bdCorr_matrix

Compute correlation matrix for in-memory matrices (unified function)

LINEAR_ALGEBRA

1 Description

Compute Pearson or Spearman correlation matrix for matrices that fit in memory. This function automatically detects whether to compute:

2 Usage

bdCorr_matrix(X, Y = NULL, trans_x = NULL, trans_y = NULL, method = NULL, use_complete_obs = NULL, compute_pvalues = NULL, threads = NULL)

3 Arguments

Parameter	Description
`X`	First numeric matrix (observations in rows, variables in columns)
`Y`	Second numeric matrix (optional, observations in rows, variables in columns)
`trans_x`	Logical, whether to transpose matrix X (default: FALSE)
`trans_y`	Logical, whether to transpose matrix Y (default: FALSE, ignored if Y not provided)
`method`	Character string indicating correlation method (“pearson” or “spearman”, default: “pearson”)
`use_complete_obs`	Logical, whether to use only complete observations (default: TRUE)
`compute_pvalues`	Logical, whether to compute p-values for correlations (default: TRUE)
`threads`	Integer, number of threads for parallel computation (optional, default: -1 for auto)

4 Value

A list containing correlation results

5 Examples

\donttest{
set.seed(123)
X <- matrix(rnorm(1000), nrow = 100, ncol = 10)

# Single matrix correlation
res <- bdCorr_matrix(X)

# Transposed (sample-sample correlations)
res_t <- bdCorr_matrix(X, trans_x = TRUE)

# Cross-correlation with a second matrix
Y <- matrix(rnorm(400), nrow = 100, ncol = 4)
res_xy <- bdCorr_matrix(X, Y)
}

---
title: "bdCorr_matrix"
subtitle: "Compute correlation matrix for in-memory matrices (unified function)"
---

<span class="category-badge linear_algebra">LINEAR_ALGEBRA</span>

## Description

Compute Pearson or Spearman correlation matrix for matrices that fit in memory.
This function automatically detects whether to compute:
\itemize{
  \item Single matrix correlation cor(X) - when only matrix X is provided
  \item Cross-correlation cor(X,Y) - when both matrices X and Y are provided
}

## Usage

```r
bdCorr_matrix(X, Y = NULL, trans_x = NULL, trans_y = NULL, method = NULL, use_complete_obs = NULL, compute_pvalues = NULL, threads = NULL)
```
## Arguments

::: {.param-table}
| Parameter | Description |
|-----------|-------------|
| `X` | First numeric matrix (observations in rows, variables in columns) |
| `Y` | Second numeric matrix (optional, observations in rows, variables in columns) |
| `trans_x` | Logical, whether to transpose matrix X (default: FALSE)  |
| `trans_y` | Logical, whether to transpose matrix Y (default: FALSE, ignored if Y not provided) |
| `method` | Character string indicating correlation method ("pearson" or "spearman", default: "pearson") |
| `use_complete_obs` | Logical, whether to use only complete observations (default: TRUE) |
| `compute_pvalues` | Logical, whether to compute p-values for correlations (default: TRUE) |
| `threads` | Integer, number of threads for parallel computation (optional, default: -1 for auto)  |
:::

## Value

::: {.return-value}
A list containing correlation results

:::
## Examples
```{r}
#| eval: false
#| warning: false

\donttest{
set.seed(123)
X <- matrix(rnorm(1000), nrow = 100, ncol = 10)

# Single matrix correlation
res <- bdCorr_matrix(X)

# Transposed (sample-sample correlations)
res_t <- bdCorr_matrix(X, trans_x = TRUE)

# Cross-correlation with a second matrix
Y <- matrix(rnorm(400), nrow = 100, ncol = 4)
res_xy <- bdCorr_matrix(X, Y)
}

```