Display MagmaClust predictions. According to the dimension of the inputs, the graph may be a mean curve (dim inputs = 1) or a heatmap (dim inputs = 2) of probabilities. Moreover, MagmaClust can provide credible intervals only by visualising cluster-specific predictions (e.g. for the most probable cluster). When visualising the full mixture-of-GPs prediction, which can be multimodal, the user should choose between the simple mean function or the full heatmap of probabilities (more informative but slower).

Usage

plot_magmaclust(
pred_clust,
cluster = "all",
x_input = NULL,
data = NULL,
data_train = NULL,
col_clust = FALSE,
prior_mean = NULL,
y_grid = NULL,
heatmap = FALSE,
prob_CI = 0.95,
size_data = 3,
size_data_train = 1,
alpha_data_train = 0.5
)

Arguments

pred_clust

A list of predictions, typically coming from pred_magmaclust. Required elements: pred, mixture, mixture_pred.

cluster

A character string, indicating which cluster to plot from. If 'all' (default) the mixture of GPs prediction is displayed as a mean curve (1-D inputs) or a mean heatmap (2-D inputs). Alternatively, if the name of one cluster is provided, the classic mean curve + credible interval is displayed (1-D inputs), or a heatmap with colour gradient for the mean and transparency gradient for the Credible Interval (2-D 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 MagmaClust 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 or a cluster, see col_clust below).

col_clust

A boolean indicating whether backward points are coloured according to the individuals or to their most probable cluster. If one wants to colour by clusters, a column Cluster shall be present in data_train. We advise to use data_allocate_cluster for automatically creating a well-formatted dataset from a trained MagmaClust model.

prior_mean

(Optional) A list providing, for each cluster, a tibble containing prior mean parameters of the prediction. This argument typically comes as an outcome hyperpost\$mean, available through the train_magmaclust, pred_magmaclust functions.

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.

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 if available) are displayed.

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 MagmaClust prediction (optional: display data points, training data points and the prior mean functions). For 1-D inputs, the prediction is represented as a mean curve (and its associated 95% Credible Interval for cluster-specific predictions), or as a heatmap of probabilities if heatmap = TRUE. In the case of MagmaClust, the heatmap representation should be preferred for clarity, although the default display remains mean curve for quicker execution. 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). As for 1-D inputs, Credible Interval information is only available for cluster-specific predictions.

Examples

TRUE
#> [1] TRUE