Dear Mark,
Thank you very much for your mail. This is what I really wanted!
I tried dudi.mix in ade4 package.
> ade4plaque.df <- x18.df[c("age", "sex", "symptom", "HT", "DM", "IHD",
"smoking", "DL", "Statin")]
> head(ade4plaque.df)
age sex symptom HT DM IHD smoking
hyperlipidemia Statin
1 62 M asymptomatic positive negative negative positive
positive positive
2 82 M symptomatic positive negative negative negative
positive positive
3 64 M asymptomatic negative positive negative negative
positive positive
4 55 M symptomatic positive positive positive negative
positive positive
5 67 M symptomatic positive negative negative negative
negative positive
6 79 M asymptomatic positive positive negative negative
positive positive
> x18.dudi.mix <- dudi.mix(ade4plaque.df)
> x18.dudi.mix$eig
[1] 1.7750557 1.4504641 1.2178640 1.0344946 0.8496640 0.8248379
0.7011151 0.6367328 0.5097718
> x18.dudi.mix$eig[1:9]/sum(x18.dudi.mix$eig)
[1] 0.19722841 0.16116268 0.13531822 0.11494385 0.09440711 0.09164866
0.07790168 0.07074809 0.05664131
Still first component explained only 19.8% of the variances, right?
Then, I investigated values of dudi.mix corresponding to PC1 of PCA.
Help file say;
l1 principal components, data frame with n rows and nf columns
li row coordinates, data frame with n rows and nf columns
So, I guess I should use x18.dudi.mix$l1[, 1].
Am I right?
Or should I use multiple correpondence analysis because the first plane
explained 43% of the variance?
Thank you for your help in advance.
Kohkichi
(11/08/18 18:33), Mark Difford wrote:
On Aug 17, 2011 khosoda wrote:
1. Is it O.K. to perform PCA for data consisting of 1 continuous
variable and 8 binary variables?
2. Is it O.K to perform transformation of age from continuous variable
to factor variable for MCA?
3. Is "mjca1$rowcoord[, 1]" the correct values as a predictor of
logistic regression model like PC1 of PCA?
Hi Kohkichi,
If you want to do this, i.e. PCA-type analysis with different
variable-types, then look at dudi.mix() in package ade4 and homals() in
package homals.
Regards, Mark.
-----
Mark Difford (Ph.D.)
Research Associate
Botany Department
Nelson Mandela Metropolitan University
Port Elizabeth, South Africa
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神戸大学大学院医学研究科 脳神経外科学分野
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