Contents
How to adjust for multiple comparisons with lsmeans?
Both of the lsmeans statements you show generate lists of lsmobj s, and each element of those lists is handled separately. If you want to incorporate an overall adjustment for two or more lists combined, it is technical and it takes a bit of work.
How to solve the lsmeans problem in R?
I have a lsmeans problem in R. I want to do a post-hoc analysis of an interaction, similar to examples provided in the lsmeans documentation. Should the p-values not be higher in the case of (A) than (B)? (A) makes 9 post-hoc comparisons, (B) only 3. Can I be sure that (A) is adjusted for all 9 comparisons? Thank you!
When to use least square means for multiple comparisons?
Least Square Means for Multiple Comparisons Comparisons of values across groups in linear models, cumulative link models, and other models can be conducted easily with the lsmeans package. Importantly, it can make comparisons among interactions of factors. L.S. means stands for least square means.
How can one make lsmeans correct across combinations?
How can one make lsmeans correct across combinations (i.e. for the total number of tests?) You can exert more control over what you get if you do the contrasts in a separate step: The by = NULL part of the last statement tells it to ignore the by grouping and thus treat it as one family of tests.
Which is the default adjustment method in lsmeans?
It is also a problem that different users expect different defaults. The next update of lsmeans (2.20 or later) will include an rbind method for ref.grid and lsmobj objects. It makes it easy to combine two or ore objects into one family, and defaults to the “mvt” adjustment method. Here is the present example:
What does LS mean mean in treatment levels?
L.S. means stands for least square means . Least square means are means for treatment levels that are adjusted for means of other factors in the model. For a more complete explanation, see the What are least square means? chapter. Adjustment of p -values for multiple comparisons is indicated with the adjust= option in the lsmeans function.
Least Square Means for Multiple Comparisons. Comparisons of values across groups in linear models, cumulative link models, and other models can be conducted easily with the lsmeans package. Importantly, it can make comparisons among interactions of factors. L.S. means stands for least square means.
Do you ignore the values of the LS means?
Typically you should ignore the values of the LS means themselves ( lsmeans) when using them with clm and clmm models. With default settings, the values of the LS means and the values of differences among the LS means are not easy to interpret.
How is the model specified in the lsmeans function?
In the lsmeans function, model specifies the model object that was previously fitted. Note the specialized formula where pairwise indicates that all pairwise comparisons should be conducted, and Speaker indicates the variable whose levels will be compared.