How do I approach a linear mixed effects model with a 2-level group?

[s75moham]

My data looks something like this:

> head(my_data,n=8)

Subject Group     Value1     Value2   Outcome
1         A        12.404    5.00     1.33
2         A        14.898    4.34     6.77
3         A        13.870    2.53     5.01
4         A        14.313    9.33     6.82
5         B        13.407    4.99     8.42
6         B        13.480    2.59     9.11
7         B        13.761    1.42     8.21
8         B        12.607    0.55     8.32
...
...

I'm interested in performing a mixed linear effects model with a comparison between the Groups. I want to see the effect the group difference has on the different fixed variables (i.e. Value1 and Value2). I wasn't sure how to go about that. Initially this is what I had:

lme(outcome*Group~ (Value1+Value2)*Group, random= ~ 1|Subject, data=my_data)

But once I read further, I thought it should look something like this:

lme(outocme~Value1+Value2+Group, random=1|Subject, data=my_data)

However, when I do that I get an output that only lists group A but not group B for some reason.

Any help/guidance would be greatly appreciated.

Thanks.

[HanOostdijk]

Hello @s75moham, welcome in this group.

I have no experience at all with 'linear mixed effect models' but being interested I tried to reproduce your problem. However I run into errors such as object 'Subject' not found , but maybe I am not using the same packages as you. Therefore I enclose a REPRoducible EXample (reprex) of what I did.
A reprex consists of the minimal code and data needed to recreate the issue/question one is having. See e.g. whats-a-reproducible-example-reprex-and-how-do-i-do-one:

library(tibble)
library(nlme)

my_data = tribble(
  ~Subject, ~Group, ~Value1, ~Value2, ~Outcome,
 1,        "A",        12.404,    5.00,     1.33,
 2,        "A",        14.898,    4.34,     6.77,
 3,        "A",        13.870,    2.53,     5.01,
 4,        "A",        14.313,    9.33,     6.82,
 5,        "B",        13.407,    4.99,     8.42,
 6,        "B",        13.480,    2.59,     9.11,
 7,        "B",        13.761,    1.42,     8.21,
 8,        "B",        12.607,    0.55,     8.32
)

lme(Outcome~Value1+Value2+Group, random=1|Subject, data=my_data)
#> Error in reStruct(random, REML = REML, data = NULL): object 'Subject' not found

^{Created on 2020-09-03 by the reprex package (v0.3.0)}

I would be helpful if you present your problem in the same way.

By the way 'googling' on linear mixed-effects models using r also produced some results, but probably you are already familiar with these.