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 kde tidy(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Returns a data frame in long format with four columns. Use
tidyr::pivot_wider(..., names_from = variable, values_from = value)
on the output to return to a wide format.
A tibble::tibble()
with columns:
The estimated value of the regression term.
weighted observed number of events in each group.
The value/estimate of the component. Results from data reshaping.
Variable under consideration.
if (requireNamespace("ks", quietly = TRUE)) { library(ks) dat <- replicate(2, rnorm(100)) k <- kde(dat) td <- tidy(k) td library(ggplot2) library(dplyr) library(tidyr) td %>% pivot_wider(c(obs, estimate), names_from = variable, values_from = 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 × 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