For each algorithm, visualises a selected score over the design grid of
simulated \(\boldsymbol{p}\) (and related scenario factors).
Usage
plot_correlation_Heatmap(
distribution_metrics,
score_variable = "model_mse",
n_break = 20,
uni_scale = TRUE
)
Arguments
- distribution_metrics
Tibble of metric scores from a benchmark.
- score_variable
Column name of the metric to display.
- n_break
Number of colour breaks.
- uni_scale
If FALSE, each panel uses its own colour scale.
Value
A named list of ComplexHeatmap heatmap objects (one per algorithm).
Examples
metrics <- tibble::tibble(
correlation_celltype1 = c(0, 0, 0.5, 0.5),
correlation_celltype2 = c(0, 0.5, 0, 0.5),
algorithm = "nnls",
model_mse = c(0.01, 0.02, 0.015, 0.03)
)
ht <- plot_correlation_Heatmap(metrics, score_variable = "model_mse")
names(ht)
#> [1] "nnls"
