a(n) kappa objectSource:
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 kappa tidy(x, ...)
kappaobject returned from
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:
Note that confidence level (alpha) for the confidence interval
cannot be set in
tidy. Instead you must set the
psych::cohen.kappa() when creating the
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.
Either `weighted` or `unweighted`.
# load libraries for models and data library(psych) #> #> Attaching package: ‘psych’ #> The following object is masked from ‘package:boot’: #> #> logit #> The following object is masked from ‘package:lavaan’: #> #> cor2cov #> The following object is masked from ‘package:car’: #> #> logit #> The following object is masked from ‘package:drc’: #> #> logistic #> The following objects are masked from ‘package:ggplot2’: #> #> %+%, alpha #> The following object is masked from ‘package:mclust’: #> #> sim # generate example data rater1 <- 1:9 rater2 <- c(1, 3, 1, 6, 1, 5, 5, 6, 7) # fit model ck <- cohen.kappa(cbind(rater1, rater2)) # summarize model fit with tidiers + visualization tidy(ck) #> # A tibble: 2 × 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))