# fboehm/limmbo2

R wrapper to python module limmbo for covariance matrix estimation in multivariate linear mixed models in genetics studies
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# limmbo2

The goal of `limmbo2` is to estimate Vg and Ve covariance matrices in the multivariate LMM. Note that it uses the python module `limmbo` to do this. The preprint article that describes the `limmbo` python module's methods is here: https://www.biorxiv.org/content/early/2018/01/30/255497

## Installation

First, be sure that you have python modules `limix` and `limmbo` installed:

```conda install -c conda-forge limix
pip install limmbo```

Once those two modules are successfully installed, you can proceed to install the limmbo2 R package:

```# install.packages("devtools")
devtools::install_github("fboehm/limmbo2")```

## Example

This is a basic example which shows you how to solve a common problem:

```library(limmbo2)
pheno <- matrix(data = runif(300), nrow = 100, ncol = 3)
kinship <- diag(100)
t(chol(kinship)) -> chol_kin

prep_data(pheno, kinship) -> input_data

make_limmbo(input_data, TRUE, 10, 2) -> l_out
bs_covar_est(l_out, 1, 1) -> bs_out
bs_out2 <- lapply(FUN = convert_for_bscombine, X = bs_out)

combine_bs(bs_out2, l_out) -> fits
(fits\$Cn_fit -> Ve)
#>           [,1]      [,2]      [,3]
#> [1,] 0.1696938 0.1342838 0.1126630
#> [2,] 0.1342838 0.1813464 0.1230871
#> [3,] 0.1126630 0.1230871 0.1421201
(fits\$Cg_fit -> Vg)
#>           [,1]      [,2]      [,3]
#> [1,] 0.1696938 0.1342838 0.1126630
#> [2,] 0.1342838 0.1813464 0.1230871
#> [3,] 0.1126630 0.1230871 0.1421201```
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