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仯伜xmeng at capitalbio.com
Address:18 Life Science Parkway,
Changping District, Beijing 102206, China
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, 仼s仾Y <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仭Gxmeng at capitalbio.com > Address:18 Life Science Parkway, > Changping District, Beijing 102206, China > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! R-project.org/posting-guide.html >