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 kde
tidy(x, ...)

Arguments

x

A kde object returned from ks::kde().

...

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.

Value

A tibble::tibble with one row for each point in the estimated grid. The result contains one column (named x1, x2, etc) for each dimension, and an estimate column containing the estimated density.

See also

Examples

library(ks)
#> #> Attaching package: ‘ks’
#> The following object is masked from ‘package:lavaan’: #> #> vech
dat <- replicate(2, rnorm(100)) k <- kde(dat) td <- tidy(k) td
#> # A tibble: 45,602 x 4 #> obs variable value estimate #> <int> <chr> <dbl> <dbl> #> 1 1 x1 -5.41 0 #> 2 2 x1 -5.34 0 #> 3 3 x1 -5.28 0 #> 4 4 x1 -5.22 0 #> 5 5 x1 -5.15 0 #> 6 6 x1 -5.09 0 #> 7 7 x1 -5.03 0 #> 8 8 x1 -4.96 0 #> 9 9 x1 -4.90 0 #> 10 10 x1 -4.84 0 #> # ... with 45,592 more rows
library(ggplot2) ggplot(td, aes(x1, x2, fill = estimate)) + geom_tile() + theme_void()
#> Error in FUN(X[[i]], ...): object 'x1' not found
# also works with 3 dimensions dat3 <- replicate(3, rnorm(100)) k3 <- kde(dat3) td3 <- tidy(k3) td3
#> # A tibble: 397,953 x 4 #> obs variable value estimate #> <int> <chr> <dbl> <dbl> #> 1 1 x1 -4.77 0 #> 2 2 x1 -4.59 0 #> 3 3 x1 -4.41 0 #> 4 4 x1 -4.23 0 #> 5 5 x1 -4.05 0 #> 6 6 x1 -3.87 0 #> 7 7 x1 -3.69 0 #> 8 8 x1 -3.51 0 #> 9 9 x1 -3.33 0 #> 10 10 x1 -3.15 0 #> # ... with 397,943 more rows