r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels
r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels
are you doing this:
t.test(y~x)
when you mean to do this
t.test(y,x)
In general use the ~ then you have data like
y <- 1:10
x <- rep(letters[1:2], each = 5)
and the , when you have data like
y <- 1:5
x <- 6:10
I assume youre doing something like:
y <- 1:10
x <- rep(1,10)
t.test(y~x) #instead of t.test(y,x)
because the error suggests you have no variation in the grouping factor x
The differences between ~ and , is the type of statistical test you are running. ~ gives you the mean differences. This is for dependent samples (e.g. before and after). , gives you the difference in means. This is for independent samples (e.g. treatment and control). These two tests are not interchangeable.
r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels
I was having a similar problem and did not realize given the size of my dataset that one of my ys had no values for one of my levels. I had taken a series of gene readings for two groups and one gene had readings only for group 2 and not group 1. I hadnt even noticed but for some reason this presented with the same error as what I would get if I had too many levels. The solution is to remove that y or in my case gene from my analysis and then the error is solved.
Related posts on r Basic T Test Grouping Factor :
- r – lmer error: grouping factor must be < number of observations
- r – How to debug contrasts can be applied only to factors with 2 or more levels error?
- r – Mixed Modelling – Different Results between lme and lmer functions
- r – data.table vs dplyr: can one do something well the other cant or does poorly?
- r – ggplot2 line chart gives geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?