Dear Mark,
Thank you very much for your advice.
I will try it.
I really appreciate your all kind advice.
Thanks a lot again.
Best regards,
Kohkichi
(11/08/19 22:28), Mark Difford wrote:
On Aug 19, 2011 khosoda wrote:
I used x10.homals4$objscores[, 1] as a predictor for logistic
Dear Mark,
Thank you very much for your kind advice.
Actually, I already performed penalized logistic regression by pentrace
and lrm in package rms.
The reason why I wanted to reduce dimensionality of those 9 variables
was that these variables were not so important according to the subject
On Aug 19, 2011 khosoda wrote:
I used x10.homals4$objscores[, 1] as a predictor for logistic regression
as in the same way as PC1 in PCA.
Am I going the right way?
Hi Kohkichi,
Yes, but maybe explore the sets= argument (set Response as the target
variable and the others as the predictor
Pooling nominal with numeric variables and running pca on them sounds like
conceptual nonsense to me. You use PCA to reduce the dimensionality of the
data if the data are numeric. For categorical data analysis, you should use
latent class analysis or something along those lines.
The fact that
Dear Daniel,
Thank you for your mail.
Your comment is exactly what I was worried about.
I konw very little about latent class analysis. So, I would like to use
multiple correspondence analysis (MCA) for data redution. Besides, the
first plane of the MCA captured 43% of the variance.
Do you
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
On Aug 18, 2011; Daniel Malter wrote:
Pooling nominal with numeric variables and running pca on them sounds like
conceptual
nonsense to me.
Hi Daniel,
This is not true. There are methods that are specifically designed to do a
PCA-type analysis on mixed categorical and continuous variables,
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
Hi all,
I'm trying to do model reduction for logistic regression. I have 13
predictor (4 continuous variables and 9 binary variables). Using subject
matter knowledge, I selected 4 important variables. Regarding the rest 9
variables, I tried to perform data reduction by principal component
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