Raj at 09:39, 6 August 2013

2013-08-06T09:39:06Z

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Raj at 09:35, 6 August 2013

2013-08-06T09:35:48Z

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Raj at 09:25, 6 August 2013

2013-08-06T09:25:18Z

← Older revision		Revision as of 09:25, 6 August 2013
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Raj: Created page with "Category:R

 ## Getting the data > mydata <- read.table ("D:/ITC/USER/Rudresh/data/data.txt", header=T, sep="\t")  > mydata <- na.omit(mydata) # listwise deletion of ..."

2012-07-10T04:33:51Z

Created page with "Category:R <pre> ## Getting the data > mydata <- read.table ("D:/ITC/USER/Rudresh/data/data.txt", header=T, sep="\t") > mydata <- na.omit(mydata) # listwise deletion of ..."

New page

[[Category:R]]

<pre>
## Getting the data
> mydata <- read.table ("D:/ITC/USER/Rudresh/data/data.txt", header=T, sep="\t")
> mydata <- na.omit(mydata) # listwise deletion of missing
> mydata <- scale(mydata) # standardize variables

## Pricipal Components Analysis (from the correlation matrix )
> fit <- princomp(mydata, cor=TRUE, scores=TRUE )
> summary(fit) # print variance accounted for
> loadings(fit) # pc loadings
> plot(fit,type="lines") # scree plot
> fit$scores # the principal components
> biplot(fit)

## Determine Number of Factors to Extract
> library(nFactors)
> ev <- eigen(cor(mydata)) # get eigenvalues
> ap <- parallel(subject=nrow(mydata),var=ncol(mydata), rep=100,cent=.05)
> nS <- nScree(ev$values, ap$eigen$qevpea)
> plotnScree(nS)

## Simple PCA analysis (PCA Variable Factor Map )
> library(FactoMineR)
> result <- PCA(mydata) # graphs generated automatically
</pre>

PCA - Revision history

Raj at 09:39, 6 August 2013

Raj at 09:35, 6 August 2013

Raj at 09:25, 6 August 2013

Raj: Created page with "Category:R
## Getting the data > mydata <- read.table ("D:/ITC/USER/Rudresh/data/data.txt", header=T, sep="\t") > mydata <- na.omit(mydata) # listwise deletion of ..."