https://github.com/irinagain/mixedCCA

R package for sparse canonical correlation analysis with mixed data types (continuous, binary, zero-truncated)
https://github.com/irinagain/mixedCCA

Last synced: about 8 hours ago
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R package for sparse canonical correlation analysis with mixed data types (continuous, binary, zero-truncated)

• Host: GitHub
• URL: https://github.com/irinagain/mixedCCA
• Owner: irinagain
• Created: 2018-07-11T15:19:15.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-09-09T21:19:37.000Z (about 2 years ago)
• Last Synced: 2024-08-02T20:43:39.305Z (3 months ago)
• Language: R
• Homepage:
• Size: 9.71 MB
• Stars: 18
• Watchers: 5
• Forks: 8
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

• awesome-multi-omics - mixedCCA - Yoon - sparse CCA for data of mixed types - [paper](https://arxiv.org/abs/1807.05274) (Software packages and methods / Multi-omics correlation or factor analysis)

README

        
---

output:

  md_document:

    variant: markdown_github

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

  [![R-CMD-check](https://github.com/irinagain/mixedCCA/workflows/R-CMD-check/badge.svg)](https://github.com/irinagain/mixedCCA/actions)

# mixedCCA: sparse CCA for data of mixed types

The R package `mixedCCA` implements sparse canonical correlation analysis for data of mixed types: continuous, binary or zero-inflated (truncated continuous). The corresponding reference is

[Yoon G., Carroll R.J. and Gaynanova I. (2020). "Sparse semiparametric canonical correlation analysis for data of mixed types". *Biometrika*](https://doi.org/10.1093/biomet/asaa007).

The faster version of latent correlation computation part is now fully available and implemented to the R package `mixedCCA`. The corresponding reference is available on arXiv: 

[Yoon G., Müller C.L. and Gaynanova I., "Fast computation of latent correlations" *JCGS*](https://doi.org/10.1080/10618600.2021.1882468).

## Installation

```{install}

devtools::install_github("irinagain/mixedCCA")

```

## Example

```{r example, results="hide", message=FALSE}

library(mixedCCA)

### Simple example

# Data setting

n <- 100; p1 <- 15; p2 <- 10 # sample size and dimensions for two datasets.

maxcancor <- 0.9 # true canonical correlation

# Correlation structure within each data set

set.seed(0)

perm1 <- sample(1:p1, size = p1);

Sigma1 <- autocor(p1, 0.7)[perm1, perm1]

blockind <- sample(1:3, size = p2, replace = TRUE);

Sigma2 <- blockcor(blockind, 0.7)

mu <- rbinom(p1+p2, 1, 0.5)

# true variable indices for each dataset

trueidx1 <- c(rep(1, 3), rep(0, p1-3))

trueidx2 <- c(rep(1, 2), rep(0, p2-2))

# Data generation

simdata <- GenerateData(n=n, trueidx1 = trueidx1, trueidx2 = trueidx2, maxcancor = maxcancor,

                        Sigma1 = Sigma1, Sigma2 = Sigma2,

                        copula1 = "exp", copula2 = "cube",

                        muZ = mu,

                        type1 = "trunc", type2 = "trunc",

                        c1 = rep(1, p1), c2 =  rep(0, p2)

)

X1 <- simdata$X1

X2 <- simdata$X2

# Sparse semiparametric CCA with BIC1 criterion

mixedCCAresult <- mixedCCA(X1, X2, type1 = "trunc", type2 = "trunc", BICtype = 1)

mixedCCAresult$KendallR # estimated latent correlation matrix

mixedCCAresult$w1 # estimated canonical vector for X1

mixedCCAresult$w2 # estimated canonical vector for X2

mixedCCAresult$cancor # estimated canonical correlation

# Separate estimation of latent correlation matrix

estimateR(X1, type = "trunc")$R # For X1 only

estimateR_mixed(X1, X2, type1 = "trunc", type2 = "trunc")$R12 # For X = (X1, X2)

```