plotcorrelationmatrix

May 7, 2026 · View on GitHub

plot_correlation_matrix computes and renders the pairwise correlation matrix among columns of a data matrix. Out of the box it does Pearson correlations of an n × k matrix, draws a circle plot (where each circle's diameter encodes magnitude and its color encodes sign), and overlays the numeric correlation as text in each cell. Optionally it switches to Spearman / robust regression / partial correlations, applies FDR correction across the pairwise tests, swaps the circles for a flat heatmap, sorts the variables by partition labels or by a clustering dendrogram, and overlays partition color bars along the edges.

The function is also useful as a thin display layer for any correlation matrix you've already computed: pass 'input_is_r' and a precomputed R matrix (or a ttest3d-style struct with .r, .p, .sig) and it will skip recomputation and just draw. This is the recommended way to plot output from canlab_compute_similarity_matrix, canlab_sort_distance_matrix, and other CANlab pairwise routines.

It returns an OUT struct with the correlation values, p-values, significance mask, FDR-corrected mask, reordered names, and the figure handle, so the same call can drive both the figure and downstream reporting code.

Quick example

rng(7);
S = toeplitz([1 .6 .3 .1 0 0]);
X = mvnrnd([0 0 0 0 0 0], S, 50);
var_names = {'A' 'B' 'C' 'D' 'E' 'F'};
OUT = plot_correlation_matrix(X, 'var_names', var_names);

plot_correlation_matrix sample

Usage

OUT = plot_correlation_matrix(X, [optional inputs])

% Pre-computed matrix (skip stats):
OUT = plot_correlation_matrix(R,    'input_is_r', true, 'names', n);
OUT = plot_correlation_matrix(stat, 'input_is_r', true);   % stat has .r .p .sig

X may be:

A numeric n × k data matrix — correlations are computed.
A MATLAB table — variable names are pulled out automatically.
A ttest3d-style struct with .r, .p, .sig (k × k) — used as-is.
A pre-computed R matrix when paired with 'input_is_r', true.

How it works

Input handling. Tables are converted with table2array, keeping Properties.VariableNames as labels. A struct input with .r .p .sig triggers skip_calculation. With 'input_is_r' the input matrix is treated as a correlation matrix and stats are not computed (so significance markers will not be drawn unless you supply your own).
Correlation method. Default is Pearson. 'dospearman' uses Spearman rank correlations. 'dopartial' uses partialcorr (controls each pair for all other columns). The methods can be combined.
Significance. Pairwise p-values are obtained from the chosen method. With 'dofdr', p-values are FDR-corrected across all k*(k-1)/2 unique pairs and the corrected mask used for star overlays / cell highlighting. With 'p_thr' you can change the uncorrected threshold (default .05).
Display style. Two render modes:
- Circles (default for k ≤ 50): each cell is a filled circle whose radius scales with |r| and whose color is sampled from a blue–white–red colormap by the signed r. Numeric r is overlaid as text (suppressed automatically for k > 15 or with 'notext').
- Image (default for k > 50, or via 'doimage'): a flat imagesc heatmap. Combine with 'docircles', false to suppress the circle layer entirely.
Reordering. With 'partitions' (a k-length integer vector defining variable groups), columns are sorted ascending by partition ID and color bars are drawn along the matrix edges using 'partitioncolors' and 'partitionlabels'. With 'reorder_by_clustering', the function calls a hierarchical dendrogram routine to reorder variables so that highly-correlated ones are adjacent. Either reordering is reflected in the returned var_names.
Color limits and sizing. 'colorlimit' (default [-1 1]) sets the colormap range; 'max_radius' controls the maximum circle size (defaults to range(colorlimit)/4 or 0.5). Text size, color, and offsets are controllable via the text_* options.

Inputs

Argument	Type	Description
`X`	matrix / table / struct / R-matrix	Data matrix (`n × k`), a `table`, a `ttest3d`-style struct (`.r .p .sig`), or a precomputed correlation matrix with `'input_is_r'`.

Optional inputs

Stats

Argument	Type	Description
`'p_thr'`	scalar	Uncorrected p-value threshold for significance markers. Default `.05`.
`'dospearman'` / `'spearman'` / `'rank'` / `'dorank'`	flag	Use Spearman rank correlations.
`'dopartial'` / `'partial'` / `'partialcorr'`	flag	Use partial correlations (controls for other columns).
`'dofdr'` / `'fdr'` / `'FDR'`	flag	FDR-correct across all pairs.
`'input_is_r'` / `'input_rmatrix'`	flag	Skip computation; treat `X` as a correlation matrix.

Display

Argument	Type	Description
`'image'`	flag	Heatmap mode (`doimage = true`, `docircles = false`).
`'circles'`	flag	Circle mode (default for small matrices).
`'doimage'`	logical	Same as `'image'` flag, name/value form.
`'docircles'`	logical	Toggle circles.
`'dotext'`	logical	Overlay numeric `r` in each cell. Default true for k ≤ 15.
`'notext'`	flag	Suppress numeric overlays.
`'colorlimit'`	2-vector	Colormap range (default `[-1 1]`).
`'max_radius'`	scalar	Max circle radius (default `range(colorlimit)/4` or 0.5).
`'text_x_offset'`	scalar	Horizontal offset for text labels (default `.15`).
`'text_y_offset'`	scalar	Vertical offset for text labels (default `0`).
`'text_fsize'`	scalar	Font size for in-cell text. Default 16.
`'text_nonsig_color'`	RGB	Text color for non-significant cells. Default `[.3 .3 .3]`.
`'text_sig_color'`	RGB	Text color for significant cells. Default `[0 0 0]`.
`'nofigure'`	flag	Don't open a new figure (use the current axes).
`'dofigure'`	flag	Open a new figure (default).

Labels and grouping

Argument	Type	Description
`'var_names'` / `'names'` / `'labels'`	cell of strings	Column labels (auto from `table` input).
`'partitions'`	k-length integer vector	Group labels — columns will be sorted ascending and partition bars drawn.
`'partitioncolors'`	cell of strings or RGBs	One color per partition.
`'partitionlabels'`	cell of strings	One label per partition.
`'reorder_by_clustering'`	flag	Reorder columns by hierarchical clustering of `R` so similar variables are adjacent.

Outputs

Output	Type	Description
`OUT.r`	k × k matrix	Correlation values.
`OUT.p`	k × k matrix	p-values for each pair.
`OUT.sig`	k × k logical	Significant pairs (uncorrected, at `p_thr`).
`OUT.fdrsig`	k × k logical	FDR-significant pairs (when `'dofdr'`).
`OUT.fdrthr`	scalar	FDR threshold (when `'dofdr'`).
`OUT.var_names`	cell	Column labels (re-ordered if a partition or clustering reorder happened).
`OUT.reorder_note`	char	Present when reordering occurred; describes the reorder.
`OUT.han`	handles	Figure / axis / circle / text / colorbar handles.

Notes

For matrices with k > 15, text overlays are turned off automatically. For k > 50, circles are switched off in favour of a heatmap. Override these by passing the relevant flags explicitly.
'partitions' requires the partition labels to be sortable in a way that corresponds to a meaningful grouping; the function sorts them ascending and warns when reordering happens. Re-sort your data matrix yourself first if you need a custom partition order.
With 'input_is_r', no significance is computed and so no stars appear by default. If you want significance overlays, pre-compute them and pass a ttest3d-style struct.
'dopartial' uses partialcorr, which requires the Statistics Toolbox and complete cases. NaNs need to be handled upstream.
The default colormap is blue-white-red and can be tuned implicitly by 'colorlimit' (asymmetric ranges shift the white point).

Examples

% Larger matrix without circles
OUT = plot_correlation_matrix(X, 'doimage', true, 'docircles', false);

% Spearman partial correlations with FDR correction
OUT = plot_correlation_matrix(X, 'doimage', true, 'docircles', false, ...
                              'dospearman', true, ...
                              'dopartial', true, 'dofdr', true);

% Plot a precomputed similarity matrix from canlab_compute_similarity_matrix
obj = load_image_set('hansen22');
[R, ~] = canlab_compute_similarity_matrix(obj.dat, 'doplot', false);
plot_correlation_matrix(R, 'input_is_r', true, ...
                        'names', obj.metadata_table.target);

% Sort by clustering and re-plot
[R_sorted, perm_order] = canlab_sort_distance_matrix(R, ...
                            'correlation_matrix', true);
plot_correlation_matrix(R_sorted, 'input_is_r', true, ...
                        'names', obj.metadata_table.target(perm_order));

% Use auto-reordering
plot_correlation_matrix(R, 'input_is_r', true, ...
                        'names', obj.metadata_table.target, ...
                        'reorder_by_clustering');

% Color-bar partitions for grouped variables
partition  = [1 1 1 2 2 3 3 3];
pcolors    = {'r', 'g', 'b'};
plabels    = {'Sensory', 'Affect', 'Cognitive'};
plot_correlation_matrix(X, 'partitions', partition, ...
                        'partitioncolors', pcolors, ...
                        'partitionlabels', plabels);