I am working on R, and have multiple data.frames. They all look like this one:
head(Stim.Adjs) LogFreq Word PhonCV FreqDev LengthDev 6316 2.673 enkele VC-CV-CV -0.1794029 -1.0608453 12638 2.415 laatste CVVC-CCV -0.2207496 -0.7113483 12639 2.415 laatste CVVC-CCV -0.2207496 -0.7113483 1633 2.248 bang CVC -0.2475127 -1.7598394 3167 2.243 bezig CVV-CVC -0.2483140 -1.4103424 25467 2.243 vroeger CCVV-CVC -0.2483140 -0.7113483
These are list of words extracted from a corpus, and the columns 4 and 5 represent measures of standard deviation of Frequency and Word length respectively. What I want to do is to have all lists with a similar frequency deviation and word length deviation (two separate controls). So far I had been doing this by comparing each dimension in pairs of list using t.tests, and then removing values from the lists so that the t.tests showed no significant difference in means.
Now that I have more than two lists, I'm a bit confused as to how to test if the means are equal or not. At first I though of an ANOVA, but I don't have a predictor variable, just 3 independent means (for each of the two values I want to control).
The question is, then, what alternative to the t.test could I use that allows me to compare the means of independent vectors. Help with the function for this in R is much appreciated.
Thanks in advance.