所有由sxlion发布的文章

读书人偷书不算偷 from SFG2011

这是件发生在今年SAS全球用户大会上的真事,当然我是道听途说的。

在这次的SAS大会上,SAS公司开发员Sanjay Matange and Dan Heath把他们写的一本关于SGplot画图程序的草稿,放在大会的展示室中。后来有人想去看看,结果被告知被偷了。据说这是SAS大会第一次有人偷书的事。可见这本书对这位SASor有多么大的吸引力啊,都迫不及待的到直接偷了。 继续阅读读书人偷书不算偷 from SFG2011

用SAS实现不等比例扇形的南丁格尔玫瑰图

有朋友看到上一篇文章: 用SAS实现堆积式南丁格尔玫瑰图Nightingale Rose Diagram (上),然后就自己改造了一下,因为他的数据中并不是等比例的,因此对应的饼图并不是等比例的,不过由于对那段SAS代码理解不深,因此出现了错误。

后来我帮忙修正了下代码,照样也很好用。 继续阅读用SAS实现不等比例扇形的南丁格尔玫瑰图

SAS JMP9和IML/Studio3.2开始兼容R

SAS公司产品JMP9和IML/Studio3.2开始兼容R,  注意不是SAS系统。

2010.10.8日Rick Wicklin在他博客中提到在SAS公司的独立模块IML/Studio3.2(可以调用SAS IML模块和使用IMPLUS语言),用IMLPLUS语言(非SAS原系统语言,在IML模块语言上进行了扩展)也可以调用R语句,当然前提是你要两者都安装了。继IML/stuidio模块开始兼容R以后,2010.10.18日,SAS公司新推出的另一个工业界颇有建树的JMP9也可以调用R软件。 继续阅读SAS JMP9和IML/Studio3.2开始兼容R

Some discriminat methodes and SAS codes

NOTICE: The following text is one part of my published paper. Do not distribute it! All right reserved.

介绍线性判别方法、针对强共线性数据的两种降维判别方法及SAS实现代码。

1, linear discriminant analysis (LDA)
Because of its simplicity and robustness, LDA has been one of the most frequently used classification techniques since 1936.
LDA:
proc discrim data=ex1 testdata=ex2;
class g;
var x1-x10
run;

2, Combation of PCA and LDA(PCA+LDA), or PLS and PCA (PLS+LDA)
Principal component analysis (PCA) is the fundamental method used in chemometric and is based on vector algebra. The main purpose of this method is to reduce the dimensions of a data set with a large number of intercorrelated variables, whilst retaining as much of the information present in the original data as possible. A new set of orthogonal variables, principal components (PCs), describe the variance in data. Only first few of them can retain most of variation in describing the systematic information of all the original variables. Usually, a subset of limited PCs is used to explore the trends of samples with different treatments. Furthermore, when using these PCs as input variables, linear discriminant analysis (LDA) can greatly reduce multiple co-linearity among the variables of the original data. Therefore, the combination of PCA and LDA (PCA+LDA) was used here for the goal of classification. Principal component regression (PCR) is a multiple linear regression method for relating two sets of variables (PCs and response variables) with predictive purposes. PLS is an extension of PCR, which is applied to relate two sets of variables by a regression model. But in PLS, the principal components are more correlated with the response variables. This results in a more effective prediction of the response variable. In the same way, the PCs of PLS can be used in conjunction with LDA (PLS+LDA) to tackle classification problems. 继续阅读Some discriminat methodes and SAS codes