<div dir="ltr"><div><div>What do you mean by "normalizing" ?<br><br></div>[a] transforming to a normal (Gaussian) distribution.<br></div>[b] transforming to unit variance<br><div><div>[c] ???<br></div><div>===<br></div><div>Using the raw data:<br><br></div><div>[1] Using the covariance matrix emphasizes variance.<br>[2] Using the correlation matrix emphasizes patterns.<br><br></div><div>Correlations are 'normalized' by the product of the standard deviations.<br><pre> cor = SUM [(X(t)-Xave)*(Y(t)-Yave)}]/(Xstd*Ystd)<br><br>====<br></pre><pre>I'd suggest you experiment. <br></pre><pre>Incidentally, as noted in the 'eofunc' documentation, <br>the input data array need not be anomalies. NCL's function <br>does that 'under-the-hood'<br></pre><pre>Good luck<br></pre><br></div></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Dec 8, 2016 at 9:28 PM, <span dir="ltr"><<a href="mailto:Ziguang.Li@csiro.au" target="_blank">Ziguang.Li@csiro.au</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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<p class="MsoNormal"><span lang="EN-US">Hi there,<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-US">I have some uncertainty about using eofunc to do empirical orthogonal function analysis (EOF). Following the online instruction, I should set attribution of optEOF, jopt, to assign correlation matrix or covariance matrix
for calculating EOFs. I want to use normalized data as input, should I need to pre-process raw data as normalized values then set optEOF@jpopt=1 before perform eofunc? Or just use anoamlies as input and set optEOF@jopt=1 is enough and this function will automatically
normalize input data?<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-US">Looking forward to your reply.<u></u><u></u></span></p>
<p class="MsoNormal"><span lang="EN-US">Ziguang <u></u><u></u></span></p>
<p class="MsoNormal"><u></u> <u></u></p>
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