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I'm working on a study where I employed an analysis of covariance (Ancova) with unbalanced factors. I used permutation tests to obtain p-values for my estimates since my observation does not come from a random sample of some statistical population. My Ancova was found significant and I wanted to conduct multiple pairwise comparisons (MPC) to observed differences between my factors.

Since my factors are unbalanced, I ran my analysis using least-square means with the function lsmean() from the package {lsmeans} in R 3.1.2.

unb_ancova <- lm(doorw10_sqrt ~ MDS + modularity_cluster, data=df_mandrill)
pairwise.lsm <- lsmeans(unb_ancova, list(pairwise ~ modularity_cluster), 
adjust = c("tukey"))    

This code return a list containing two tables:

Table 1

Table 2

From this point, my first clue was to generate the bootstrapped 95% confidence interval for estimate pairwise differences (table 2, column 2). However, I did not found how to extract this estimate from the lsmeans object. Moreover, i'm not sure this is the right thing to do. Finally, how do I apply tukey p-value adjustment for p-values generated by permutation?

Thanks for the feedback.

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If you do

pairwise.summ = summary(pairwise.lsm[[2]])

the result is a data.frame which you can use as you please. The command vignette("using-lsmeans") displays a PDF file that has a lot of information and examples on how to use the package and what kinds of objects are produced.

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