[R-meta] Meta-analysis in R when there is no sampling variances

You cannot just replace 'vi' with 'wi'. Sampling variances and weights are
different things.

As I suggested, calculate approximate sampling variances if that's the
best thing that can be done. Use those for the 'V' argument (the second
argument -- the one that you are currently misusing for specifying weights).

Since you have a multilevel structure, the bootstrapping needs to account
for this. It would require considerable thought to implement this correctly.

An alternative is (still) to use robust(), using 'site_id' as the
clustering variable.

Best,
Wolfgang

-----Original Message-----
From: Resham Thapa [mailto:reshambt1 at gmail.com]
Sent: Friday, June 30, 2017 19:39
To: Viechtbauer Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Meta-analysis in R when there is no sampling
variances

ATTACHMENT(S) REMOVED: data_eg.csv

Dear Dr. Wolfgang,

Thank you for your quick response.

In my dataset, I have sample size (replications) and means for both
treatment and control groups. With that, I am calculating the log response
ratio (lrr) as an effect size. Yes, most of the articles in my search have
not reported SD or CV. I found that many researchers in my field prefer to
weight effect sizes using Wi=(Nt*Nc)/(Nt+Nc); where Nt and Nc are
replicates for treatment and control groups. Here is one link of a paper:
https://www.researchgate.net/publication/315379724_Increased
_plant_uptake_of_native_soil_nitrogen_following_fertilizer_
addition_-_not_a_priming_effect ( Increased plant uptake of native soil
nitrogen following fertilizer addition ? not a priming effect?).

My understanding is:
1. We can replace 'vi' with 'wi' and conduct the meta-analysis. Also, I
think multiple effect sizes from the study and multiple studies from the
same site will be more correlated with each other. To account for this, I
need to perform multi-level meta-analysis using random statement as
outlined in your website. So, my code looks like:

library(metafor)
dat <- read.csv('C:/Users/resham.thapa/Desktop/data_eg.csv') dat
# Calculating weighted effect size with multiple, nested
location/study/year as random effects.
res <- rma.mv(lrr, wi, random = ~ 1|site_id/study_id/site_year/com_control,
data=dat, method="REML")
res

Is this correct?

2.  Then, I need to perform non-parametric bootstrapping by slightly
modifying the codes from your website. However, I always get error while
running it. Any suggestions.

library(boot)
boot.func <- function(dat, indices) {
  res <- try(rma.mv(lrr, wi, random = ~ 1|site_id/study_id/site_year/com_control,
data=dat, method="REML", subset=indices), silent=TRUE)
  if (is.element("try-error", class(res))) {
     NA
  } else {
     c(coef(res), vcov(res), res$sigma)
  }
}

res.boot <- boot(dat, boot.func, R=4999)
## There were 50 or more warnings (use warnings() to see the first 50)
warnings()
# 'V' appears to be not positive definite.
boot.ci(res.boot, index=1:2)
I have attached a subset of my data to illustrate about the nature of my
data-set more clearly. Thank you in advance.

Regards'
Resham