LowerMiddleClass LowerWorkingClass LowerWorkingClass UpperWorkingClass LowerMiddleClass LowerMiddleClass LowerMiddleClass UpperWorkingClass LowerMiddleClass Of the current levels, or allow you to edit the levels by assigning a new character vector to it. It will either produce a character vector The levels() command accesses the levels of a factor. I haven't used ordered factors much myself, so I don't really understand how It is possible to alsoĭefine ordered factors, which will strongly affect model fitting. That is, the factor levels define groups rather than an ordering. The factor vector that we built is unordered. In essence, the factor vector has a representation like this: Vector We can also add meaninful labels to the factor levels. Theįactor() function will convert it to a factor vector. Say we had collected class data in a study,Īnd coded lower working class, upper working class, and lower middle class with numeric codes, 1, 2, and 3, repectively.įirst, lets simulate a sample of 10 speakers. > install.packages(c("ISwR","languageR"),dependencies = T)Īlso, download the zip file containing data from Johnson 2008's chapter on Phonetics:įor the most part, if we're using data which has some variable coded as a character string, it is actually a factor withĪ good way to grasp the structure of factors is to build one from scratch. Install and load the ISwR and languageR libraries. Introductory Statistics with R: Chapter 1Īnalyzing Linguistic Data: A practical introduction to statistics: Chapter 1 While we settle
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