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rfactorialposthoc

Error with post hoc tests in ANCOVA with factorial design

Does anyone know how to do post hoc tests in an ANCOVA model with a factorial design?

I have two vectors consisting of 23 baseline values (covariate) and 23 values after treatment (dependent variable) and I have two factors with both two levels. I created an ANCOVA model and calculated the adjusted means, standard errors and confidence intervals. Example:

library(effects)

baseline = c(0.7672,1.846,0.6487,0.4517,0.5599,0.2255,0.5946,1.435,0.5374,0.4901,1.258,0.5445,1.078,1.142,0.5,1.044,0.7824,1.059,0.6802,0.8003,0.5547,1.003,0.9213)
after_treatment = c(0.4222,1.442,0.8436,0.5544,0.8818,0.08789,0.6291,1.23,0.4093,0.7828,-0.04061,0.8686,0.8525,0.8036,0.3758,0.8531,0.2897,0.8127,1.213,0.05276,0.7364,1.001,0.8974)

age = factor(c(rep(c("Young","Old"),11),"Young")) 
treatment = factor(c(rep("Drug",12),rep("Placebo",11)))

ANC = aov(after_treatment ~ baseline + treatment*age)

effect_treatage = effect("treatment*age",ANC)
data.frame(effect_treatage)

  treatment   age       fit        se     lower     upper
1      Drug   Old 0.8232137 0.1455190 0.5174897 1.1289377
2   Placebo   Old 0.6168641 0.1643178 0.2716452 0.9620831
3      Drug Young 0.5689036 0.1469175 0.2602413 0.8775659
4   Placebo Young 0.7603360 0.1462715 0.4530309 1.0676410

Now I want test if there is a difference between the adjusted means of

Young-Placebo:Young-Drug
Old-Placebo:Old-Drug
Young-Placebo:Old-Drug
Old-Placebo:Young-Drug

So I tried:

library(multcomp)
pH = glht(ANC, linfct = mcp(treatment*age="Tukey"))
# Error: unexpected '=' in "ph = glht(ANC_nback, linfct = mcp(treat*age="

And:

pH = TukeyHSD(ANC)
# Error in rep.int(n, length(means)) : unimplemented type 'NULL' in 'rep3'
# In addition: Warning message:
# In replications(paste("~", xx), data = mf) : non-factors ignored: baseline

Does anyone know how to resolve this?

Many thanks!

PS for more info see

R: How to graphically plot adjusted means, SE, CI ANCOVA

Solution

  • You need to use the which argument in TukeyHSD; "listing terms in the fitted model for which the intervals should be calculated". This is needed because you have a non-factor variable in the model ('baseline'). The variable causes trouble when included, which is default when which is not specified.

    ANC = aov(after_treatment ~ baseline + treatment*age)
TukeyHSD(ANC, which = c("treatment:age"))

    If you wish to use the more flexible glht, see section 3, page 8- here