Thank you a lot for the update.
I understand leaving NaN/NA in these cases, that can make sense.
But feels to me that this situation could maybe produce a warning, to inform
the user of what had happened?
Kind regards,
Karolis K.
> On Jan 24, 2021, at 6:52 PM, Kurt Hornik wrote:
>
>> Karo
> Karolis K writes:
> To me it seems like returning chi-sq = 0 and p-value = 1 would make sense.
> It would also be consistent with other scenarios of equal variance in all
> groups. One example:
> fligner.test(1:8, gl(2,4))
> #Fligner-Killeen test of homogeneity of variances
> #
> #
Not sure
If all of the variances are zero, they are homogenous in that sense,
and I would give a p-value of 1 ..
if only *some* of the variances are zero... it's less easy.
I still would try to *not* give an error in such cases and even
prefer NA statistic and p-value.. because yes, the
To me it seems like returning chi-sq = 0 and p-value = 1 would make sense.
It would also be consistent with other scenarios of equal variance in all
groups. One example:
fligner.test(1:8, gl(2,4))
#Fligner-Killeen test of homogeneity of variances
#
# data: 1:8 and gl(2, 4)
# Fligner-Kille
> Karolis K writes:
Any preferences?
Best
-k
> Hello,
> In certain cases fligner.test() returns NaN statistic and NA p-value.
> The issue happens when, after centering with the median, all absolute values
> become constant, which ten leads to identical ranks.
> Below are a few examples:
>
Hello,
In certain cases fligner.test() returns NaN statistic and NA p-value.
The issue happens when, after centering with the median, all absolute values
become constant, which ten leads to identical ranks.
Below are a few examples:
# 2 groups, 2 values each
# issue is caused by residual values
