Input and Result Rules

This topic supplements the SJS.TABLE function by explaining how the output array dimensions are determined and how input values are mapped to each simulation position.

It focuses specifically on:

How the size of the spilled result is calculated
How many simulations are performed
Which input values are selected for each simulation
How mismatched input dimensions are handled

These rules apply when working with non-standard result_reference shapes or when combining multiple inputs of different sizes.

Dimension Overview

The following table summarizes how the shape of result_reference affects the result size and simulation behavior.

result_reference Shape	Output Size	Simulation Count	Input Usage
Multi-cell range	Same as `result_reference`	1	First value only
Single column	Preserve row count; columns = max input columns	Per column	First row of each input
Single row	Preserve column count; rows = max input rows	Per row	First column of each input
Single cell	Max rows × max columns across inputs	Per output cell	Full positional mapping

Examples by Shape

Base Model (From Complete Example)

The Predict Table uses:

=SJS.TABLE(B7, C7:J7, B2, B8:B41, B1)

Where:

B7 → Net Income (single-cell result_reference)
C7:J7 → Discount values (1 × 8)
B8:B41 → Price values (34 × 1)

This model is used to explain the dimension rules below.

All examples use:

Result formula: =B1*10 + C1

1. Single Cell (Complete Example Case)

Formula:

=SJS.TABLE(B7, C7:J7, B2, B8:B41, B1)

Shapes:

B7 → 1 × 1
C7:J7 → 1 × 8
B8:B41 → 34 × 1

Result size:

34 rows × 8 columns

Mapping:

Each row corresponds to one Price value.
Each column corresponds to one Discount value.
Each cell recalculates Net Income using:
- Price = current row value
- Discount = current column value

This demonstrates full positional expansion.

2. Single Column result_reference (Variation of Base Model)

Assume Net Income is referenced as a vertical range:

=SJS.TABLE(B8:B41, C7:J7, B2)

Behavior:

Row count (34) is preserved.
Columns expand to match the 8 Discount values.
One simulation per Discount value.

Each column represents recalculation under one Discount rate.

3. Single Row result_reference (Variation)

Assume a horizontal summary row:

=SJS.TABLE(C7:J7, B8:B41, B1)

Behavior:

Column count (8) is preserved.
Rows expand to match 34 Price values.
One simulation per Price value.

Each row represents recalculation under one Price.

4. Multi-Cell result_reference

If a 2 × 2 result block is used:

=SJS.TABLE(B7:C8, C7:J7, B2)

Behavior:

Output size remains 2 × 2.
Only the first Discount value is used.
Additional input values are ignored.
Only one simulation occurs.

This case does not produce dimensional expansion.

Matching Rules

Single-value inputs apply to all simulations.
Single-row inputs vary by column.
Single-column inputs vary by row.
Multi-row and multi-column inputs vary by both row and column.
Out-of-range positions are treated as empty, and extra input values beyond the required dimensions are ignored.