How can I perform tests for equality of variance of data points in a row when I have multiple (400k+) rows

Question

I have data points for two groups arranged in the same row, and multiple rows of data (400k+). I would like to compare the variance of two groups for each of these 400K+ rows. The data would be something like the following -

y<-rbind(c(1,2,20,50,100,1,2,3,1,2),c(20,2,80,50,100,1,2,3,1,2))
group<-structure(c(1L,1L,1L,1L,1L,2L,2L,2L,2L,2L), .Label = c("T","C"), class="factor")

I can run the leveneTest from the car package on a single row of data, for example -

leveneTest(y = y[1,], group = group) # first row of data

Levene's Test for Homogeneity of Variance (center = median)
  Df F value  Pr(>F)  
group  1   4.527 0.06603 .
   8 

or

leveneTest (y = y[2,], group = group) # second row of data

Levene's Test for Homogeneity of Variance (center = median)
  Df F value   Pr(>F)   
group  1   11.92 0.008662 **
   8 

But obviously this would be impractical for 400k+ rows of data.

I thought it would be something simple, like using the apply as I would for the t.test, for example -

apply(y, 1, function (x) t.test(x[1:5],x[6:10])$p.value)
[1] 0.15260837 0.05551746

But when I try it for the leveneTest

apply(y, 1, function(x) leveneTest (y = y, group = group))

I get the following error

Error in complete.cases(y, group) : 
not all arguments have the same length

Does any one know how to do this?

Answer 1

As we are using the anonymous function call, the 'y' for leveneTest is 'x' (i.e. values in each row) and not the full dataset.

apply(y, 1, function(x) leveneTest (y = x, group = group))

Or instead of using the anonymous call, the following should also work

apply(y, 1, FUN = leveneTest, group=group)