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As soon as such correlations become high, Fisher-transforming the correlations will (visually) pronounce differences between the pairs better than leaving the correlations uncorrected. … I can understand the motivation behind including the matrices in this way in a paper, but is it really correct to report Fisher-transformed correlation matrices in this case? …

I have independent correlation coefficient measures for each subject. I would like to test whether the correlation coefficient of the group is significantly different from 0. … My understanding is that the Fisher's transform is used because the r's are not normally distributed. …

Wikipedia has a Fisher transform of the Spearman rank correlation to an approximate z-score. Perhaps that z-score is the difference from null hypothesis (rank correlation 0)? … use the Fisher transform to get the 95% confidence interval? …

I am not sure I understand the jump from cross-correlation to Fisher's Z-transform: isn't Fisher's transform based on the Pearson correlation coefficient? … What does it mean to apply Fisher's Z-transform to a cross-correlation? …

I follow the standard prescription: apply the Fisher z-transformation to my $\rho_i$ ($z_i$ are the z-transformed correlations) perform the mean $\bar{z}$ of the $z_i$ and then apply the inverse transformation … I went through the literature of the Fisher transform but I cannot find any clue on this specific point. …

In https://www.statisticshowto.com/fisher-z/, it is stated that “But it’s probably most commonly be used to test the significance of the difference between two correlation coefficients, r1 and r2 from … Or I do r1-r2 and apply the transform on this output? …

$\rho$ between these sets and a 95% confidence interval with a Fisher transform, as follows: r, p = st.pearsonr(x,y) r_z = np.arctanh(r) se = 1/np.sqrt(x.size-3) alpha = 0.05 z = st.norm.ppf(1-alpha … Given that I know the Fisher transform is an asymptotic approximation for large $n$, is there a better method than above for calculating the confidence interval I want? …

In the introduction, there is a table of before/after treatment values called cd4, and the correlation $\hat\theta$ is given as 0.723. … When I key in the data and compute a confidence interval by hand in R, either using the Fisher transform or the T distribution, I don't get that interval. …

I read that the correlation coefficients have to be converted by using a fisher-transformation when one wants to test their significance (like here When is Fisher's z-transform appropriate? ). … https://blogs.sas.com/content/iml/2017/09/20/fishers-transformation-correlation.html Ok, so what? What does it mean that it is bounded? When there are no bounds, then "bounds" would be +/- oo. …

I am currently working with panel data, namely its correlations. The data is three-dimensional. I am looking for a way to aggregate the correlation coefficients. … I found a model which uses the Fisher transformation to do this as shown as follows: Let $i$ be an invidual (20), consisting of 5 buckets $b$, each containing its own time-series, $j$, $n_j = 3000$ For …

I’m running a multilevel model in which the outcome variable is a Fisher Z-transformed correlation coefficient. … Edit: As per @nick-cox's comment, I've pasted a few histograms below indicating the distribution of the original correlations and of the Fisher-transformed correlations. …

I have 100 50x50 correlation matrices, which I have all Fisher z-transformed. I understood that this results in the all the entries of one matrix being approximately normally distributed. … Does the Fisher z-transformation implies that? …

I have implemented a mixed model to assess relationships between the observations using Fisher-transformed correlation coefficients. … I then perform a Fisher-transform on the 9 resulting correlation coefficients to get 9 z-scores. Then it's a simple matter of testing whether or not those z-scores differ from 0. …

Let $z_A$ = the Fisher transform of the observed correlation of set $A$, $z_B$ = the Fisher transform of the observed correlation of set $B$. … For $i = 1,\dots,n$, let $y_{A_i} = nz_A- (n - 1)z_{A'i}$, where $z_{A'i}$ is the Fisher transform of set $A$ of the one-left-out correlation obtained by deleting $(x_i,y_i)$, re-ranking, and re-computing …

I want to test a sample correlation $r$ for significance ($n=16$), using p-values, in Python. As I have understood from this question, I can achieve that by using Fisher's z-transform. … Is there a Python module, which allows easy use of Fisher's z-transform? I have not been able to find the functionality in SciPy or Statsmodels. …