<font><span style="font-family:arial,helvetica,sans-serif">Hi Dehui,<br> <br>What about my previous suggestion to use dummy variable that indicates time periods (fixed values 0, 1,2,3...N) for N time periods? I am new to time series analysis, so please let me know if the below makes sense.<br>
<br>So you could use your data (Y) as one time series, and the dummy time variables as the second time series (T): Y = bT + c. Resulting slope, offset and R2 between r.series and r.regression.series are close to identical. Differenc<font>es are very smal<font>l (a f<font>actor <font>10¹<font>⁰</font> smaller then the actual slope or <font>off<font>set value</font></font></font>) </font>so</font> </font>I<font> assume this <font>is due to some <font>differences in rounding (@</font></font></font></span></font><font><span style="font-family:arial,helvetica,sans-serif"><font><font><font>MarkusN<font>, do you have an idea?)</font></font></font><br>
<br></font>Concrete, if you have a time series of let's say annual rainfall over 10 years. You create 10 layers using r.mapcalc, with all cells of the first layer = 0, all cells in the second layer = 1... all cells in the 10th layer = 9. Now you have two time series you can use in r.regression.series.<br>
<br>There is the possible problem of auto-correlation, so the t and f values calculated with r.regression.series should be considered with care. As said, I am new to time series analysis, so will need to study more how to deal with this. Not sure that is going to be easy using GRASS. <br>
</span></font><br>Cheers,<br><br>Paulo<br><br><div class="gmail_extra"><br><br><div class="gmail_quote">On Thu, Nov 8, 2012 at 2:21 PM, 王德辉 <span dir="ltr"><<a href="mailto:dehuiw@gig.ac.cn" target="_blank">dehuiw@gig.ac.cn</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><p>r.regression.series is a module to calculate linear regression parameters between two time series. I only want to calculate parameters in one time series, how to use this model in one time serie condition?</p>
<p>thanks!</p><p>dehui<br></p><blockquote style="padding-left:5px;margin-right:0px;margin-left:5px;border-left-color:rgb(182,182,182);border-left-width:2px;border-left-style:solid" name="replyContent">-----原始邮件-----<br>
<b>发件人:</b> "Paulo van Breugel" <<a href="mailto:p.vanbreugel@gmail.com" target="_blank">p.vanbreugel@gmail.com</a>><br>
<b>发送时间:</b> 2012年11月8日 星期四<br>
<b>收件人:</b> "Moritz Lennert" <<a href="mailto:mlennert@club.worldonline.be" target="_blank">mlennert@club.worldonline.be</a>><br>
<b>抄送:</b> "GRASS users email list" <<a href="mailto:grass-user@lists.osgeo.org" target="_blank">grass-user@lists.osgeo.org</a>><br>
<b>主题:</b> Re: [GRASS-user] Calculate p value for regression slope in r.series<div><div class="h5"><br><br>Good suggestion, and coincidentally I was just looking at it. Am I correct to assume that to mimic the calculation of slope etc in r.series, I would need to create a second time series of 'dummy' maps with values 0 for first time period, 1 for second time period, etc.? I did this in R and it gave the same slope and offset as r.series. <br>
<br>Just wondering, if it is implemented in r.regression.series, would it be very difficult to port to r.series? Or do the two things function different internally?<br><br>Cheers,<br><br>Paulo<br><br><div class="gmail_extra">
<br><br><div class="gmail_quote">On Thu, Nov 8, 2012 at 10:55 AM, Moritz Lennert <span dir="ltr"><<a href="mailto:mlennert@club.worldonline.be" target="_blank">mlennert@club.worldonline.be</a>></span> wrote:<br><blockquote style="margin:0px 0px 0px 0.8ex;padding-left:1ex;border-left-color:rgb(204,204,204);border-left-width:1px;border-left-style:solid" class="gmail_quote">
On 06/11/12 09:55, Paulo van Breugel wrote:<br>
<blockquote style="margin:0px 0px 0px 0.8ex;padding-left:1ex;border-left-color:rgb(204,204,204);border-left-width:1px;border-left-style:solid" class="gmail_quote">
Hi,<br>
<br>
I am using r.series to calculate linear regression slope, offset and<br>
coefficient of determination. But any idea how I can get the standard<br>
deviation t-value and p-value of the slope?<br>
</blockquote>
<br>
Try MarkusN's r.regression.series in the addons for grass7. It allows the calculation of t and f values. IIRC, p-value can be calculated from t-value.<span><font color="#888888"><br>
<br>
Moritz<br>
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