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, ...)
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.
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.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("ks", quietly = TRUE)) {
# load libraries for models and data
library(ks)
# generate data
dat <- replicate(2, rnorm(100))
k <- kde(dat)
# summarize model fit with tidiers + visualization
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