[R-meta] measure="CVR" in escalc

Viechtbauer, Wolfgang (SP) wolfg@ng@viechtb@uer @ending from m@@@trichtuniver@ity@nl
Tue May 22 17:29:20 CEST 2018

Hi Sean,

If you install the 'devel' version:


then escalc() also has measure="CVRC" for the coefficient of variation ratio for pre-post or matched designs (that is, y = logCV_1 - logCV_2 for matched groups 1 and 2 or time 1 and time 2). Note that computation of the sampling variance then requires knowing the correlation beween the measurements for matched pairs in groups 1 and 2 or at time 1 and time 2.

I just realized that I forgot to document measure "CVRC" in help(escalc). Will do so asap.


-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Sean Bloszies
Sent: Tuesday, 22 May, 2018 17:16
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] measure="CVR" in escalc

Hi all,
Escalc can give you an effect size that is the difference in CV's between
two groups if you tell it measure="CVR". The calculations for difference
and sampling variance in lnCV's this is based on assumes independence
between groups (Nakagawa et al 2015).  One example they use is male vs.
female animals. What if instead the source data had been weights of mated
pairs of male and female sampled over time? Or instead, in my case, if I
have crop yield info for paired treatment and control field plots with CV's
over time, measured repeatedly on the same adjacent two plots year after
year.  Do I need to take into account lack of independence between
treatment and control by modeling this covariance somehow? If so, how do I
do that in R?

If this lack of independence is a problem, would an even simpler t-test of
the ratio of CV's with a null mean of 0 be more appropriate?

Thanks for having me on the list.

*Reference: Nakagawa et al. , 2015. Meta-analysis of variation:
**ecological and evolutionary applications and beyond. Methods Ecol Evol
**6, 143–152. doi:10.1111/2041-210X.12309

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