Cluster analysis on a pivot table The link of the pivot table is hereThe increasing supremacy of JavaScript on both server side and client side seems a good news for those statistical workers who deal with data and model, and therefore always live in the darkness. They could eventually find a relatively easier way to show off their hard work on Web, the final destination of data. Here I show how to display the result of a cluster analysis on a web-based pivot table.Back-end: cluster analysisSAS has a FASTCLUS procedure, which implements a nearest centroid sorting algorithm and is similar to k-means. It has some time and space advantages ...
More SQL taste in SAS 9.4 Compared with SAS 9.3, the latest SAS 9.4 introduced a few new procedures for the BASE and STAT components: 7 new procedures for BASE 9.4 and 4 for STAT 12.3. 6 high-performance procedures (thanks to Dr. Wicklin's correction).New in BASE 9.4New in STAT 12.1New in STAT 12.3DELETEADAPTIVEREGHPGENSELECTDS2QUANTLIFEHPLOGISTICJSONQUANTSELECTHPLMIXEDPRESENVSTDRATEHPNLMODSTREAMHPREGFEDSQLHPSPLITAUTHLIBDS2 is a new SAS proprietary programming language that is appropriate for advanced data manipulation.It is exciting to see the emergence of DS2 and FEDSQL. According to SAS 9.4 DS2 Language Reference,DS2 is a SAS programming language that is appropriate for advanced data manipulationContrary to the thought I had last year, DS2 or PROC DS2 is not a complied language. It seems ...
Why Principle Component Analysis is always better than Factor Analysis? Principle Component AnalysisIf where and , and let , then where i the ith principal component and Factor Analysisif where , then and the common factors f are independent to .For the two methods, the proportion of variance of explained is called the community of . The summation of community is not equal to 1 for Factor Analysis, while it is always equal to 1 Principle Component Analysis. We can use PROC FACTOR and a simple SASHELP.CLASS dataset as example. The only difference between them is the priors option. In Factor Analysis, starting from one common factor, the commonality is greater ...
Use Google Trends and SAS to select movies to watch The newest success story about data science is Google search predicts box office with 94 percent accuracy. I am a frequent movie theater goer, and it will be great if we can implement Google's impressive research result.There are quite a few offering for this summer. Now I am considering five incoming movies.TitleDateThis is the EndWednesday, June 12World War ZFriday, June 21Man of SteelFriday, June 14Monsters UniversityFriday, June 21The InternshipFriday, June 7Google Trends reflects what keywords people are searching for, which is a reliable and free data source. Let's use SAS to do some scripting work to generate the URL query based on the get method.data ...
Create transition matrices by cohort approach and hazard rate approch ------------The cohort approach macro has a bug and I am working on it ------------------------------------Cohort approach is a widely-used method in creating transition matrices for evaluation of credit risk. In the example below, I made a macro to automate this process. Although the code is for one-year period transition matrices, scorecards for multiple-year period can simply derive from it. %macro cohort(filepath = );   options mlogic mprint;   data _tmp01;      infile "&filepath\data.txt" delimiter='09'x missover dsd firstobs=2 ;      informat id best12.; informat date mmddyy10.; informat rawrating $5.;      input id date rawrating;   run;   proc format;      value $rating       'NR   ' =   0      'AAA'   =   1      ...