Re: [R] How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction

Fri, 19 Aug 2011 02:02:11 -0700 

Dear Mark,

Thank you very much for your kind advice.
Actually, I already performed penalized logistic regression by pentrace and lrm in package "rms".
The reason why I wanted to reduce dimensionality of those 9 variables was that these variables were not so important according to the subject matter knowledge and that I wanted to avoid events per variable problem. 

Your answer about dudi.mix$l1 helped me a lot.
I finally was able to perform penalized logistic regression for data consisting of 4 important variables and x18.dudi.mix$l1[, 1]. Thanks a lot again.
One more question, I investigated homals package too. I found it has "ndim" option. 

mydata is followings;

> head(x10homals.df)
age sex symptom HT DM IHD smoking hyperlipidemia Statin Response 1 62 M asymptomatic positive negative negative positive positive positive negative 2 82 M symptomatic positive negative negative negative positive positive negative 3 64 M asymptomatic negative positive negative negative positive positive negative 4 55 M symptomatic positive positive positive negative positive positive negative 5 67 M symptomatic positive negative negative negative negative positive negative 6 79 M asymptomatic positive positive negative negative positive positive negative
age is continuous variable, and Response should not be active for computation, so, ...
x10.homals4 <- homals(x10homals.df, active = c(rep(TRUE, 9), FALSE), level=c("numerical", rep("nominal", 9)), ndim=4)
I did it with ndim from 2 to 9, compared Classification rate of Response by predict(x10.homals). 

> p.x10.homals4

Classification rate:
         Variable Cl. Rate %Cl. Rate
1             age   0.4712     47.12
2             sex   0.9808     98.08
3         symptom   0.8269     82.69
4              HT   0.9135     91.35
5              DM   0.8558     85.58
6             IHD   0.8750     87.50
7         smoking   0.9423     94.23
8  hyperlipidemia   0.9519     95.19
9          Statin   0.8942     89.42
10       Response   0.6154     61.54
This is the best for classification of Response, so, I selected ndim=4. Then, I found objscores. 

> head(x10.homals4$objscores)
            D1           D2           D3          D4
1 -0.002395321 -0.034032230 -0.008140378  0.02369123
2  0.036788626 -0.010308707  0.005725984 -0.02751958
3  0.014363031  0.049594466 -0.025627467  0.06254055
4  0.083092285  0.065147519  0.045903394 -0.03751551
5 -0.013692504  0.005106661 -0.007656776 -0.04107009
6  0.002320747  0.024375393 -0.017785415 -0.01752556
I used x10.homals4$objscores[, 1] as a predictor for logistic regression as in the same way as PC1 in PCA. 

Am I going the right way?

Thanks a lot for your help in advance.

Best regards

--
Kohkichi Hosoda


(11/08/19 4:21), Mark Difford wrote:

On Aug 18, 2011 khosoda wrote:


I'm trying to do model reduction for logistic regression.


Hi Kohkichi,

My general advice to you would be to do this by fitting a penalized logistic
model (see lrm in package rms and glmnet in package glmnet; there are
several others).

Other points are that the amount of variance explained by mixed PCA and MCA
are not comparable. Furthermore, homals() is a much better choice than MCA
because it handles different types of variables whereas MCA is for
categorical variables.

On the more specific question of whether you should use dudi.mix$l1 or
dudi.mix$li, it doesn't matter: the former is a scaled version of the
latter. Same for dudi.acm. To see this do the following:

##
plot(x18.dudi.mix$li[, 1], x18.dudi.mix$l1[, 1])

Regards, Mark.

-----
Mark Difford (Ph.D.)
Research Associate
Botany Department
Nelson Mandela Metropolitan University
Port Elizabeth, South Africa
--
View this message in context: 
http://r.789695.n4.nabble.com/How-to-use-PC1-of-PCA-and-dim1-of-MCA-as-a-predictor-in-logistic-regression-model-for-data-reduction-tp3750251p3753437.html
Sent from the R help mailing list archive at Nabble.com.

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.




--
*************************************************
　神戸大学大学院医学研究科 脳神経外科学分野
　細田 弘吉
　
　〒650-0017　神戸市中央区楠町7丁目5-1
    Phone: 078-382-5966
    Fax  : 078-382-5979
    E-mail address
        Office: khos...@med.kobe-u.ac.jp
        Home  : khos...@venus.dti.ne.jp

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Reply via email to