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

[Gabriel Cotlier]

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>
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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>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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