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.

Details

Returns a data frame in long format with four columns. Use tidyr::spread(..., variable, value) on the output to return to a wide format.

See also

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

obs

weighted observed number of events in each group

value

TODO. needs better documentation.

variable

Variable under consideration.

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) library(dplyr) library(tidyr) td %>% spread(variable, value) %>% ggplot(aes(x1, x2, fill = estimate)) + geom_tile() + theme_void()
# 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