I tried to do a Funnel plot of a dataset for a disease-prevention paper, view dataset here. Got the plot, but am worried it is wrong.The dataset contains the Hazard ratio(HR), Lower control limit(LCL), Upper control limit(UCL) and the Standard Errors(SE)[of Ln(HR)] values, and I used the SE values to calculate Variances(var in the dataset). Here is the code I used:

library(metafor)
age <- read.csv("age.csv", header=TRUE)  
age
res <- rma(yi=lnhr, vi=var, data=age)
res
funnel(res,xlab = "ln(HR)",ylab = "SE.ln(HR)")

The question I had was this: In most studies I have seen, the Ln(HR) values are negative, whereas in the dataset I was working on, almost all are positive. So for the correct funnel, should I take yi=-lnhr, or is yi=lnhr the correct method, in the res object?

P.S:(According to the Metafor repository linked here, yi is effect-size, and vi is the variance)

II tried to do a Funnel plot of a dataset for a disease-prevention paper, view dataset here. Got the plot, but am worried it is wrong.The dataset contains the Hazard ratio(HR), Lower control limit(LCL), Upper control limit(UCL) and the Standard Errors(SE)[of Ln(HR)] values, and I used the SE values to calculate Variances(var in the dataset). Here is the code I used:

library(metafor)
age <- read.csv("age.csv", header=TRUE)  
age
res <- rma(yi=lnhr, vi=var, data=age)
res
funnel(res,xlab = "ln(HR)",ylab = "SE.ln(HR)")

The question I had was this: In most studies I have seen, the Ln(HR) values are negative, whereas in the dataset I was working on, almost all are positive. So for the correct funnel, should I take yi=-lnhr, or is yi=lnhr the correct method, in the res object?

P.S:(According to the Metafor repository linked here, yi is effect-size, and vi is the variance)

Update:Here are the results using the above code and dataset, taking yi=-lnhr

Random-Effects Model (k = 11; tau^2 estimator: REML)

tau^2 (estimated amount of total heterogeneity): 0.0001 (SE = 0.0002)

tau (square root of estimated tau^2 value): 0.0122

I^2 (total heterogeneity / total variability): 35.20%

H^2 (total variability / sampling variability): 1.54

Test for Heterogeneity:

Q(df = 10) = 16.8884, p-val = 0.0769**

Model Results:

estimate= -0.0379
se =0.0067

zval=-5.6850

pval<.0001

ci.lb=-0.0509

ci.ub=-0.0248 ***

And here is the funnel plot generated.