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SPSS canonical analysis

2012-11-16 05:58:37| 分类：生物统计 | 标签： |举报 |字号大中小订阅

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1. 按File→New→Syntax的顺序新建一个语句窗口。在语句窗口中输入下面的语句：

INCLUDE 'C:\Program Files\IBM\SPSS\Statistics\20\Samples\English\Canonical correlation.sps'.

CANCORR SET1=x1 x2 x3 /

SET2= x4 x5 x6 / .

2. click Run => all

结果：

Correlations for Set-1

x1 x2 x3

x1 1.0000 .8362 .8651

x2 .8362 1.0000 .7863

x3 .8651 .7863 1.0000

Correlations for Set-2

x4 x5 x6

x4 1.0000 .7403 .8086

x5 .7403 1.0000 .8538

x6 .8086 .8538 1.0000

Correlations Between Set-1 and Set-2

x4 x5 x6

x1 .8089 .8800 .8407

x2 .8897 .8213 .8458

x3 .7685 .7786 .7200

Canonical Correlations

1 .954

2 .346

3 .212

上面是提取出的两个典型相关系数的大小，可见第一典型相关系数为0.954，第二典型相关系数为0.346,第三典型相关系数为0.212

Test that remaining correlations are zero:

Wilk's Chi-SQ DF Sig.

1 .075 187.428 9.000 .000

2 .841 12.586 4.000 .013

3 .955 3.340 1.000 .068

上表为检验各典型相关系数有无统计学意义，可见第一典型相关系数有统计学意义（p-value: .000) ，而第二典型相关系数 (p-value=.013)。

Standardized Canonical Coefficients for Set-1

1 2 3

x1 -.460 2.065 -.920

x2 -.563 -1.563 -.865

x3 -.023 -.525 1.979

上面为各典型变量与变量组1中各变量间标准化的系数列表，由此我们可以写出典型变量的转换公式(标化的)为：

第一典型变量: v1=-0.460 * X1 -0.563 * x2 -0.023 * X3

第一典型变量 v2= 2.065 * X1 -1.563 * x2 -0.525 * X3

Raw Canonical Coefficients for Set-1

1 2 3

x1 -.027 .121 -.054

x2 -.401 -1.114 -.616

x3 -.006 -.147 .552

上面为各典型变量与变量组1中各变量间未标准化的系数列表

Standardized Canonical Coefficients for Set-2

1 2 3

x4 -.482 -1.459 .778

x5 -.430 1.269 1.412

x6 -.165 .201 -2.209

上面为各典型变量与变量组2中各变量间标准化的系数列表，由此我们可以写出典型变量的转换公式(标化的)为：

第一典型变量: v1= -0.482 * X4 - 0.430 * x5 - 0.165 * X6

第一典型变量 v2= -1.459 * X4 + 1.269 * x5 +0.201 * X6

Raw Canonical Coefficients for Set-2

1 2 3

x4 -.143 -.432 .230

x5 -.173 .510 .567

x6 -.161 .196 -2.158

上面为各典型变量与变量组2中各变量间未标准化的系数列表

Canonical Loadings for Set-1

1 2 3

x1 -.950 .304 .070

x2 -.965 -.249 -.077

x3 -.863 .032 .504

Cross Loadings for Set-1

1 2 3

x1 -.907 .105 .015

x2 -.921 -.086 -.016

x3 -.824 .011 .107

Canonical Loadings for Set-2

1 2 3

x4 -.933 -.358 .037

x5 -.927 .360 .102

x6 -.921 .104 -.374

Cross Loadings for Set-2

1 2 3

x4 -.890 -.124 .008

x5 -.885 .125 .022

x6 -.879 .036 -.079

Redundancy Analysis:

冗余度(Redundancy)分析结果，它列出各典型相关系数所能解释原变量变异的比例，可以用来辅助判断需要保留多少个典型相关系数。

Proportion of Variance of Set-1 Explained by Its Own Can. Var.

Prop Var

CV1-1 .860

CV1-2 .052

CV1-3 .088

首先输出的是第一组变量的变异可被自身的典型变量所解释的比例，可见第一典型变量解释了总变异的86.0％，而第二典型变量只能解释5.20％。

Proportion of Variance of Set-1 Explained by Opposite Can.Var.

Prop Var

CV2-1 .783

CV2-2 .006

CV2-3 .004

第一组变量的变异能被它们相对的典型变量（即第二组的典型变量）所解释的比例，例，对于第一典型变量来说第一组变量可以被第二组变量的第一典型变量解释78.3%。

Proportion of Variance of Set-2 Explained by Its Own Can. Var.

Prop Var

CV2-1 .860

CV2-2 .090

CV2-3 .051

第二组变量的变异可被自身的典型变量所解释的比例，可见第一典型变量解释了总变异的86.0％，而第二典型变量只能解释9.0％。

Proportion of Variance of Set-2 Explained by Opposite Can. Var.

Prop Var

CV1-1 .783

CV1-2 .011

CV1-3 .002

第二组变量的变异能被它们相对的典型变量（即第一组的典型变量）所解释的比例，例，对于第一典型变量来说第二组变量可以被第一组变量的第一典型变量解释78.3%。

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