[R] covariance analysis by using R
Wuming Gong
wuming.gong at gmail.com
Fri May 20 19:54:27 CEST 2005
You may fit the model using lm() directly - R will set up a coding for
qualitative predictor automatically (taking experiments as qualitative
predictor).
HTH
Wuming
On 5/18/05, ©sªY <xmeng at capitalbio.com> wrote:
> Hello sir:
> Here's a question on covariance analysis which needs your help.
> There're 3 experiments,and x refers to control while y refers to experimental result.
> The purpose is to compare the "y" values across the 3 experiments.
>
> experiment_1:
> x:0.1 0.2 0.3 0.4 0.5
> y:0.5 0.6 0.6 0.7 0.9
>
> experiment_2:
> x:1 2 3 4 5
> y:3 4 6.5 7.5 11
>
> experiment_3:
> x:10 20 30 40 50
> y:18 35 75 90 98
>
> Apparently,the control("x") isn't at the similar level so that we can't compare the "y" directly through ANOVA.
> We must normalize "y" via "x" in order to eliminate the influence of different level of "x".
> The method of normalize I can get is "covariance analysis",since "x" is the covariant of y.
>
> My question is:
> How to perform "covariance analysis" by using R?
> After this normalization,we can get the according "normalized y" of every "original y".
>
> All in all,the "normalized y" of every "original y" is what I want indeed.
>
>
> Thanks a lot!
>
> My best regards!
>
>
>
>
>
>
> ------------------------------
> *******************************************
> Xin Meng
> Capitalbio Corporation
> National Engineering Research Center
> for Beijing Biochip Technology
> Microarray and Bioinformatics Dept.
> Research Engineer
> Tel: +86-10-80715888/80726868-6364/6333
> Fax: +86-10-80726790
> Email¡Gxmeng at capitalbio.com
> Address:18 Life Science Parkway,
> Changping District, Beijing 102206, China
>
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