A realisation of a posterior GP distribution is drawn and displayed. According to the dimension of the inputs, the graph may be a curve or a heatmap.

## Usage

```
sample_gp(
pred_gp,
x_input = NULL,
data = NULL,
data_train = NULL,
prior_mean = NULL,
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, containing the data used in the GP prediction.

- 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 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.

- 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.