Plot jackstraw results

Produces diagnostic plots for a jackstraw_rajive object. Three plot types are supported (selected via type):

Usage

plot_jackstraw(
  jackstraw_result,
  type = c("pvalue_hist", "scatter", "loadings_significance"),
  block = 1L,
  component = 1L,
  label_top = 10L,
  ...
)

Arguments

jackstraw_result: An object of class "jackstraw_rajive".
type: Character string; one of "pvalue_hist", "scatter", or "loadings_significance".
block: Positive integer; which block to plot. Default 1.
component: Positive integer; which joint component to plot (used for "pvalue_hist" and "scatter"). Default 1.
label_top: Non-negative integer; for "scatter", the number of top-significant features to label. Set to 0 to suppress labels. Default 10.
...: Ignored (reserved for future use).

Value

A ggplot2 object.

Details

"pvalue_hist": Histogram of empirical p-values for a single block / component. A horizontal reference line is drawn at the effective significance threshold (Bonferroni-adjusted if applicable). Enrichment of small p-values indicates real signal.
"scatter": Scatter plot of observed F-statistic (x-axis) versus \(-\log_{10}(p\text{-value})\) (y-axis) for a single block / component. Significant features are coloured red; non-significant features are grey. Features are optionally labelled when column names are present and label_top > 0.
"loadings_significance": Heatmap of \(-\log_{10}(p\text{-value})\) across all joint components for a single block. Significant cells are marked with an asterisk.

References

Yang X, Hoadley KA, Hannig J, Marron JS (2021). Statistical inference for data integration. arXiv:2109.12272.

Examples

# \donttest{
set.seed(42)
n   <- 50
pks <- c(100, 80)
Y   <- ajive.data.sim(K = 2, rankJ = 2, rankA = c(5, 4), n = n,
                      pks = pks, dist.type = 1)
data.ajive           <- Y$sim_data
initial_signal_ranks <- c(5, 4)
ajive_result <- Rajive(data.ajive, initial_signal_ranks)
js <- jackstraw_rajive(ajive_result, data.ajive, alpha = 0.05, n_null = 10)
plot_jackstraw(js, type = "pvalue_hist", block = 1, component = 1)

plot_jackstraw(js, type = "scatter",     block = 1, component = 1)

plot_jackstraw(js, type = "loadings_significance", block = 1)

# }