ggvis (version 0.4.1)

compute_model_prediction: 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

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.

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)

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.

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_)