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How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction
2011 Aug 17
4
How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction
...e and did
PCA with princomp. PC1 explained only 20% of the variance. Still, I used
the PC1 as a predictor of the logistic model and obtained some results.
Then, I tried multiple correspondence analysis (MCA). The only one
continuous variable was age. I transformed "age" variable to "age_Q"
factor variable as the followings.
> quantile(mydata.df$age)
0% 25% 50% 75% 100%
53.00 66.75 72.00 76.25 85.00
> age_Q <- cut(x17.df$age, right=TRUE, breaks=c(-Inf, 66, 72, 76, Inf),
labels=c("53-66", "67-72", "73-76", "77-85"))
>...
2005 Aug 26
3
parts of data frames: subset vs. [-c()]
...d ReVerb:
?? str(ReVerb)
`data.frame': 92713 obs. of 16 variables:
$ CHILD : Factor w/ 7 levels "ABE","ADA","EVE",..: 1 1 1 1 1 1 1 1 1 1 ...
$ AGE : Factor w/ 484 levels "1;06.00","1;06.16",..: 43 43 43 99 99 99 99 99 99 99 ...
$ AGE_Q : num 2.0 2.0 2.0 2.4 2.4 ...
$ INTERVALS: num 2 2 2 2.25 2.25 2.25 2.25 2.25 2.25 2.25 ...
$ RND : int 34368 38311 14949 20586 72516 27186 88019 10767 114448 86146 ...
$ SYNTAX : Factor w/ 17 levels "Acmp","Amats",..: 15 12 8 15 7 16 7 7 16 7 ...
$ LEXICAL : F...