The scale() function will center and/or scale a numeric object. Use center = TRUE to subtract the mean from the values; use scale = TRUE to divide the centered values by the standard deviation to produce z scores. center variable A scale(A, center = TRUE, scale = FALSE) generate z-scores for variable A scale(A, center = TRUE, scale = TRUE) Lets try it with EMG data
I’ve been analysing some future thinking data and playing around with packages that can do repeated ANOVA (I know, I should do LMM). There is more than one way to skin a cat and the differences come down to two things. the intuitiveness of the function arguments the readibility of the output Some checks to do before you start all of these packages assume that you within and between subjects variables are factors, but also that your participant id is a factor A few favourites Option 1: old school aov() This code comes from the ANOVA cookbook
Learning new things is HARD. Stats methods are particularly hard because it not just about running the test. You have to… learn when to run the test learn how to run the test learn how to interpret the test AND learn how to write about the results of the test Part of what makes this learning hard is that our research methods training has drummed into us that there is a RIGHT WAY TO DO IT.
When we want to learn how to do something new in R, we all start by googling it. Having worked out simple ANOVA, next on my list is repeated measures. Domnique Makowski has an excellent blog post that comes up at the top of the search results when you google “how to do repeated measures ANOVA in R”. He lures you in with a post that seems like it is going to solve all your problems and then and BAM… starts the post by saying “DON’T do it”.
In the kind of research that we do, t-tests can only take you so far. Most often we design factorial experiments where we are interested in both main effects and interactions. Because we work with infants and young children, who are both expensive to recruit/test and notoriously variable in their behaviour, we try to design experiments that use within-subjects designs; each child gives us more than one data point and we need to use repeated-measures analyses.
I am feeling more confident about my resolution to get rid of Excel and only use R for data wrangling and visualisation. Next steps… analysis. I’m starting simple (I presume) with t-tests. Mostly commonly I want to determine whether there is a difference in the performance of independent groups of kids, or a difference between kids' performance on two different conditions, or whether kids are just guessing (i.e. whether their performance differs significantly chance).
I was trying to work out how to do t-tests using my own data and the lsr package but ended up working with Dani’s AFL data from her book while trying to work out why R insisted that my outcome variable wasn’t numeric (it definitely was). Turns out that the lsr package doesn’t deal well with tibbles (which are created by default when you use read_csv to get your data) but if you use read.