All Discussions Tagged 'Variables' - AnalyticBridge2020-07-04T19:41:47Zhttps://www.analyticbridge.datasciencecentral.com/forum/topic/listForTag?tag=Variables&feed=yes&xn_auth=noHow to Define ordinal variables in SAS?tag:www.analyticbridge.datasciencecentral.com,2016-02-12:2004291:Topic:3409452016-02-12T16:10:32.674ZRaghu Chittarihttps://www.analyticbridge.datasciencecentral.com/profile/RaghuChittari
<p>Hello All,</p>
<p>I need help in defining ordinal variables in SAS. I have age buckets given for each person. Say 26 Years-30 years as bucket 1, 31 years to 35 years as bucket 2, 36 years to 40 years as bucket 3, and so on. If I include these bucket numbers in the model, then SAS considers it as continuous numeric variable. I want to make SAS understand that these buckets are ordinal. I did not get any useful information regarding this online. </p>
<p>Can you please help me…</p>
<p>Hello All,</p>
<p>I need help in defining ordinal variables in SAS. I have age buckets given for each person. Say 26 Years-30 years as bucket 1, 31 years to 35 years as bucket 2, 36 years to 40 years as bucket 3, and so on. If I include these bucket numbers in the model, then SAS considers it as continuous numeric variable. I want to make SAS understand that these buckets are ordinal. I did not get any useful information regarding this online. </p>
<p>Can you please help me out.</p>
<p>Thanks a lot in advance :)</p>
<p></p>
<p>Regards,</p>
<p>Raghu</p> Understanding the Kalman Filter Application in Economic Time Series Datatag:www.analyticbridge.datasciencecentral.com,2013-04-25:2004291:Topic:2430742013-04-25T13:37:53.300ZArunhttps://www.analyticbridge.datasciencecentral.com/profile/Arun
<div class="discussion"><div class="description"><div class="xg_user_generated"><p>The Kalman filter has been extensively used in Science for various applications, from detecting missile targets to just any changing scenario that can be learned.</p>
<p>I'm trying to understand how Kalman Filter can be applied on Time Series data with Exogenous variables - in a nutshell, trying to replicate PROC UCM in excel.</p>
<p>State-space equation :</p>
<p><img alt="Kalman - equation 1" border="p" height="23" src="http://bilgin.esme.org/Portals/0/images/kalman/equation1.gif" width="215"></img></p>
<p><img alt="Kalman - equation 2" border="0" height="30" src="http://bilgin.esme.org/Portals/0/images/kalman/equation2.gif" width="115"></img></p>
<p>To those…</p>
</div>
</div>
</div>
<div class="discussion"><div class="description"><div class="xg_user_generated"><p>The Kalman filter has been extensively used in Science for various applications, from detecting missile targets to just any changing scenario that can be learned.</p>
<p>I'm trying to understand how Kalman Filter can be applied on Time Series data with Exogenous variables - in a nutshell, trying to replicate PROC UCM in excel.</p>
<p>State-space equation :</p>
<p><img alt="Kalman - equation 1" src="http://bilgin.esme.org/Portals/0/images/kalman/equation1.gif" border="p" height="23" width="215"/></p>
<p><img alt="Kalman - equation 2" src="http://bilgin.esme.org/Portals/0/images/kalman/equation2.gif" border="0" height="30" width="115"/></p>
<p>To those familiar with the Kalman filter, it essentially consists of the following two steps,</p>
<p>Predict:</p>
<p><img alt="Kalman Filter - Time Update Equations" src="http://bilgin.esme.org/Portals/0/images/kalman/time_update_equations.gif" style="width: 177px; height: 86px; border-width: 0px; border-style: solid;"/></p>
<p>Update:</p>
<p><img alt="Kalman Filter - Measurement Update Equations" src="http://bilgin.esme.org/Portals/0/images/kalman/measurement_update_equations.gif" style="width: 233px; height: 142px; border-width: 0px; border-style: solid;"/></p>
<p>Most of the text on Kalman only introduce univariate analysis, with no exogenous variables. And most applications in control engg seem to suit that as well.</p>
<p>What I'm stuck figuring is -</p>
<p>1. How can I update the H matrix with every observation? Pretty much, MMSE or ML can help me do this, but I'm just unable to do this with just one observation! The problem of recursive estimation with just one observation if I could say...</p>
<p>2. How can I bring in the estimation of betas of other exogenous variables that also affect the Y variable, so, I'm going to be understating the latent state variable to be just a constant base or linear trend.</p>
<p>Any help would be greatly appreciated, and if you have some good docs/sites that explain this better for the econometrician, please do pass it on.</p>
<p>Thanks,</p>
<p>Arun</p>
</div>
</div>
</div> Discriminant Analysis on Categorical Variablestag:www.analyticbridge.datasciencecentral.com,2009-10-26:2004291:Topic:562332009-10-26T10:27:40.888ZArunhttps://www.analyticbridge.datasciencecentral.com/profile/Arun
I have a set of Independent Variables - both Categorical Variables and Continuous Variables. There is the predictor variable which have certain classes say C1 to Cn. The aim is to predict the category membership!<br />
<br />
I'm facing two issues. Any discriminant procedure requires only continuous variables for prediciting. And second, logistic regression which can be used produces probability values of category membership, which does not equivalently specify the inter-class variance using distance…
I have a set of Independent Variables - both Categorical Variables and Continuous Variables. There is the predictor variable which have certain classes say C1 to Cn. The aim is to predict the category membership!<br />
<br />
I'm facing two issues. Any discriminant procedure requires only continuous variables for prediciting. And second, logistic regression which can be used produces probability values of category membership, which does not equivalently specify the inter-class variance using distance measures like a Canonical Discriminant Analysis does using %plotit macro.<br />
<br />
Hence, I've got two questions.<br />
1. If I've got mixed variables - both Continuous & Catergorical, can I still predict membership of category in the predictor variable? If yes, how?<br />
2. If the answer to the above is to use Logistic Regression or Genmod/Catmod, can I still obtain a plot of the various observations that are governed by the category in a distance measure plot to find out the between category variance/distance and hence understand visually what is the scenario of the categories.<br />
<br />
Also, I'm not able to plot using %plotit due to the high no. of observations I've got (1.5 Mi). Do I need to consider a downscaling to bring it down to a lesser no? Or can I plot a contour to know the idea of the area coverage?