<div dir="ltr">Dear Greta and colleagues:<div>Probably, I think I found the solution to reproduce outside the metafor package the funnel plot without funnel background and with :</div><div><code style="box-sizing:border-box;background-color:rgb(248,248,248);color:rgb(51,51,51);font-family:ui-monospace,SFMono-Regular,Consolas,"Liberation Mono",Menlo,monospace;font-size:13.5px;padding:2px 4px;border-radius:4px">yaxis="seinv"</code><span style="color:rgb(44,62,80);font-family:-apple-system,"system-ui","Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";font-size:15px"> for the inverse of the standard errors</span></div><div><div>I got the same results as in the funnel plot from the metafor package using the inverse of the square root of the variance (not of the standard deviation) as 1/SE, that would be:</div><div>Inv_SE <- 1/sqrt(vi_data), where vi is the variance data. <br></div><div><br></div><div><br></div><div><br>## Greta example using metafore function funenel()<br>ri <- c(0.5, 0.6, 0.7, 0.8, 0.9)<br>ni <- c(100,110,150,200,250)<br>dat <-escalc(measure = "ZCOR", ri = ri, ni = ni)<br>funnel_all <- <a href="http://rma.mv">rma.mv</a>(yi, vi, data=dat)<br><br><br>png(file = "funnel_Greta_example.png",<br> width = 250, <br> height = 200,<br> res = 600, <br> units = "mm")<br>f1 <- funnel(funnel_all,<br> back = "white", <br> # shade = "white",<br> yaxis = "seinv", <br> level = 0,<br> # ylim = c(1, 5), <br> refline=0,<br> main="my plot", <br> ylab = "Presicion (1/SE)", <br> xlim = c(0.53,1.5))<br># shade = c("white", "gray55", "gray75"),<br># refline = 0)<br># bg = "grey") <br># legend = TRUE)<br>#grid(NULL, NULL,lwd = 1.6)<br>abline(h=c(9.849, 11.316,12.783, 14.249, 15.716 ), col="grey", lwd=1, lty=3)<br>abline(v=1, col="blue", lwd=2)<br>abline(v=c( 0.6, 0.8, 1, 1.2, 1.4), col="grey", lwd=1, lty=3)<br>dev.off()<br><br>##<br><br>## Variance : from general model extracted <br>vi_data <-funnel_all$vi<br><br>## Estimates : from general model extracted estimates<br>yi_data <-funnel_all$yi[1:5]<br><br>## square root of the variance<br>SE_5 <- 1/sqrt(vi_data)<br><br># estimates<br>E <-funnel_all$yi<br><br>N<-dat$ni<br><br>## construct data frame<br>df <- data.frame (Estimates = c(E), Standsrd_Error = c(SE_5))<br>View(df)<br>library(ggplot2)<br><br>###################################### MY PLOT ###################################<br><br>## Scatter plot <br>scaleFUN <- function(x) sprintf("%.2f", x)<br>p<- ggplot(df, aes(x=Estimates, y=Standsrd_Error)) + <br> geom_point(aes(size = Estimates), alpha=0.7, color="#2568E6")+<br> scale_size_area() + <br> labs(x = "Fisher's z",<br> y = "Standard Error (SE)")+<br> theme(plot.title = element_text(hjust = 0.5))+<br> # theme(plot.margin = unit(2 ,8, 8, 2), "cm"))+ <br> scale_y_continuous(n.breaks = 12,labels=scaleFUN)+<br> scale_x_continuous(n.breaks = 12,labels=scaleFUN)+<br> geom_vline(xintercept = 0)+<br> theme(axis.text.y = element_text(size = 15))+<br> theme(axis.text.x = element_text(size = 15))+<br> theme(axis.title.y = element_text(size = 15))+ <br> theme(axis.title.x = element_text(size = 15))+ <br> ggtitle( "Fisher's z vs. Standard Error")+<br> theme(plot.title = element_text(size = 17, face = "bold"))+ <br> theme(legend.text = element_text(size = 15))<br>p<br>png(filename = "myExample.png", width = 28, height = 18 ,units = "cm" , res =100 )<br>print(p)<br><br></div><div><br></div><div><br></div><div><br></div><div><img src="cid:ii_loo5drrq0" alt="image.png" width="1007" height="349" style="margin-right: 25px;"><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div> </div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 7, 2023 at 9:49 AM Gabriel Cotlier <<a href="mailto:gabiklm01@gmail.com" target="_blank">gabiklm01@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hello all, <div>According to: <a href="https://www.metafor-project.org/doku.php/plots:funnel_plot_variations" target="_blank">https://www.metafor-project.org/doku.php/plots:funnel_plot_variations</a> I think it could be the argument:</div><div><br></div><div><ul style="box-sizing:border-box;margin-top:0px;margin-bottom:10.5px;color:rgb(44,62,80);font-family:-apple-system,"system-ui","Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji";font-size:15px"><li style="box-sizing:border-box"><p style="box-sizing:border-box;margin:0px 0px 10.5px;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Helvetica,Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji""><code style="box-sizing:border-box;background-color:rgb(248,248,248);color:rgb(51,51,51);font-family:ui-monospace,SFMono-Regular,Consolas,"Liberation Mono",Menlo,monospace;font-size:13.5px;padding:2px 4px;border-radius:4px">yaxis="seinv"</code> for the inverse of the standard errors<br></p></li></ul>Is what gives the 1/SE for each element in the vector of effect sizes in the x-axis.</div><div><br></div><div>How can I "stand alone"—that would be outside the metafore package or not using the funnel function—to plot the same scatterplot of effect sizes (x-axis) against 1/SE (y-axis) in communion and a simple R scatter plot—without the funnel background—and afterwards use it to further cluster by coloring or using shapes ("pch") the effect sizes according to different categorical variables in the data frame?</div><div><br></div><div>Thanks a lot for your help.</div><div>Kind regards, </div><div>Gabriel</div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Nov 7, 2023 at 9:13 AM Gabriel Cotlier <<a href="mailto:gabiklm01@gmail.com" target="_blank">gabiklm01@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Dear Greta and colleges,<div><br></div><div>As can be seen below, Greta has provided a nice solution to a problem I could not solve before, which is to have as an output of metafor's package function funnel() for plotting the funnel plot without the background of the funnel itself, which I very slightly modified as follows:</div><div><br></div><div><font face="monospace">###################</font></div><div><font face="monospace">##</font></div><div><font face="monospace">##. CODE FUNNEL</font></div><div><font face="monospace">##</font></div><div><font face="monospace">###################</font></div><div><font face="monospace"><br></font></div><div><font face="monospace">## data</font></div><div><font face="monospace">ri <- c(0.5, 0.6, 0.7, 0.8, 0.9)<br>ni <- c(100,110,150,200,250)<br>dat <-escalc(measure = "ZCOR", ri = ri, ni = ni)<br><br>## model<br>funnel_all <- <a href="http://rma.mv" target="_blank">rma.mv</a>(yi, vi, data=dat)<br><br>## get max and min values for plot<br>#funnel_all$yi<br>#min(funnel_all$yi)<br>#max(funnel_all$yi)<br><br>## funnel plot<br>f1 <- funnel(funnel_all,<br> back = "white", <br> # shade = "white",<br> yaxis = "seinv", <br> level = 0,<br> # ylim = c(1, 5), <br> refline=0,<br> main="my plot", <br> ylab = "Presicion (1/SE)", <br> xlim = c(0.53,1.5))<br># shade = c("white", "gray55", "gray75"),<br># refline = 0)<br># bg = "grey") <br># legend = TRUE)<br>#grid(NULL, NULL,lwd = 1.6)<br>abline(h=c(9.849, 11.316, 12.783, 14.249, 15.716 ), col="grey", lwd=1, lty=3)<br>abline(v=1, col="blue", lwd=2)<br>abline(v=c( 0.6, 0.8, 1, 1.2, 1.4), col="grey", lwd=1, lty=3)<br></font></div><div><br></div><div><br></div><div>This has been a very efficient solution for my problem of getting exactly the same funnel plot as the result of the metafore package function funnel() without the funnel as a background. However, now I am facing the challenge of having plotted the same funnel plot as the output from the metafor's funnel() function without the background but without the option of clustering by coloring or giving different point shapes to the effect szes (points in the funnel plot) according to a categorical in my data frame. I assume—probably wrongly—that for this task I would have to reproduce the same funnel plot as is output from the funnel plot function in the metafore package without the funnel background, as in the code above, and use my data frame with the categorical variables to color the points or give them different shapes and sizes using the categorical variables in my data frame. Now the funnel plot function plots in the x-axis the effect sizes, something I can easily get from my data frame, but in the y-axis it uses 1/standad error (or 1 / SD). The problem is that, as far as I understand, the standard error (SE) corresponds to the standard deviation, or the R function sd() which gives one value per input vector. Therefore, for some reason, plotting the effect sizes (a vector class numeric) in the x-axis and 1/sd(effect_sizes) will give me a number, not a vector of the same length as the effect sizes. </div><div><br></div><div><i><font color="#0000ff">Therefore, how could one reproduce the same funnel plot as in the metafore function (without the funnel background, just a scatterplot) with an x-axis composed of the vector of effect sizes and a y-axis with another vector corresponding to 1/stands error (1/SE) of each element in the x-axis--If 1/SE is is equal to 1/sd(efect_sizes) which is a scaler and not a vector ?</font></i></div><div> </div><div>I think maybe this could be achieved somehow by giving, in the y-axis, a kind of <font color="#0000ff"><i>"element-wise 1/SE"</i></font> to each element in the x-axis; that would be an value corresponding to 1/SE to each of the elements in the vector of effect sizes in the x-axis. Could this be the idea behind the funnel plot function with 1/SE on the y-axis?</div><div><br></div><div>If so, can this be somehow achieved following the example provided by Greta below?</div><div><br><span style="font-family:monospace">ri <- c(0.5, 0.6, 0.7, 0.8, 0.9)</span><br style="font-family:monospace"><span style="font-family:monospace">ni <- c(100,110,150,200,250)</span><br style="font-family:monospace"><span style="font-family:monospace">dat <-escalc(measure = "ZCOR", ri = ri, ni = ni)</span><br></div><div><br></div><div>Thanks a lot for your help and guidance.</div><div>Kind regards,</div><div>Gabriel</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, Jul 21, 2023 at 9:28 AM Gabriel Cotlier <<a href="mailto:gabiklm01@gmail.com" target="_blank">gabiklm01@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><font face="arial, sans-serif" size="4">Dear Greta, </font><div><font face="arial, sans-serif" size="4"><br></font><div><div><font face="arial, sans-serif" size="4">Thank you very much.</font></div><div><font face="arial, sans-serif" size="4">The code you provided for the funnel plot is exactly what I was looking for. </font></div><div><font face="arial, sans-serif" size="4"><br></font></div><div><font face="arial, sans-serif" size="4">I just changed my refline to zero and adjusted the xlim to the interval [-4 6], so it is exactly the same as the metafor::funnel plot but without all the background, keeping only the scattering of the points.</font></div><div><font face="arial, sans-serif" size="4"><br></font></div><div><font face="arial, sans-serif" size="4">Thanks a lot again.</font></div><div><font face="arial, sans-serif" size="4">Kind regards,</font></div><div><font face="arial, sans-serif" size="4">Gabriel</font></div><div><font face="arial, sans-serif" size="4"> </font></div><div><br></div></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Jul 20, 2023 at 11:21 PM Dr. Gerta Rücker <<a href="mailto:gerta.ruecker@uniklinik-freiburg.de" target="_blank">gerta.ruecker@uniklinik-freiburg.de</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Dear Gabriel,<br>
<br>
Both plots are correct and equivalent (though I like the metafor plot much more). If it is only to get rid of the confidence region, why don't you use the funnel() function of metafor and suppress all elements you don't want?<br>
For example, try (object funnel_all taken from your R code, fictitious data coming from me, as you did not provide them!)<br>
<br>
ri <- c(0.5, 0.6, 0.7, 0.8, 0.9)<br>
ni <- c(100,110,150,200,250)<br>
dat <-escalc(measure = "ZCOR", ri = ri, ni = ni)<br>
<br>
funnel_all <- <a href="http://rma.mv" rel="noreferrer" target="_blank">rma.mv</a>(yi, vi, data=dat)<br>
funnel(funnel_all, back = "white", shade = "white", level = 0, xlim = c(0.5,1.6), refline = 2)<br>
<br>
The xlim argument is used to fix the x-axis range, while putting the refline outside the visible region (simply a trick, I couldn't find an argument to determine the refline's color). You may also change the y-axis range, e.g.,<br>
<br>
funnel(funnel_all, back = "white", shade = "white", level = 0, xlim = c(0.5,1.6), refline = 2, ylim = c(0.06, 0.105))<br>
<br>
if you think this makes sense. With respect to the inverted axis, see Michael's post.<br>
<br>
Best,<br>
Gerta<br>
<br>
<br>
<br>
UNIVERSITÄTSKLINIKUM FREIBURG<br>
Institute for Medical Biometry and Statistics<br>
<br>
Dr. Gerta Rücker<br>
Guest Scientist<br>
<br>
Stefan-Meier-Straße 26 · 79104 Freiburg<br>
<a href="mailto:gerta.ruecker@uniklinik-freiburg.de" target="_blank">gerta.ruecker@uniklinik-freiburg.de</a><br>
<br>
<a href="https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker" rel="noreferrer" target="_blank">https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker</a><br>
<br>
<br>
-----Ursprüngliche Nachricht-----<br>
Von: R-sig-meta-analysis <<a href="mailto:r-sig-meta-analysis-bounces@r-project.org" target="_blank">r-sig-meta-analysis-bounces@r-project.org</a>> Im Auftrag von Gabriel Cotlier via R-sig-meta-analysis<br>
Gesendet: Donnerstag, 20. Juli 2023 16:28<br>
An: Michael Dewey <<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>><br>
Cc: Gabriel Cotlier <<a href="mailto:gabiklm01@gmail.com" target="_blank">gabiklm01@gmail.com</a>>; R Special Interest Group for Meta-Analysis <<a href="mailto:r-sig-meta-analysis@r-project.org" target="_blank">r-sig-meta-analysis@r-project.org</a>><br>
Betreff: Re: [R-meta] question on scatter plot of estimates (Fisher's Z) against the standard error<br>
<br>
Dear Michael,<br>
Here is the code below:<br>
Thanks a lot.<br>
Kind regards,<br>
Gabriel<br>
<br>
## Transformation of Pearson's Product-moment correlation coefficient (r)<br>
to Fisher's Z<br>
dat <-escalc(measure = "ZCOR",<br>
ri = ri,<br>
ni = ni,<br>
data = dat)<br>
<br>
##<br>
funnel_all <- <a href="http://rma.mv" rel="noreferrer" target="_blank">rma.mv</a>(yi,<br>
vi,<br>
# mods = ~ Type,<br>
random = ~ 1 | Article / Sample_ID,<br>
data=dat)<br>
<br>
funnel_all<br>
## funnel plot form metafor<br>
funnel(funnel_all)<br>
<br>
## Variance : from general model extracted<br>
vi_data <-funnel_all$vi<br>
<br>
## Estimates : from general model extracted estimates<br>
yi_data <-funnel_all$yi[1:150]<br>
<br>
## calculate standard error SE: square root of the variance<br>
SE<- sqrt(vi_data)<br>
<br>
# estimates<br>
E <-funnel_all$yi<br>
<br>
## construct data frame<br>
df <- data.frame (Estimates = c(E), Standsrd_Error = c(SE))<br>
View(df)<br>
library(ggplot2)<br>
## Scatter plot<br>
scaleFUN <- function(x) sprintf("%.2f", x)<br>
p<- ggplot(df, aes(x=Estimates, y=Standsrd_Error)) +<br>
geom_point(aes(size = Estimates), alpha=0.7, color="#2568E6")+<br>
scale_size_area() +<br>
labs(x = "Fisher's z",<br>
y = "Standard Error (SE)")+<br>
theme(plot.title = element_text(hjust = 0.5))+<br>
# theme(plot.margin = unit(2 ,8, 8, 2), "cm"))+<br>
scale_y_continuous(n.breaks = 12,labels=scaleFUN)+<br>
scale_x_continuous(n.breaks = 12,labels=scaleFUN)+<br>
geom_vline(xintercept = 0)+<br>
theme(axis.text.y = element_text(size = 15))+<br>
theme(axis.text.x = element_text(size = 15))+<br>
theme(axis.title.y = element_text(size = 15))+<br>
theme(axis.title.x = element_text(size = 15))+<br>
ggtitle( "Fisher's z vs. Standard Error")+<br>
theme(plot.title = element_text(size = 17, face = "bold"))+<br>
theme(legend.text = element_text(size = 15))<br>
p<br>
png(filename = "myplot.png", width = 28, height = 18 ,units = "cm" , res<br>
=100 )<br>
print(p)<br>
dev.off()<br>
<br>
<br>
<br>
On Thu, Jul 20, 2023 at 4:51 PM Michael Dewey <<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>><br>
wrote:<br>
<br>
> Dear Gabriel<br>
><br>
> Comments in-line<br>
><br>
> On 20/07/2023 05:55, Gabriel Cotlier wrote:<br>
> > Dear Michael,<br>
> ><br>
> > I think you are completely right, in the fact, the plot I am producing<br>
> > is indeed valid for the purpose for which I want to use it, meaning it<br>
> > is representative of the relationship I want to show. Therefore, I<br>
> > assume that the plot I am getting, is supposed to be sufficient.<br>
> ><br>
> > However, I receive from the function metafore:: funnel (model), for a<br>
> > model without modierators, a very nice representation of the scarring of<br>
> > the observed outcomes or the estimates (x axis), as a function of the SE<br>
> > (e.i., square root of the sampling variance, SE assumef to have a pseudo<br>
> > confidence interval region drawn around each of its values). While, when<br>
> > I plot by myself<br>
> > x = observed outcomes<br>
> > y = square root of the sampling variance,<br>
> ><br>
> > Then the plot shows that:<br>
> > a. the scattering of the points appears upside down with respect to the<br>
> > output of the function metafore:: funnel (model),<br>
><br>
> I have already answered that one in a previous post. It is just the<br>
> convention<br>
><br>
> > b. the scale of the y axis, instead of having a defined top at zero and<br>
> > from there values are represented downwards, the scale is different.<br>
> ><br>
><br>
> Without your code it is hard to tell but I suspect you are not plotting<br>
> what you think you are. Are you plotting the inverse of the se?<br>
><br>
> Michael<br>
><br>
> ><br>
> > Anyways, I started thinking that in any case, such a difference in the<br>
> > plot I am doing by myself is not necessarily wrong, but is just a<br>
> > different way of representing the data. Just the scattering of the<br>
> > points in one case looks like the upside down scattering of the other.<br>
> > And I assume this is because maybe the function metafore::funnel()<br>
> > applies some operation on the square root of the mean (y axis) that I<br>
> > presume is the calculation of the aforementioned pseudo confidence<br>
> > interval for each value, but I am not sure.<br>
> ><br>
> > Thanks a lot for your response.<br>
> > Kind regards,<br>
> > Gabriel<br>
> ><br>
> > On Wed, Jul 19, 2023 at 7:20 PM Michael Dewey <<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a><br>
> > <mailto:<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>>> wrote:<br>
> ><br>
> > I am sorry Gabriel but I do not understand why the plot you say you<br>
> > produced fails to do what you say you want.<br>
> ><br>
> > Michael<br>
> ><br>
> > On 19/07/2023 10:59, Gabriel Cotlier wrote:<br>
> > > Hello Michael,<br>
> > > Thank you very much for your response.<br>
> > > I just would like to show that the of data set I have has high<br>
> > > uncertainty given that no possible pattern is observable or<br>
> > detectable<br>
> > > and no order is possible to visulize in the scattering,<br>
> > > I thought that a plot with x axis = fisher's z observed<br>
> > > outcomes (estimates) and y axis = standard error or any<br>
> > other measure of<br>
> > > uncertainty could at least visually demostrate that assumption.<br>
> > > If such a lack of pattern or high uncertainty in the data set can<br>
> > also<br>
> > > be demonstrated numerically, even better.<br>
> > > Kind regards,<br>
> > > Gabriel<br>
> > ><br>
> > > On Wed, Jul 19, 2023 at 12:29 PM Michael Dewey<br>
> > <<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a> <mailto:<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>><br>
> > > <mailto:<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a><br>
> > <mailto:<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>>>> wrote:<br>
> > ><br>
> > > Dear Gabriel<br>
> > ><br>
> > > I am not realy sure what you are trying to do but one point<br>
> which<br>
> > > occurs<br>
> > > to me is that forest plots are conventional plotted with small<br>
> > > values of<br>
> > > standard error at the top.<br>
> > ><br>
> > > Michael<br>
> > ><br>
> > > On 19/07/2023 06:07, Gabriel Cotlier via R-sig-meta-analysis<br>
> > wrote:<br>
> > > > Dear all,<br>
> > > ><br>
> > > > I have already posted this question with no response.<br>
> > > > Maybe this time I am luckier and someone with more<br>
> > knowledge than<br>
> > > me in the<br>
> > > > Metafor package can answer me.<br>
> > > ><br>
> > > > In a nutshell, what I would like is to be able to produce a<br>
> > > scatter plot of<br>
> > > > the observed oucomes or the estimates, in my case Fisher's<br>
> > z for<br>
> > > the x axis<br>
> > > > and the standard error in the y axis, with the standard<br>
> error<br>
> > > (SE) the<br>
> > > > same as it appears when running the funnel() function for a<br>
> > > funnel plot<br>
> > > > with the model (without moderators) as the input argument.<br>
> > > Actually, it is<br>
> > > > a funnel plot without the background of the funnel<br>
> > distribution<br>
> > > but just<br>
> > > > the scatter of points, that is suppressing the funnel<br>
> > distribution on<br>
> > > > the background.<br>
> > > ><br>
> > > > I tried to do so in agreement with the definition of SE<br>
> > used for<br>
> > > the funnel<br>
> > > > plot in the package Vignette published at Journal of<br>
> > Scientific<br>
> > > software in<br>
> > > > page 26:<br>
> > > ><br>
> > > > "*For models without moderators, the figure shows the<br>
> observed<br>
> > > outcomes on<br>
> > > > the horizontal axis against their corresponding standard<br>
> > errors<br>
> > > (i.e., the<br>
> > > > square root of the sampling variances) on the vertical<br>
> axis. A<br>
> > > vertical<br>
> > > > line indicates the **estimate based on the model. A pseudo<br>
> > confidence<br>
> > > > interval region is drawn around this value with bounds<br>
> > equal to<br>
> > > ±1.96 · SE,<br>
> > > > where SE is the standard error value from the vertical<br>
> axis.*"<br>
> > > ><br>
> > > ><br>
> > > > I tried to reproduce the vertical axis (y) using the<br>
> > square root<br>
> > > of the<br>
> > > > sampling variable, but the result was an upside down<br>
> > scaling of the<br>
> > > > observed outcomes or estimates on a different y scale for<br>
> > the x<br>
> > > ticks. The<br>
> > > > plot seems to have similarities with the funnel plot from<br>
> the<br>
> > > funnel()<br>
> > > > function, but it is not exactly the same without the<br>
> > background<br>
> > > of the<br>
> > > > funnel distribution graphic. Maybe the problem could be<br>
> > that in the<br>
> > > > funnel() function, contrary to my simple attempt to<br>
> imitate it<br>
> > > with the<br>
> > > > square root of the sampling variable, the pseudo confidence<br>
> > > interval is<br>
> > > > estimated for each value? Could this be the reason?<br>
> > > ><br>
> > > ><br>
> > > > If so, how could I reproduce the funnel () function plot<br>
> > without<br>
> > > the funnel<br>
> > > > distribution graphic in the background and just the<br>
> > scattering of the<br>
> > > > points using the same pseudo-confidence interval?<br>
> > > ><br>
> > > ><br>
> > > > Thanks a lot for your help and assistance.<br>
> > > ><br>
> > > > Kind regards,<br>
> > > ><br>
> > > > Gabriel<br>
> > > ><br>
> > > > [[alternative HTML version deleted]]<br>
> > > ><br>
> > > > _______________________________________________<br>
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> > > --<br>
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> ><br>
> > --<br>
> > Michael<br>
> > <a href="http://www.dewey.myzen.co.uk/home.html" rel="noreferrer" target="_blank">http://www.dewey.myzen.co.uk/home.html</a><br>
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><br>
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> Michael<br>
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