<div dir="ltr">Thanks a lot, your answer was really helpful and I have another question: <div><br></div><div>Before I use regCoef function in NCL to calculate linear regression. Using this function gives other variables such as rcraw_npt, and rcraw_tval, so I was able to calculate significance at 95%, 90% and 99% confidence level. I've put that part of the code here:</div><div><br></div><div><div>do k=0,2999</div><div> </div><div> ab = regCoef(AI_fall(:,k),Nd_fall(:,k))</div><div> rcraw_npt=ab@nptxy-2</div><div> rcraw_tval=ab@tval</div><div> b = rcraw_tval</div><div> b = 0.5</div><div> rcraw_npt=where(rcraw_npt.lt.1,1,rcraw_npt)</div><div> rcraw_prob = (1 - betainc(rcraw_npt/(rcraw_npt+rcraw_tval^2),rcraw_npt/2.0,b) )</div><div> sig_fall(k)=where(rcraw_prob.lt.0.95,-999,rcraw_prob)</div><div> </div><div>end do</div><div><br></div><div><br></div><div>How can I calculate the significance at different confidence level here by using xBoot?</div><div><br></div><div>Best,</div><div>Ana </div><div><br></div><div><br></div><div><br></div></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Dec 14, 2016 at 2:45 PM, Dennis Shea <span dir="ltr"><<a href="mailto:shea@ucar.edu" target="_blank">shea@ucar.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><div><div>I think you will have to decide what is best.<br><br></div>[a]<br></div>Constrain the F,N,A triplets to be 'coupled'<br><br><pre> nBoot = 10000
xBoot = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/new.shtml" target="_blank"><strong>new</strong></a> (nBoot, typeof(F))
do ns=0,nBoot-1 ; generate multiple estimates
iw = <a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/generate_sample_indices.shtml" target="_blank"><strong>generate_sample_indices</strong></a>(N,<b><font color="red">1</font></b>)) ; indices with replacement
xBoot(ns) = (dF(iw)/dN(iw))*(dN(iw)/dA(iw)<wbr>)
end do<br><br></pre><pre>[b]<br></pre><pre>Unconstrained<br><br> <br> nBoot = 10000
xBoot = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/new.shtml" target="_blank"><strong>new</strong></a> (nBoot, typeof(F))
do ns=0,nBoot-1 ; generate multiple estimates
iwF = <a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/generate_sample_indices.shtml" target="_blank"><strong>generate_sample_indices</strong></a>(N,<b><font color="red">1</font></b>)) ; indices with replacement<br> iwN = <a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/generate_sample_indices.shtml" target="_blank"><strong>generate_sample_indices</strong></a>(N,<b><font color="red">1</font></b>)) <br> iwA = <a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/generate_sample_indices.shtml" target="_blank"><strong>generate_sample_indices</strong></a>(N,<b><font color="red">1</font></b>)) </pre><pre> xBoot(ns) = (dF(iwF)/dN(iwN))*(dN(iwN)/dA(<wbr>iwA))
end do<br></pre>[c]<br></div>See where your product fits.<br><br><pre> ia = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/dim_pqsort_n.shtml" target="_blank"><strong>dim_pqsort_n</strong></a>(xBoot, 2, 0) ; sort bootstrap means into ascending order
n025 = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/round.shtml" target="_blank"><strong>round</strong></a>(0.025*(nBoot-1),3) ; indices for sorted array
n500 = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/round.shtml" target="_blank"><strong>round</strong></a>(0.500*(nBoot-1),3)
n975 = <a href="http://www.ncl.ucar.edu/Document/Functions/Built-in/round.shtml" target="_blank"><strong>round</strong></a>(0.975*(nBoot-1),3)
xBoot_025= xBoot(n025) ; 2.5% level
xBoot_500= xBoot(n500) ; 50.0% level (median)
xBoot_975= xBoot(n975) ; 97.5% level
</pre><br></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">On Wed, Dec 14, 2016 at 3:12 PM, Anahita Amiri Farahani <span dir="ltr"><<a href="mailto:aamir003@ucr.edu" target="_blank">aamir003@ucr.edu</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5"><div dir="ltr">Dear all,<div><br></div><div>I have a product of two partial derivatives : dF/dN*dN/dA and for each of the variables (F, N, and A) I have data for 720 times. Each partial derivatives are calculated by linear regression. I was wondering how I can calculate the significant test for this product. All examples in NCL to estimate linear regression by this function: <strong style="margin:0px;padding:0px;text-decoration:none;font-family:verdana,sans-serif;font-size:13.3333px"><a href="http://www.ncl.ucar.edu/Document/Functions/Contributed/regline_stats.shtml" style="color:rgb(133,45,133);margin:0px;padding:0px;text-decoration:none;font-family:verdana,sans-serif;font-size:13.3333px" target="_blank">regline_stats</a><font color="#000000"><span style="background-color:rgb(255,231,198)"> </span></font></strong> give the regression coefficient for two variables.</div><div><br></div><div>Thanks,</div><div>Ana <br></div></div>
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