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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: 28 days 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 (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-09-09T21:19:37.000Z (almost 2 years ago)
- Last Synced: 2024-03-16T02:19:38.835Z (4 months ago)
- Language: R
- Homepage:
- Size: 9.71 MB
- Stars: 18
- Watchers: 5
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Lists
- 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$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
[](https://github.com/irinagain/mixedCCA/actions)
# mixedCCA: sparse CCA for data of mixed types
The R package `mixedCCA` implements sparse canonical correlation analysis for data of mixed types: continuous, binary or zero-inflated (truncated continuous). The corresponding reference is
[Yoon G., Carroll R.J. and Gaynanova I. (2020). "Sparse semiparametric canonical correlation analysis for data of mixed types". *Biometrika*](https://doi.org/10.1093/biomet/asaa007).
The faster version of latent correlation computation part is now fully available and implemented to the R package `mixedCCA`. The corresponding reference is available on arXiv:
[Yoon G., Müller C.L. and Gaynanova I., "Fast computation of latent correlations" *JCGS*](https://doi.org/10.1080/10618600.2021.1882468).
## Installation
```{install}
devtools::install_github("irinagain/mixedCCA")
```
## Example
```{r example, results="hide", message=FALSE}
library(mixedCCA)
### Simple example
# Data setting
n <- 100; p1 <- 15; p2 <- 10 # sample size and dimensions for two datasets.
maxcancor <- 0.9 # true canonical correlation
# Correlation structure within each data set
set.seed(0)
perm1 <- sample(1:p1, size = p1);
Sigma1 <- autocor(p1, 0.7)[perm1, perm1]
blockind <- sample(1:3, size = p2, replace = TRUE);
Sigma2 <- blockcor(blockind, 0.7)
mu <- rbinom(p1+p2, 1, 0.5)
# true variable indices for each dataset
trueidx1 <- c(rep(1, 3), rep(0, p1-3))
trueidx2 <- c(rep(1, 2), rep(0, p2-2))
# Data generation
simdata <- GenerateData(n=n, trueidx1 = trueidx1, trueidx2 = trueidx2, maxcancor = maxcancor,
Sigma1 = Sigma1, Sigma2 = Sigma2,
copula1 = "exp", copula2 = "cube",
muZ = mu,
type1 = "trunc", type2 = "trunc",
c1 = rep(1, p1), c2 = rep(0, p2)
)
X1 <- simdata$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)
```