Basic - T-Test -> Grouping Factor Must Have Exactly 2 Levels

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 you're 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

R t-test Grouping factor must have exactly 2 levels error

The problem is that your grouping variable has more than two levels, when the t.test requires that you cannot have more than two levels.

Here is a reproduction of your error:

library(tidyverse)

##This will reproduce your error

##Create some fake data
data_test <- tibble(measure = c(rnorm(100,30,5),rnorm(100,15,5)),
       group = factor(rep(c("A","B","C"),c(95,95,10))))

table(data_test$group) ##Notice that you have three levels

#Try to run the test
t.test(measure~group, data = data_test, paired = TRUE)

Here is an example that will run

##This will not result in a error, because you only have two groups

data_test2 <- tibble(measure = c(rnorm(100,30,5),rnorm(100,15,5)),
                    group = factor(rep(c("A","B"),c(100,100))))

table(data_test$group) ##Notice that you have the required two levels
t.test(measure~group, data = data_test2,paired = TRUE) ##Test will now run

Takeaway: Check the number of levels in your data. If there are more than two, recode or delete them.

R: wilcoxon test Error: Grouping Factor must have exactly 2 levels

I just figured out , instead of ~ works for my problem.

R ttest inside ddply gives error grouping factor must have exactly 2 levels

You get an error because , you get a for at least one sub-group , unique status value for all score's.

This reproduce the error, the status is unique (equal to 1) for all scores.

dx = read.table(text='   score status
1 1 1 
2 2 1 
3 3 1 ')

t.test(score ~ status, data = dx) 
Error in t.test.formula(score ~ status, data = dx) : 
  grouping factor must have exactly 2 levels

this correct the problem but create another known problem with t.test, you should have enough observations( I think >= 2):

dx = read.table(text='   score status
1 1 1 
2 2 1 
3 3 2 ')

t.test(score ~ status, data = dx) 
Error in t.test.default(x = 1:2, y = 3L) : not enough 'y' observations

Finally this correct all the problems:

dx = read.table(text='   score status
1 1 1 
2 2 1 
3 3 2 
4 4 2')

t.test(score ~ status, data = dx) 

Welch Two Sample t-test

data:  score by status
t = -2.8284, df = 2, p-value = 0.1056
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -5.042435  1.042435
sample estimates:
mean in group 1 mean in group 2 
            1.5             3.5 

EDIT I explain the problem without giving a solution, because you don't give a reproducible example.

one solution is do computation only for good groups:

  ddply(df, c("freq","snpsincluded"), function(x)
      { 
       if(length(unique(x$status)==2)
         pval=t.test(score~status,data=x)$p.value
     })

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