compute_model_prediction: Create a model of a data set and compute predictions.

In ggvis: Interactive Grammar of Graphics

Create a model of a data set and compute predictions.

Description

Fit a 1d model, then compute predictions and (optionally) standard errors over an evenly spaced grid.

Usage

compute_model_prediction(
  x,
  formula,
  ...,
  model = NULL,
  se = FALSE,
  level = 0.95,
  n = 80L,
  domain = NULL,
  method
)

compute_smooth(x, formula, ..., span = 0.75, se = FALSE)

Arguments

x	Dataset-like object to model and predict. Built-in methods for data frames, grouped data frames and ggvis visualisations.
formula	Formula passed to modelling function. Can use any variables from data.
...	arguments passed on to model function
model	Model fitting function to use - it must support R's standard modelling interface, taking a formula and data frame as input, and returning predictions with predict. If not supplied, will use loess for <= 1000 points, otherwise it will use gam. Other modelling functions that will work include lm, glm and rlm.
se	include standard errors in output? Requires appropriate method of predict_grid, since the interface for returning predictions with standard errors is not consistent acrossing modelling frameworks.
level	the confidence level of the standard errors.
n	the number of grid points to use in the prediction
domain	If NULL (the default), the domain of the predicted values will be the same as the domain of the prediction variable in the data. It can also be a two-element numeric vector specifying the min and max.
method	Deprecated. Please use model instead.
span	Smoothing span used for loess model.

Details

compute_model_prediction fits a model to the data and makes predictions with it. compute_smooth is a special case of model predictions where the model is a smooth loess curve whose smoothness is controlled by the span parameter.

Value

A data frame with columns:

resp_	regularly spaced grid of n locations
pred_	predicted value from model
pred_lwr_ and pred_upr_	upper and lower bounds of confidence interval (if se = TRUE)
pred_se_	the standard error (width of the confidence interval) (if se = TRUE)

Examples

# Use a small value of n for these examples
mtcars %>% compute_model_prediction(mpg ~ wt, n = 10)
mtcars %>% compute_model_prediction(mpg ~ wt, n = 10, se = TRUE)
mtcars %>% group_by(cyl) %>% compute_model_prediction(mpg ~ wt, n = 10)

# compute_smooth defaults to loess
mtcars %>% compute_smooth(mpg ~ wt)

# Override model to suppress message or change approach
mtcars %>% compute_model_prediction(mpg ~ wt, n = 10, model = "loess")
mtcars %>% compute_model_prediction(mpg ~ wt, n = 10, model = "lm")

# Set the domain manually
mtcars %>%
  compute_model_prediction(mpg ~ wt, n = 20, model = "lm", domain = c(0, 8))

# Plot the results
mtcars %>% compute_model_prediction(mpg ~ wt) %>%
  ggvis(~pred_, ~resp_) %>%
  layer_paths()
mtcars %>% ggvis() %>%
  compute_model_prediction(mpg ~ wt) %>%
  layer_paths(~pred_, ~resp_)

ggvis documentation built on May 29, 2024, 1:12 a.m.