[R-meta] Differences in calculation of CVR in escalc()

Mon, Oct 9, 2017 9:34 AM

Dear Michael and others,

thanks for your reply.

I discovered the difference: in escalc(), in the calculation of the 
variance of lnCVR (vi), the subtraction of term including the 
correlation (Eqn 12 in Nakagawa et al 2015) is ommited. Does anybody 
know, if this Is this due due to any mathematical reasons, or just to 
keep it simple? Or should/could this be adjusted in the function?

The values for vi estimated by escalc() differ from the values based on 
the original equation, on average by a factor of 2.6 assessed on the 
gibson example dataset.

Best regards,

Samuel

On 09/10/17 16:17, Michael Dewey wrote:

Dear Samuel

Not sure what the issue is but the code from escalc is

????????? if (measure == "CVR") {
??????????????? yi <- log(sd1i/m1i) + 1/(2 * (n1i - 1)) - log(sd2i/m2i) -
????????????????? 1/(2 * (n2i - 1))
??????????????? vi <- 1/(2 * (n1i - 1)) + sd1i^2/(n1i * m1i^2) +
????????????????? 1/(2 * (n2i - 1)) + sd2i^2/(n2i * m2i^2)
??????????? }

Note you can obtain this by going
library(metafor)
sink("escalc.txt")
escalc.default
sink()

and examining escalc.txt with your favourite text editor

Michael

On 09/10/2017 13:12, Samuel Knapp wrote:

Dear all,

I am conducting a meta-analysis on the stability of crop yields. I now
follow the approach suggeted by Nakagawa et al. (2015) approach and its
implementation in the metafor package, which helps me a lot!

As I first step I compared the estimates of the escalc function for ROM,
VR and CVR to the actual formulas (actually I used the functions in the
supplement of Nakagawa). Fortunately, they all yielded the same
estimates, except for the variance estimate of CVR. I did the
calculations on the gibson example data. The respective code (only for
CVR) is:

data <- get(data(dat.gibson2002))

metadat <- escalc(measure="CVR", m1i=m1i, m2i=m2i, sd1i=sd1i, sd2i=sd2i,
n1i=n1i, n2i=n2i, data=data)

# functions from Nakagawa et al. (2015)
Calc.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN){
? ? ES<-log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - 
log(CMean)
+ 1 / (2*(CN - 1)))
? ? return(ES)
}
Calc.var.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN, 
Equal.E.C.Corr=T){
? ? if(Equal.E.C.Corr==T){
? ??? mvcorr<-cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
? ??? S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr *
sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) +
? ???????? ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr *
sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
? ? }? else{
? ??? Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate? # corrected
(missing log()), was "cor.test(log(EMean), (ESD))$estimate"
? ??? Emvcorr<-cor.test(log(EMean), log(ESD))$estimate
? ??? S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 *Cmvcorr *
sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) +
? ???????? ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 *Emvcorr *
sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
? ? }
? ? return(S2)
}

# compare

with(data,Calc.lnCVR(m2i,sd2i,n2i,m1i,sd1i,n1i))
metadat$yi # is the same
# with pooled correlation
with(data,Calc.var.lnCVR(m2i,sd2i,n2i,m1i,sd1i,n1i,Equal.E.C.Corr = T))
metadat$vi # NOT THE SAME!!!
# with separate correlations for E and C
with(data,Calc.var.lnCVR(m2i,sd2i,n2i,m1i,sd1i,n1i,Equal.E.C.Corr = F))
metadat$vi # ALSO NOT THE SAME!!!


I checked all the equations in the Nakagawa functions and couldn't find
any error. Also, I tried the pooled and separate correlation.
Unfortunately, I didn't manage to access the code behind the escalc
function in order to check the underlying calculations.

Does anybody have a suggestion, what this difference could be due to?

(Versions: R 3.4.2, metafor 2.0)

Many thanks,

Sam

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

[R-meta] Differences in calculation of CVR in escalc()

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