| 查看: 2989 | 回复: 14 | |||
| 当前只显示满足指定条件的回帖，点击这里查看本话题的所有回帖 | |||
[交流]
【求助/交流】虫虫们，有没有做PCA主成分分析的啊？ 已有12人参与
|
|||
| 各位虫虫们，有没有做PCA主成分分析的啊，一般是用什么软件呢，有可以免费下载的网址吗？ |
回复此楼» 猜你喜欢
湖北师范大学招调剂啦
已经有9人回复
-
邵阳学院食品与化学工程学院硕士调剂
已经有0人回复
-
化学工程及工业化学论文润色/翻译怎么收费?
已经有56人回复
-
天津商业大学 生物与医药课题组招生
已经有0人回复
-
生物化工学硕招收调剂
已经有0人回复
-
广东石油化工学院2026年硕士研究生需要多名生物，制药工程，食品专业的调剂生
已经有0人回复
-
化工申博
已经有3人回复
-
申博自荐
已经有2人回复
-
广西民族大学化学化工学院化学工程与技术学硕，材料化工专硕调剂名额充足！
已经有0人回复
» 本主题相关价值贴推荐，对您同样有帮助:
- 【讨论】电力行业的虫虫们来讨论一下 电力主辅分离利弊以及发展出路
已经有10人回复
- 有关主成份PCA、多元线性回归的计算问题？？
已经有8人回复
- 院校选择，急啊急（帮别人问的，虫虫们赶紧帮忙啊，啊啊）
已经有58人回复
- 求助PCA主成分分析遇到的问题！急！！
已经有9人回复
- 做DGGE的童鞋们，有人做过主成分分析（PCA）吗？
已经有22人回复
- 【求助/交流】做酵母的虫虫们帮忙看一下~
已经有9人回复
- 【求助】希望帮我简单解释下电子鼻分析方法——PCA和LDA
已经有7人回复
- 【讨论】用过液溴的虫虫们请进啊
已经有30人回复
4楼2010-08-25 10:54:10
2楼2010-08-25 09:18:13
reasonspare
木虫 (著名写手)
- 应助: 0 (幼儿园)
- 贵宾: 0.894
- 金币: 2137.1
- 红花: 11
- 帖子: 1909
- 在线: 72.5小时
- 虫号: 180928
- 注册: 2006-02-10
- 专业: All in one
★ ★ ★
小木虫(金币+0.5):给个红包，谢谢回帖交流
silicare(金币+2):专家级~ 2010-08-25 12:28:28
小木虫(金币+0.5):给个红包，谢谢回帖交流
silicare(金币+2):专家级~ 2010-08-25 12:28:28
|
最经典的是用SAS得princomp过程。另来SAS 得的主因子分析 Factor 因不错。 此外SPSS.也不错。其他的诸如 Metlab，也有现成的命令 princomp - Principal component analysis on data Syntax [COEFF,SCORE] = princomp(X) [COEFF,SCORE,latent] = princomp(X) [COEFF,SCORE,latent,tsquare] = princomp(X) [...] = princomp(X,'econ') Description COEFF = princomp(X) performs principal components analysis on the n-by-p data matrix X, and returns the principal component coefficients, also known as loadings. Rows of X correspond to observations, columns to variables. COEFF is a p-by-p matrix, each column containing coefficients for one principal component. The columns are in order of decreasing component variance. princomp centers X by subtracting off column means, but does not rescale the columns of X. To perform principal components analysis with standardized variables, that is, based on correlations, use princomp(zscore(X)). To perform principal components analysis directly on a covariance or correlation matrix, use pcacov. [COEFF,SCORE] = princomp(X) returns SCORE, the principal component scores; that is, the representation of X in the principal component space. Rows of SCORE correspond to observations, columns to components. [COEFF,SCORE,latent] = princomp(X) returns latent, a vector containing the eigenvalues of the covariance matrix of X. [COEFF,SCORE,latent,tsquare] = princomp(X) returns tsquare, which contains Hotelling's T2 statistic for each data point. The scores are the data formed by transforming the original data into the space of the principal components. The values of the vector latent are the variance of the columns of SCORE. Hotelling's T2 is a measure of the multivariate distance of each observation from the center of the data set. When n <= p, SCORE(:,n:p) and latent(n:p) are necessarily zero, and the columns of COEFF(:,n:p) define directions that are orthogonal to X. [...] = princomp(X,'econ') returns only the elements of latent that are not necessarily zero, and the corresponding columns of COEFF and SCORE, that is, when n <= p, only the first n-1. This can be significantly faster when p is much larger than n. Examples Compute principal components for the ingredients data in the Hald data set, and the variance accounted for by each component. load hald; [pc,score,latent,tsquare] = princomp(ingredients); pc,latent pc = 0.0678 -0.6460 0.5673 -0.5062 0.6785 -0.0200 -0.5440 -0.4933 -0.0290 0.7553 0.4036 -0.5156 -0.7309 -0.1085 -0.4684 -0.4844 latent = 517.7969 67.4964 12.4054 0.2372 The following command and plot show that two components account for 98% of the variance: cumsum(latent)./sum(latent) ans = 0.86597 0.97886 0.9996 1 biplot(pc(:,1:2),'Scores',score(:,1:2),'VarLabels',... {'X1' 'X2' 'X3' 'X4'})  |
» 本帖已获得的红花（最新10朵）
快乐的米粒3楼2010-08-25 09:59:53
sqhnsd
金虫 (正式写手)
- 应助: 1 (幼儿园)
- 金币: 515.3
- 散金: 100
- 帖子: 412
- 在线: 56.9小时
- 虫号: 1045397
- 注册: 2010-06-22
- 性别: GG
- 专业: 蛋白质组学
5楼2010-08-25 10:55:13
