[R-meta] Question on Forest Plot by subgroups

[Gabriel Cotlier]

Hello all,

Yesterday I posted a solution for the plotting forest plot by subgroups for
my particular case and I was actually making the mistake when choosing the
variable for the argument "order" which I corrected in the previously
posted code (found my own mistake in the argument "order").

But I also posted that was redundant information in my case to use
subset=(alloc
== 1) for the subgroups models: res.s, res.r and res.a and indeed it is if
I use "alloc" as follows:

res.s <- rma(yi, subset=(alloc == 1), vi, data=dat)
res.r <- rma(yi, subset=(alloc == 1), vi, data=dat)
res.a <- rma(yi, vi, subset=(alloc == 1),  data=dat)

However, I found out that this gives the same overall result for each
sub-group as for the total data.
Therefore I correct it herein as follows:  to get at the end of each
subgroup its particular overall results the use subset= c() is needed.
It  worked for me as follows:

### fit random-effects model in the three subgroups
res.s <- rma(yi, vi, subset= c(Type == "Rainfed"), data=dat)
res.r <- rma(yi, vi, subset = c(Type == "Experimental"), data=dat)
res.a <- rma(yi, vi, subset = c(Type == "Computational simulation"),
data=dat)

In this way it will give the overall model results for each subgroup
correctly together with the overall result for the complete dataset at the
end.

Sorry for the confusion.
Kind regards,
Gabriel

On Mon, Jun 19, 2023 at 5:49 PM Gabriel Cotlier <gabiklm01 using gmail.com> wrote:

> Hello all,
>
> I found the solution to the problem to the code I plotted previously. The
> problem was that I was ordering by "alloc" which is a column of sing() of
> correlations and not by the column that divide the groups (3 groups
> expressed in a column named "Type"  : Computational
> simulation","Experimental","Natural Rainfed") which in my case is named
> "Type",after correcting this I also remove unnecessary redundant
> information subset=(alloc == 1) in each of the models res.s, res.r and
> res.a. Please find the corrected working code below with highlighted
> locations where modifications were made  :
>
> ############################## CODE
> ###########################################
>
> pdf(file="ForestPlotCompleteDataSetRahul_Test.pdf", width = 12, height =
> 35)
>
> mlabfun <- function(text, res) {
>   list(bquote(paste(.(text),
>                     " (Q = ", .(formatC(res$QE, digits=2, format="f")),
>                     ", df = ", .(res$k - res$p),
>                     ", p ", .(metafor:::.pval(res$QEp, digits=2,
> showeq=TRUE, sep=" ")), "; ",
>                     I^2, " = ", .(formatC(res$I2, digits=1, format="f")),
> "%, ",
>                     tau^2, " = ", .(formatC(res$tau2, digits=2,
> format="f")), ")")))}
>
> forest(res,
>        top = 2,
>        #xlim=c(-8, 6),
>        #alim = c(-3.36077, 5.181815),
>        alim = c(-3.8, 6),
>        #at=log(c(0.05, 0.25, 1, 4)),
>        #atransf=exp,
>        #width = 4,
>        showweights = TRUE,
>        ilab=cbind(ni),  # content of extra columns of information
>        ilab.xpos=c(-4.6), # position of extra columns of information
>        cex=0.75,
>        ylim=c(-1, 161), # rows in the y-axis from min-row to max-row with
> text.
>        slab = paste(dat$Authors, dat$Year, sep = ", "),
>        order=Type,
>        rows=c(2:15,19:85,89:156), # this divide by groups the studies (3
> groups)
>        mlab=mlabfun("RE Model for All Studies", res),
>        #psize= 1 ,
>        efac = 0.2, # size of the polygon (the diamond)
>        # header="Author(s) and Year")
> )
> # abline(h=res$k+8, lwd=2, col="white")
>
> op <- par(cex=0.75, font=2)
>
> ### add additional column headings to the plot
> text(c(-15), res$k+10,  c("Author(s) and Year"), cex = 1.2, font = 2)
> text(c(-4.6), res$k+10,  c("Samples size"), cex = 1.2, font = 2)
> text(c(9.7),  res$k+10,  c("Weight"),cex = 1.2, font=2)
> text(c(9.7),  res$k+9.3, c("%"),cex = 1.2, font=2)
> text(c(12.6),  res$k+10,  c("Fisher's zr [95% CI]"),cex = 1.2, font=2)
> text(c(res$k+13), c("Complete Dataset"), cex = 2, font = 2)
>
> par(font=4)
>
> ### add text for the subgroups
> text(-17.2, c(16,86,157), pos=4, c("Computational simulation",
>                                    "Experimental",
>                                    "Natural Rainfed"), cex = 1.3)
>
> par(op)
>
> ### fit random-effects model in the three subgroups
> res.s <- rma(yi, vi, data=dat)
> res.r <- rma(yi, vi, data=dat)
> res.a <- rma(yi, vi,  data=dat)
>
> ### add summary polygons for the three subgroups
> addpoly(res.s, row= 88, mlab=mlabfun("RE Model for Subgroup", res.s),efac
> = 0.2, col='red')
> addpoly(res.r, row= 18, mlab=mlabfun("RE Model for Subgroup", res.r),efac
> = 0.2, col='green')
> addpoly(res.a, row= 1, mlab=mlabfun("RE Model for Subgroup", res.a),efac =
> 0.2, col='blue')
>
>
> dev.off()
>
>
>
>
>
> On Mon, Jun 19, 2023 at 3:46 PM Gabriel Cotlier <gabiklm01 using gmail.com>
> wrote:
>
>> Hello all,
>>
>> I have taken an example from here
>> <https://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups> and
>> modified it to my case study of Fisher's z correlations meta-analysis with
>> the code below.
>> I would like to plot a forest plot in which each of the subgroups ( 3
>> groups expressed in a column named "Type"  : Computational
>> simulation","Experimental","Natural Rainfed") each showing the studies
>> (rows) with positive correlations and those with negative correlations
>> divided in agreement to the line of no effect located at zero. I have used
>> a column named "alloc" which has a 1 for those correlations (rows) with
>> positive value and -1 for those with negative value, however for some
>> reason it does not work as expected. It mixes the lines from
>> different groups, ploting them where they do not correspond and also does
>> not plot the model summary for the second and third group.
>> I tried to change several arguments to solve it, however unsuccessfully.
>>
>> What could be the error?
>> Thanks a lot for your help.
>>
>> ############################## CODE
>> ###########################################
>>
>> pdf(file="ForestPlotCompleteDataSetbyGroups.pdf", width = 12, height = 35)
>>
>> mlabfun <- function(text, res) {
>>   list(bquote(paste(.(text),
>>                     " (Q = ", .(formatC(res$QE, digits=2, format="f")),
>>                     ", df = ", .(res$k - res$p),
>>                     ", p ", .(metafor:::.pval(res$QEp, digits=2,
>> showeq=TRUE, sep=" ")), "; ",
>>                     I^2, " = ", .(formatC(res$I2, digits=1, format="f")),
>> "%, ",
>>                     tau^2, " = ", .(formatC(res$tau2, digits=2,
>> format="f")), ")")))}
>> forest(res,
>>        top = 2,
>>        # xlim=c(-8, 6),
>>        # alim = c(-3.36077, 5.181815),
>>        alim = c(-3.8, 6),
>>        # at=log(c(0.05, 0.25, 1, 4)),
>>        # atransf=exp,
>>        # width = 4,
>>        showweights = TRUE,
>>        ilab=cbind(ni),  # content of extra columns of information
>>        ilab.xpos=c(-4.6), # position of extra columns of information
>>        cex=0.75,
>>        ylim=c(-1, 161), # rows in the y-axis from min-row to max-row with
>> text.
>>        slab = paste(dat$Authors, dat$Year, sep = ", "),
>>        order=alloc,
>>        rows=c(2:15,19:85,89:156), # this divide by groups the studies (3
>> groups) from column named "Type"
>>        mlab=mlabfun("RE Model for All Studies", res),
>>        #psize= 1 ,
>>        efac = 0.2, # size of the polygon (the diamond)
>>        # header="Author(s) and Year")
>>        )
>>
>> op <- par(cex=0.75, font=2)
>>
>> text(c(-15), res$k+10,  c("Author(s) and Year"), cex = 1.2, font = 2)
>> text(c(-4.6), res$k+10,  c("Samples size"), cex = 1.2, font = 2)
>> text(c(9.7),  res$k+10,  c("Weight"),cex = 1.2, font=2)
>> text(c(9.7),  res$k+9.3, c("%"),cex = 1.2, font=2)
>> text(c(12.6),  res$k+10,  c("Fisher's zr [95% CI]"),cex = 1.2, font=2)
>> text(c(res$k+13), c("Complete Dataset"), cex = 2, font = 2)
>>
>> par(font=4)
>>
>> text(-17.2, c(16,86,157), pos=4, c("Computational simulation",
>>                                  "Experimental",
>>                                  "Natural Rainfed"), cex = 1.3)
>> par(op)
>>
>> ### fit random-effects model in the three subgroups
>> res.s <- rma(yi, vi, subset=(alloc == 1), data=dat)
>> res.r <- rma(yi, vi, subset=(alloc == 2), data=dat)
>> res.a <- rma(yi, vi, subset=(alloc == 3),  data=dat)
>>
>> ### add summary polygons for the three subgroups
>> addpoly(res.s, row= 88, mlab=mlabfun("RE Model for Subgroup", res.s),efac
>> = 0.2, col='red')
>> addpoly(res.r, row= 18, mlab=mlabfun("RE Model for Subgroup", res.r),efac
>> = 0.2, col='green')
>> addpoly(res.a, row= 1, mlab=mlabfun("RE Model for Subgroup", res.a),efac
>> = 0.2, col='blue')
>>
>> dev.off()
>>
>

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