Help me do multivariate analysis with limma package

[CHAND]

Hi, community-
I have a file that contains the alpha diversity (Shannon) of each sample and a metadata file containing the variables (type, age, sex) of each sample. Something like the following:

|ID|Shannon|
|---|---|
|SRP090252|4.24273563892169|
|SRP090268|4.29833522242158|
|SRP090265|2.86250343813669|
|SRP090264|4.55164366562136|
|SRP090263|4.56860346053261|
|SRP090258|4.30538171901526|
|SRP090250|4.36928441493858|
|SRP090244|4.33813656831468|
|SRP090242|4.3148384711093|
|SRP090227|2.87091123652234|
|SRP090226|4.309189113755|
|SRP090224|4.27878493572071|
|SRP090222|2.55721100375525|
|SRP090217|4.09707415677326|
|SRP090216|4.20037687821853|
|SRP090215|2.89783563238177|
|SRP090209|4.18374127891958|
|SRP090205|4.53502658816734|
|SRP090193|4.41981079746818|
|SRP090179|4.26712306520757|
|SRP090177|4.00064426565985|
|SRP090175|4.01342859382592|
|SRP090173|4.09434828714534|
|SRP090171|4.43371122954567|
|SRP090170|4.00937762290449|
|SRP090169|2.8948324995059|
|SRP090167|2.87228785952243|
|SRP090163|4.14273701286906|
|SRP090161|4.15476670956544|
|SRP090157|4.22071620691975|
|SRP090154|4.25915264882035|
|SRP090152|4.18694692579925|
|SRP090150|2.87519899294173|
|SRP090147|2.65520374009004|
|SRP090140|2.99612745328042|
|SRP090136|4.22567148370127|

Metadata:

|ID|type|age|sex|
|---|---|---|---|
|SRP090252|disease|71.65|male|
|SRP090268|control|70.05|female|
|SRP090265|control|70.85|female|
|SRP090264|control|70.21|female|
|SRP090263|control|70.06|female|
|SRP090258|disease|69.53|female|
|SRP090250|disease|70.02|male|
|SRP090244|disease|70.07|female|
|SRP090242|control|69.78|female|
|SRP090227|control|69.74|female|
|SRP090226|control|69.7|male|
|SRP090224|disease|69.67|male|
|SRP090222|disease|71.4|male|
|SRP090217|control|71.53|male|
|SRP090216|control|71.84|female|
|SRP090215|control|71.26|male|
|SRP090209|control|69.41|female|
|SRP090205|control|70.63|female|
|SRP090193|control|70.25|female|
|SRP090179|disease|70.16|female|
|SRP090177|disease|70.2|female|
|SRP090175|disease|71.02|female|
|SRP090173|disease|70.11|male|
|SRP090171|control|70.55|female|
|SRP090170|control|71.11|female|
|SRP090169|disease|68.96|female|
|SRP090167|disease|70.97|female|
|SRP090163|disease|70.14|male|
|SRP090161|disease|70.39|female|
|SRP090157|disease|70.96|female|
|SRP090154|disease|71.04|female|
|SRP090152|disease|71.24|male|
|SRP090150|disease|71.58|female|
|SRP090147|control|71.39|female|
|SRP090140|disease|70.15|female|
|SRP090136|disease|70.15|male|

Now, I want to fit linear regression model and find the regression coefficient of disease vs control in two conditions: 1. univariate (only disease vs control without any covariates) and in mulivariate ( considering the age, sex as covariate of types) condition.
Can anyone please help me?

Thanks, in advance

[andresrcs]

What have you tried so far? what is your specific problem?, we are more inclined towards helping you with specific coding problems rather than doing your work for you.

Could you please turn this into a self-contained REPRoducible EXample (reprex)? A reprex makes it much easier for others to understand your issue and figure out how to help.

If you've never heard of a reprex before, you might want to start by reading this FAQ:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…