CODEWITHSUNDEEP
rstatisticsanovamultivariate-testingmanova
Rohan
Rohan

Reputation: 54

Tukey HSD for multiple variables and single variable return different results

I have tried to run Tukey HSD for a multi-variable dataset. However, when I run the same test on a single variable, the results are completely opposite.

While running for multiple variables, I observed the following error in ANOVA output:

8 out of 87 effects not estimable Estimated effects may be unbalanced

While running for single variable, I observed the following error in ANOVA output:

Estimated effects may be unbalanced

Is this in any way related to the completely opposite Tukey HSD output which I received? Also, how do I go on solving this problem?

I used aov() and have close to 500000 datapoints in my dataset.

to be more specific, the following code gave me a different result:

code1:

lm_test1 <- lm(y ~ x1+ x2, data=data)
glht(lm_test1, linfct = mcp(x1 = "Tukey"))

code2:

lm_test1 <- lm(y ~ x1, data=data)
glht(lm_test1, linfct = mcp(x1 = "Tukey"))

Please tell me how this is possible...

Upvotes: 0

Views: 1643

Answers (1)

Rohan
Rohan

Reputation: 54

after some more research, I found the answer to this, so thought I should post this. Anova in R is by default type - I anova. So that means the first variable that we input, the effects are considered without controlling for any other factors, on the other hand, for the other variables, the results are shown after controlling for the effects of other variables. Therefore, since I was inputting my variable as the 2nd variable, the results shown were after controlling for the 1st variable which was by chance, in a completely opposite direction to looking at a direct effect.

Upvotes: 0

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