Plot Magma or GP predictions — plot

Display Magma or classic GP predictions. According to the dimension of the inputs, the graph may be a mean curve + Credible Interval or a heatmap of probabilities.

Usage

plot_gp(
  pred_gp,
  x_input = NULL,
  data = NULL,
  data_train = NULL,
  prior_mean = NULL,
  y_grid = NULL,
  heatmap = FALSE,
  samples = FALSE,
  nb_samples = 50,
  plot_mean = TRUE,
  alpha_samples = 0.3,
  prob_CI = 0.95,
  size_data = 3,
  size_data_train = 1,
  alpha_data_train = 0.5
)

plot_magma(
  pred_gp,
  x_input = NULL,
  data = NULL,
  data_train = NULL,
  prior_mean = NULL,
  y_grid = NULL,
  heatmap = FALSE,
  samples = FALSE,
  nb_samples = 50,
  plot_mean = TRUE,
  alpha_samples = 0.3,
  prob_CI = 0.95,
  size_data = 3,
  size_data_train = 1,
  alpha_data_train = 0.5
)

Arguments

pred_gp: A tibble or data frame, typically coming from pred_magma or pred_gp functions. Required columns: 'Input', 'Mean', 'Var'. Additional covariate columns may be present in case of multi-dimensional inputs.
x_input: A vector of character strings, indicating which input should be displayed. If NULL (default) the 'Input' column is used for the x-axis. If providing a 2-dimensional vector, the corresponding columns are used for the x-axis and y-axis.
data: (Optional) A tibble or data frame. Required columns: 'Input', 'Output'. Additional columns for covariates can be specified. This argument corresponds to the raw data on which the prediction has been performed.
data_train: (Optional) A tibble or data frame, containing the training data of the Magma model. The data set should have the same format as the data argument with an additional required column 'ID' for identifying the different individuals/tasks. If provided, those data are displayed as backward colourful points (each colour corresponding to one individual/task).
prior_mean: (Optional) A tibble or a data frame, containing the 'Input' and associated 'Output' prior mean parameter of the GP prediction.
y_grid: A vector, indicating the grid of values on the y-axis for which probabilities should be computed for heatmaps of 1-dimensional predictions. If NULL (default), a vector of length 50 is defined, ranging between the min and max 'Output' values contained in pred_gp.
heatmap: A logical value indicating whether the GP prediction should be represented as a heatmap of probabilities for 1-dimensional inputs. If FALSE (default), the mean curve and associated Credible Interval are displayed.
samples: A logical value indicating whether the GP prediction should be represented as a collection of samples drawn from the posterior. If FALSE (default), the mean curve and associated Credible Interval are displayed.
nb_samples: A number, indicating the number of samples to be drawn from the predictive posterior distribution. For two-dimensional graphs, only one sample can be displayed.
plot_mean: A logical value, indicating whether the mean prediction should be displayed on the graph when samples = TRUE.
alpha_samples: A number, controlling transparency of the sample curves.
prob_CI: A number between 0 and 1 (default is 0.95), indicating the level of the Credible Interval associated with the posterior mean curve. If this this argument is set to 1, the Credible Interval is not displayed.
size_data: A number, controlling the size of the data points.
size_data_train: A number, controlling the size of the data_train points.
alpha_data_train: A number, between 0 and 1, controlling transparency of the data_train points.

Value

Visualisation of a Magma or GP prediction (optional: display data points, training data points and the prior mean function). For 1-D inputs, the prediction is represented as a mean curve and its associated 95% Credible Interval, as a collection of samples drawn from the posterior if samples = TRUE, or as a heatmap of probabilities if heatmap = TRUE. For 2-D inputs, the prediction is represented as a heatmap, where each couple of inputs on the x-axis and y-axis are associated with a gradient of colours for the posterior mean values, whereas the uncertainty is indicated by the transparency (the narrower is the Credible Interval, the more opaque is the associated colour, and vice versa)

Examples

TRUE
#> [1] TRUE