LSMeans [General Sta­tis­tics]

posted by ElMaestro  – Denmark, 2018-02-27 09:02  – Posting: # 18469
Views: 2,742

Sorry about this,

I am a bit backward.

Once again I would like to raise the wonderful topic of LSMeans.
They are a SAS invention and I am still not sure I have truly understood what the hell they really are. Or more correctly, I am very sure I have no clue whatsoever about them.
Now, before someone throws a handful of web pages at me, I want to assure you, I have read them already. I am posting this because I still don't get it after all that reading e.g. here, here , and here.

The recent question about baseline adjustment and covariates in this forum made me try to understand LSMeans again. So I have been sleepless since then, of course. Shouldn't have done it. But that's hindsight.

For example: "When covariates are present in the model, the LSMEANS statement produces means which are adjusted for the average value of the specified covariate(s)."
I have no friggin clue what that really means.

Can someone, without using package lsmeans in R or automated tools, explain in slowmotion me what LSMeans really are and how exactly you would go about deriving LSMeans from scratch in a concrete dataset given a model? Feel free to use the code in the thread mentioned above as a starting point, this would ease my understanding.

Muchas gracias.


Edit: Category changed; see also this post #1. [Helmut]

if (3) 4

x=c("Foo", "Bar")
b=data.frame(x)
typeof(b[,1]) ##aha, integer?
b[,1]+1 ##then let me add 1



Best regards,
ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

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