# Tagged Questions

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### How to find which variables are most correlated with the first principal component?

I came across an article where the authors did a Principal Component Analysis on gene expression data, and found out the genes that are most correlated to the 1st principal component, and they used ...
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I am using methylKit to perform an analysis on my MethylCAP-bisulfite data. The prcomp() function has been used in "PCASamples" (a command in methylKit) to do PCA ...
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### Principal component Regression Using R

Can anyone explain principal component regression with the help of an example and the code in R? How to interpret the result of a principal component regression? How to find the individual effect of ...
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### Using principal component analysis to reduce dimensions of data in R [closed]

I have a dataset which includes 4 separate measures of intelligence. To simplify my analysis, I wanted to express them as "g" a variable based on the shared variation of the 4 measures. A paper I read ...
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### Multiple Factor analysis and squared cosines

I am a bit confused on how to proceed using the MFA analysis from FactoMineR in my data set. I am currently working with activity results of 15 bacteria (b1, b2, b3, b4, b5,.., b15), divided into 3 ...
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### Multivariate regression or PCA to reduce response variables?

I hope the title is self-explanatory, but essentially I want to know which method is better: does it make sense to use a PCA to reduce a number of response Y variables and then conduct a univariate ...
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### Pull out most important variables from PCA

I would like to get the most important variables from a PCA result. I see two clusters in the plot. I now that is possible that there is no only one variable causing this, so maybe I would have to get ...
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### Principal components using correlation matrix in R

My understanding is that prcomp and princomp work off the dataset itself (row of observations, across variables in the columns). ...
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### Is it important to convert “integer” variables (with 0 or 1 values) to factors?

I am working on a high-dimensional dataset (1776 variables). When I read the csv file, R loads variables (with 0 or 1 values) as class of "integer". Is it important to convert these variables to ...
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### Feature selection from wavelet transformation in R

I am new to wavelets. Currently, I am developing a prediction model using time series data. I am using the wavelets package in R. I am taking part of the time ...
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### Correlation tests – multivariate correlation matrix?

I just got comments from a reviewer to a submitted article and didn't understand what I should do very well. Here are the tests I performed: We first used principal components analysis (PCA) to ...
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### Impractically long running time PCA command in R RStudio

I am using R in RStudio on OS X ver. 10.9.2 on 1.7 GHz Intel Core i7 with 8 GB RAM. I am trying to run a PCA command (prcomp) and plots on a dataset with ...
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I performed two principal components analyses: in R and in SPSS - using the same dataset and the same variables. I got the same results - at least to some point. The eigenvalues are the same (I used ...
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### PCA on Binary Data

I having binary data set (yes/no), so can I apply PCA on that. Is it mathematically correct to do that. In my opinion Binary variable can only be subjected to logical operations, so how it can be ...
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### Problem with PCA in R (suspiciously high explained variance)

I have always been confused about how to properly interpret PCA results. My data looks like this and it's a big table with more than 5 million rows and 12 columns.(the first few lines are all 0...) ...
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### X,Y coordinates confidence ellipses and centroids

As I try to add my PCA ggbiplot a centroid I wonder is the center of the confidence ellipses (X,Y) are the same X,Y coordinates of the centroids?. If so how can I extract them? Thanks
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### Calculate centroid in PCA

If I understand correctly in order to calculate a centroid in PCA I can calculate the mean of X points and Y points (e.g., PC1 and PC2). When I run a simple PCA (code below) I don’t get the centroid ...
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### Sparse principal component analysis

Does anyone know where I can find an algorithm (as well as an R implementation of it) to carry out sparse principal component analysis?
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### Using PC scores or cluster analsis in predictions

I have very big data and low number of observations. So I decided to use PCA to reduce dimension of the data. The following is R example (just an dummy example - for workout): ...
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### PCA and component scores based on a mix of continuous, binary and categorical variables

My question is strongly related to this one: PCA and component scores based on a mix of continuous and binary variables. I will basically use the same code, but add a new nominal feature (x6) to the ...
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### Interpreting the results of a pca

I applied pca on r using prcomp. I would like to use the main PCs to reduce the size of my problem. In order to do so, I want to express the values of my samples on ...
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### How to export and use results of PCA from R?

My ultimate goal is to run a cluster analysis on a data set with > 1 million records. The input variables for the cluster analysis will be the results of a Principal Component Analysis, as well as ...
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### R implementation of some new Principal Component Pursuit methods

I'm looking for R packages implementing some new PCA methods. The first one is the Stable Principal Component Pursuit method of Zhou et al. (2010). The second one is the PCA via Outlier Pursuit ...
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### PCA: 91% of explained variance on one principal component

I am new to PCA and wanted to do a bit of experimentation on my data set just to see what it looked like (using R). I am not able to give access to the data here since it is confidential. However, if ...
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### PCA Using prcomp in R

I'm trying to do principal component analysis (PCA) in R using the prcomp function. My input is a large matrix of 1,188 observations (rows) and 15,462 features (cols). I input this to the function ...
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### Problem with PCA

I am trying to do some PC analysis on my data coming from lipids measurements in different samples. I only have one factor: if samples are diabetic or non-dibetic. Here is the PCA graph I get: As ...
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### cluster plot: working and interpretation ?

Recently I have come across usage of cluster plot, which combines k-mean clustering along with PCA. The plot shows different clusters plotted using first two PCs. I have checked some of the threads ...
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### Can the scaling values in a linear discriminant analysis (LDA) be used to plot explanatory variables on the linear discriminants?

Using a biplot of values obtained through principal component analysis, it is possible to explore the explanatory variables that make up each principle component. Is this also possible with Linear ...
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### Multivariate orthogonal regression in R

I have a project in which I need to perform orthogonal regression in a multivariate space. For the univariate case, I've found Teetor's R Cookbook suggests using principle components: ...