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 at dewey.myzen.co.uk
<mailto:lists at 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
> Metafor package can answer me.
>
> In a nutshell, what I would like is to be able to produce a
> the observed oucomes or the estimates, in my case Fisher's z for
> and the standard error in the y axis, with the standard error
> same as it appears when running the funnel() function for a
> with the model (without moderators) as the input argument.
> a funnel plot without the background of the funnel distribution
> the scatter of points, that is suppressing the funnel
> the background.
>
> I tried to do so in agreement with the definition of SE used for
> plot in the package Vignette published at Journal of Scientific
> page 26:
>
> "*For models without moderators, the figure shows the observed
> the horizontal axis against their corresponding standard errors
> square root of the sampling variances) on the vertical axis. A
> line indicates the **estimate based on the model. A pseudo
> interval region is drawn around this value with bounds equal to
> where SE is the standard error value from the vertical axis.*"
>
>
> I tried to reproduce the vertical axis (y) using the square root
> sampling variable, but the result was an upside down scaling of
> observed outcomes or estimates on a different y scale for the x
> plot seems to have similarities with the funnel plot from the
> function, but it is not exactly the same without the background
> funnel distribution graphic. Maybe the problem could be that in
> funnel() function, contrary to my simple attempt to imitate it
> square root of the sampling variable, the pseudo confidence
> estimated for each value? Could this be the reason?
>
>
> If so, how could I reproduce the funnel () function plot without
> distribution graphic in the background and just the scattering of
> points using the same pseudo-confidence interval?
>
>
> Thanks a lot for your help and assistance.
>
> Kind regards,
>
> Gabriel
>
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>
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