README.md

In han16/GEARS: Gaussian differEntiAl netwoRk analysiS

GEARS

The goal of GEARS is to reconstruct Gaussian Bayesian network and compare two networks (identical or differential) with graph ordering unknown.

Installation

You can install the released version of GEARS from CRAN with:

install.packages("GEARS")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("han16/GEARS")

In case people have installation problem, try this

remove.packages(c("curl","httr"))
install.packages(c("curl", "httr"))
Sys.setenv(CURL_CA_BUNDLE="/usr/lib64/microsoft-r/3.4/lib64/R/lib/microsoft-r-cacert.pem")
devtools::install_git("https://github.com/han16/GEARS")

Example

library(GEARS)
## basic example code
head(exampleData1)
#>    V1  V2  V3  V4  V5 V6  V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19
#> 1 1.5 1.5 0.0 0.0 0.0  0 1.5  2  2 2.5 1.5   0 0.0   2   0 1.5 2.0 0.0   2
#> 2 1.5 1.5 2.0 1.5 2.0  2 1.5  2  2 2.5 0.0   0 0.0   0   0 1.5 0.0 2.0   2
#> 3 1.5 1.5 0.0 1.5 2.0  2 1.5  2  2 2.5 1.5   2 0.0   0   2 0.0 1.5 2.0   2
#> 4 1.5 1.5 0.0 0.0 1.5  2 0.0  0  0 1.5 0.0   0 1.5   2   2 0.0 1.5 0.0   0
#> 5 1.5 0.0 1.5 0.0 1.5  0 1.5  0  2 2.0 0.0   0 1.5   2   2 1.5 0.0 2.0   0
#> 6 1.5 1.5 2.0 1.5 2.0  2 1.5  2  2 2.5 0.0   0 1.5   0   0 0.0 0.0 1.5   0
#>   V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 V37
#> 1   0 0.0 0.0 1.5   2   2 0.0   0   0 1.5 0.0 0.0 2.0   0 2.0 0.0   0   0
#> 2   0 2.5 0.0 0.0   0   0 0.0   0   0 0.0 0.0 0.0 1.5   0 2.0 0.0   0   0
#> 3   0 2.5 0.0 1.5   0   0 0.0   2   0 0.0 0.0 2.0 0.0   0 0.0 2.5   0   0
#> 4   0 2.0 1.5 2.0   0   2 2.5   0   0 0.0 1.5 0.0 2.0   0 0.0 0.0   0   0
#> 5   2 2.5 1.5 0.0   0   2 0.0   0   2 0.0 0.0 1.5 2.0   0 2.0 2.5   0   0
#> 6   2 2.0 0.0 0.0   0   0 0.0   0   0 1.5 0.0 2.0 0.0   2 2.5 0.0   0   0
#>   V38 V39 V40 V41 V42 V43 V44 V45
#> 1   0 0.0 1.5   0 0.0   2   0 2.0
#> 2   0 0.0 1.5   2 2.0   0   0 2.5
#> 3   0 0.0 0.0   0 0.0   0   0 0.0
#> 4   0 1.5 2.0   2 2.5   0   0 0.0
#> 5   0 0.0 0.0   0 0.0   0   0 0.0
#> 6   0 0.0 1.5   0 2.0   0   0 0.0
head(exampleData4)
#>            V1         V2         V3         V4         V5         V6
#> 1  0.80426840  1.9006522  1.0777911 -0.1099156  6.7435888  1.0748047
#> 2  0.12403565  0.5072030 -0.6071371  0.9988323  1.4933037  1.3914729
#> 3 -0.09277538  1.6601093 -1.7514093  1.6036877  1.1269293  3.2696831
#> 4  0.24335230 -1.1334361 -0.2062619  0.6096576  0.8399068 -0.3126217
#> 5  0.02536413 -0.5756416 -1.3068243  0.1720098 -2.7572069  1.3700969
#> 6 -0.89784935 -1.7635564 -1.8967425 -0.8790398 -9.5008889 -2.9371123
#>          V7        V8         V9        V10
#> 1  4.144335  3.354748  2.8457608  13.357390
#> 2  1.487572  2.005783  6.7149070  18.386733
#> 3  6.053086  7.161020 10.9266216  37.144373
#> 4 -2.513313 -1.144628 -0.3666188  -5.634854
#> 5 -1.492838 -3.789733  2.9199732   3.416417
#> 6 -6.030453 -7.717611 -9.3254433 -29.471658

• exampleData1 is the regression coefficient in the network
• exampleData4 is one simulated data set with number of node as the number of the columns, 10.

Reference

Gaussian Bayesian network comparisons with graph ordering unknown

han16/GEARS documentation built on Jan. 8, 2022, 12:10 a.m.