Mixed Correlation Matrix

Combine Pearson r values, correlation ellipses, variable labels, and significance markers in one matrix. Mixed correlation is a Plus feature; standard Pearson correlation heatmaps remain Free.

Prepare the data

Start with a wide table: each column is a numeric variable and each row is the same sample across variables. Do not submit a precomputed correlation matrix as raw observations.

This practice table illustrates structure only and is not evidence for a statistical conclusion. Use sample IDs to align rows; do not select them as correlation variables.

Practice data · not the screenshot source
Sample ID	Metric A	Metric B	Metric C
S1	10	18	7
S2	12	24	6
S3	15	27	9
S4	18	35	5
S5	20	34	4
S6	23	42	8

Create a mixed correlation matrix

Confirm paired observations before choosing variables, groups, and display settings.

Step 1

Calculation and output

Set Pearson r, missing-value handling, P-value correction, and the significance threshold; then confirm matrix and heatmap output.

New Mixed Correlation MatrixInterface preview · non-interactive

Plus

Calculation

Correlation

Pearson r

Missing values

Pairwise exclusion

P-value correction

None

Significance threshold

0.05

Output

Correlation matrix and heatmap

Matrix data table only

Matrix composition

Lower trianglePearson rUpper triangleCorrelation ellipsesDiagonalVariable labelsSignificanceP ≤ 0.05

Step 2

Select multiple columns

Choose Multiple Columns and map at least two numeric variables; each row must represent the same sample.

New Mixed Correlation MatrixInterface preview · non-interactive

Multiple columnsXYZ curve groups

Numeric columns

Metric A, Metric B, Metric C

3 columns selected

Step 3

Preview and groups

Optionally assign shared group labels, then review independent tests, unavailable results, and valid sample size before creating.

New Mixed Correlation MatrixInterface preview · non-interactive

Single-level groups (optional)

Enter the same group name for variables that belong together; leave blank for no group.

Metric AGroup nameMetric BGroup nameMetric CGroup name

0/3 variables grouped

Variables3

Independent tests3

Unable to calculate0

Valid samples5

Step 4

XYZ curve groups

For an XYZ long table, switch to XYZ Curve Groups, map X, Y, and value columns, then choose the profile direction.

New Mixed Correlation MatrixInterface preview · non-interactive

Build curves from an XYZ long table along X or Y, then calculate Pearson r between profiles.

Multiple columnsXYZ curve groups

X coordinate

age_days

Y coordinate

circumference_mm

Value column

circumference_mm

Compare profiles

Compare slices by X position

Slices7

Independent tests21

Unable to calculate0

Valid samples35

At least two slices are required, with two or more valid values in each pair.

Interpretation and pre-export checks

Correlation does not imply causation. Check outliers, nonlinearity, and sample size before interpreting Pearson r.
A cross means the correlation is unavailable, not zero or non-significant. Check constant columns and insufficient valid pairs.
Significance markers require available p values. Report the threshold; stars are not a measure of effect size.
Variable groups are display labels, not sample grouping or automatic clustering.
XYZ Curve Groups is a separate input mode: map X, Y, and value columns and choose profiles by X or Y. Do not confuse this with row-wise sample pairing in a wide table.

Read the 2D color map tutorial →

Adjustable settings

These are the common settings for this plot type. For detailed line, marker, axis, legend, and font styling, see Styling.

Matrix Layout: lower values / upper ellipses / diagonal labels
Ellipse size, opacity, and outline
Cell grid
Significance markers and threshold
One-level row and column groups
Palette and Pearson r colorbar

Styling →