[R-meta] question on scatter plot of estimates (Fisher's Z) against the standard error

Gabriel Cotlier g@b|k|m01 @end|ng |rom gm@||@com
Thu Jul 20 16:28:07 CEST 2023


Dear Michael,
Here is the code below:
Thanks a lot.
Kind regards,
Gabriel

## Transformation of Pearson's Product-moment correlation coefficient (r)
to Fisher's Z
dat <-escalc(measure = "ZCOR",
             ri = ri,
             ni = ni,
             data = dat)

##
funnel_all <- rma.mv(yi,
                     vi,
                     # mods = ~ Type,
                     random = ~ 1 | Article / Sample_ID,
                     data=dat)

funnel_all
## funnel plot form metafor
funnel(funnel_all)

## Variance : from general model extracted
vi_data <-funnel_all$vi

## Estimates : from general model extracted estimates
yi_data <-funnel_all$yi[1:150]

## calculate standard error SE: square root of the variance
SE<- sqrt(vi_data)

# estimates
E <-funnel_all$yi

## construct data frame
df <- data.frame (Estimates = c(E), Standsrd_Error = c(SE))
View(df)
library(ggplot2)
## Scatter plot
scaleFUN <- function(x) sprintf("%.2f", x)
p<- ggplot(df, aes(x=Estimates, y=Standsrd_Error)) +
  geom_point(aes(size = Estimates), alpha=0.7,  color="#2568E6")+
  scale_size_area() +
  labs(x = "Fisher's z",
       y = "Standard Error (SE)")+
  theme(plot.title = element_text(hjust = 0.5))+
 # theme(plot.margin = unit(2 ,8, 8, 2), "cm"))+
  scale_y_continuous(n.breaks = 12,labels=scaleFUN)+
  scale_x_continuous(n.breaks = 12,labels=scaleFUN)+
  geom_vline(xintercept = 0)+
  theme(axis.text.y = element_text(size = 15))+
  theme(axis.text.x = element_text(size = 15))+
  theme(axis.title.y = element_text(size = 15))+
  theme(axis.title.x = element_text(size = 15))+
  ggtitle( "Fisher's z vs. Standard Error")+
  theme(plot.title = element_text(size = 17, face = "bold"))+
  theme(legend.text = element_text(size = 15))
  p
png(filename = "myplot.png", width = 28,  height = 18 ,units = "cm" , res
=100 )
print(p)
dev.off()



On Thu, Jul 20, 2023 at 4:51 PM Michael Dewey <lists using dewey.myzen.co.uk>
wrote:

> Dear Gabriel
>
> Comments in-line
>
> On 20/07/2023 05:55, Gabriel Cotlier wrote:
> > Dear Michael,
> >
> > I think you are completely right, in the fact, the plot I am producing
> > is indeed valid for the purpose for which I want to use it, meaning it
> > is representative of the relationship I want to show. Therefore, I
> > assume that the plot I am getting, is supposed to be sufficient.
> >
> > However, I receive from the function metafore:: funnel (model), for a
> > model without modierators, a very nice representation of the scarring of
> > the observed outcomes or the estimates (x axis), as a function of the SE
> > (e.i., square root of the sampling variance, SE assumef to have a pseudo
> > confidence interval region drawn around each of its values). While, when
> > I plot by myself
> > x = observed outcomes
> > y = square root of the sampling variance,
> >
> > Then the plot shows that:
> > a.  the scattering of the points appears upside down with respect to the
> > output of the function metafore:: funnel (model),
>
> I have already answered that one in a previous post. It is just the
> convention
>
> > b. the scale of the y axis, instead of having a defined top at zero and
> > from there values are represented downwards, the scale is different.
> >
>
> Without your code it is hard to tell but I suspect you are not plotting
> what you think you are. Are you plotting the inverse of the se?
>
> Michael
>
> >
> > Anyways, I started thinking that in any case, such a difference in the
> > plot I am doing by myself is not necessarily wrong, but is just a
> > different way of representing the data. Just the scattering of the
> > points in one  case looks like the upside down scattering of the other.
> > And I assume this is because maybe the function metafore::funnel()
> > applies some operation on the square root of the mean (y axis) that I
> > presume is the calculation of the aforementioned pseudo confidence
> > interval for each value, but I am not sure.
> >
> > Thanks a lot for your response.
> > Kind regards,
> > Gabriel
> >
> > On Wed, Jul 19, 2023 at 7:20 PM Michael Dewey <lists using dewey.myzen.co.uk
> > <mailto:lists using dewey.myzen.co.uk>> wrote:
> >
> >     I am sorry Gabriel but I do not understand why the plot you say you
> >     produced fails to do what you say you want.
> >
> >     Michael
> >
> >     On 19/07/2023 10:59, Gabriel Cotlier wrote:
> >      > Hello Michael,
> >      > Thank you very much for your response.
> >      > I just would like to show that the of data set I have has high
> >      > uncertainty given that no possible pattern is observable or
> >     detectable
> >      > and no order is possible to visulize in the scattering,
> >      > I thought that a plot with x axis = fisher's z observed
> >      > outcomes (estimates) and y axis = standard error or any
> >     other measure of
> >      > uncertainty could at least visually demostrate that assumption.
> >      > If such a lack of pattern or high uncertainty in the data set can
> >     also
> >      > be demonstrated numerically, even better.
> >      > Kind regards,
> >      > Gabriel
> >      >
> >      > On Wed, Jul 19, 2023 at 12:29 PM Michael Dewey
> >     <lists using dewey.myzen.co.uk <mailto:lists using dewey.myzen.co.uk>
> >      > <mailto:lists using dewey.myzen.co.uk
> >     <mailto:lists using dewey.myzen.co.uk>>> wrote:
> >      >
> >      >     Dear Gabriel
> >      >
> >      >     I am not realy sure what you are trying to do but one point
> which
> >      >     occurs
> >      >     to me is that forest plots are conventional plotted with small
> >      >     values of
> >      >     standard error at the top.
> >      >
> >      >     Michael
> >      >
> >      >     On 19/07/2023 06:07, Gabriel Cotlier via R-sig-meta-analysis
> >     wrote:
> >      >      > Dear all,
> >      >      >
> >      >      > I have already posted this question with no response.
> >      >      > Maybe this time I am luckier and someone with more
> >     knowledge than
> >      >     me in the
> >      >      > Metafor package can answer me.
> >      >      >
> >      >      > In a nutshell, what I would like is to be able to produce a
> >      >     scatter plot of
> >      >      > the observed oucomes or the estimates, in my case Fisher's
> >     z for
> >      >     the x axis
> >      >      > and the standard error in the y axis, with the standard
> error
> >      >     (SE) the
> >      >      > same as it appears when running the funnel() function for a
> >      >     funnel plot
> >      >      > with the model (without moderators) as the input argument.
> >      >     Actually, it is
> >      >      > a funnel plot without the background of the funnel
> >     distribution
> >      >     but just
> >      >      > the scatter of points, that is suppressing the funnel
> >     distribution on
> >      >      > the background.
> >      >      >
> >      >      > I tried to do so in agreement with the definition of SE
> >     used for
> >      >     the funnel
> >      >      > plot in the package Vignette published at Journal of
> >     Scientific
> >      >     software in
> >      >      > page 26:
> >      >      >
> >      >      > "*For models without moderators, the figure shows the
> observed
> >      >     outcomes on
> >      >      > the horizontal axis against their corresponding standard
> >     errors
> >      >     (i.e., the
> >      >      > square root of the sampling variances) on the vertical
> axis. A
> >      >     vertical
> >      >      > line indicates the **estimate based on the model. A pseudo
> >     confidence
> >      >      > interval region is drawn around this value with bounds
> >     equal to
> >      >     ±1.96 · SE,
> >      >      > where SE is the standard error value from the vertical
> axis.*"
> >      >      >
> >      >      >
> >      >      > I tried to reproduce the vertical axis (y) using the
> >     square root
> >      >     of the
> >      >      > sampling variable, but the result was an upside down
> >     scaling of the
> >      >      > observed outcomes or estimates on a different y scale for
> >     the x
> >      >     ticks. The
> >      >      > plot seems to have similarities with the funnel plot from
> the
> >      >     funnel()
> >      >      > function, but it is not exactly the same without the
> >     background
> >      >     of the
> >      >      > funnel distribution graphic. Maybe the problem could be
> >     that in the
> >      >      > funnel() function, contrary to my simple attempt to
> imitate it
> >      >     with the
> >      >      > square root of the sampling variable, the pseudo confidence
> >      >     interval is
> >      >      > estimated for each value? Could this be the reason?
> >      >      >
> >      >      >
> >      >      > If so, how could I reproduce the funnel () function plot
> >     without
> >      >     the funnel
> >      >      > distribution graphic in the background and just the
> >     scattering of the
> >      >      > points using the same pseudo-confidence interval?
> >      >      >
> >      >      >
> >      >      > Thanks a lot for your help and assistance.
> >      >      >
> >      >      > Kind regards,
> >      >      >
> >      >      > Gabriel
> >      >      >
> >      >      >       [[alternative HTML version deleted]]
> >      >      >
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> >      > http://www.dewey.myzen.co.uk/home.html
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