View source: R/layer_model_predictions.R
layer_model_predictions | R Documentation |
layer_model_predictions
fits a model to the data and draw it with
layer_paths
and, optionally, layer_ribbons
.
layer_smooths
is a special case of layering model predictions where
the model is a smooth loess curve whose smoothness is controlled by the
span
parameter.
layer_model_predictions(
vis,
...,
model,
formula = NULL,
model_args = NULL,
se = FALSE,
domain = NULL
)
layer_smooths(vis, ..., span = 0.75, se = FALSE)
vis |
Visualisation to modify |
... |
Visual properties. Stroke properties control only affect line, fill properties only affect standard error band. |
model |
Name of the model as a string, e.g. |
formula |
Model formula. If not supplied, guessed from the visual
properties, constructing |
model_args |
A list of additional arguments passed on to the
|
se |
Also display a point-wise standard error band? Defaults to
|
domain |
If |
span |
For |
mtcars %>% ggvis(~wt, ~mpg) %>% layer_smooths()
mtcars %>% ggvis(~wt, ~mpg) %>% layer_smooths(se = TRUE)
# Use group by to display multiple smoothes
mtcars %>% ggvis(~wt, ~mpg) %>% group_by(cyl) %>% layer_smooths()
# Control appearance with props
mtcars %>% ggvis(~wt, ~mpg) %>%
layer_smooths(se = TRUE, stroke := "red", fill := "red", strokeWidth := 5)
# Control the wiggliness with span. Default is 0.75
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>%
layer_smooths(span = 0.2)
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>%
layer_smooths(span = 1)
# Map to an input to modify interactively
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>%
layer_smooths(span = input_slider(0.2, 1))
# Use other modelling functions with layer_model_predictions
mtcars %>% ggvis(~wt, ~mpg) %>%
layer_points() %>%
layer_model_predictions(model = "lm") %>%
layer_model_predictions(model = "MASS::rlm", stroke := "red")
# Custom domain for predictions
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>%
layer_model_predictions(model = "lm", domain = c(0, 8))
mtcars %>% ggvis(~wt, ~mpg) %>% layer_points() %>%
layer_model_predictions(model = "lm",
domain = input_slider(0, 10, value = c(1, 4)))
# layer_smooths() is just compute_smooth() + layer_paths()
# Run loess or other model outside of a visualisation to see what variables
# you get.
mtcars %>% compute_smooth(mpg ~ wt)
mtcars %>% compute_model_prediction(mpg ~ wt, model = "lm")
mtcars %>%
ggvis(~wt, ~mpg) %>%
layer_points() %>%
compute_smooth(mpg ~ wt) %>%
layer_paths(~pred_, ~resp_, strokeWidth := 2)
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