Principal component analysis is a technique to decompose an array of numerical data into a set of orthogonal vectors (uncorrelated linear combinations of the variables) called principal components. The first few principal components often suffice to grasp nearly all the multivariate variability of ...

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use pca method for dementionality reduction in matlab for big data [on hold]

I must use pca in matlab for dementionality reduction for a big data. and I want that show pcs are from what variables. please help me
2
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2answers
47 views

Would PCA work for boolean (binary) data types?

I want to reduce the dimensionality of higher order systems and capture most of the covariance on a preferably 2 dimensional or 1 dimensional field. I understand this can be done via principal ...
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10 views

Back end processing of Pattern Matrix during EFA. [on hold]

I want to know that how pattern matrix (using maximum likelihood and varimax rotation) is calculated. What is the underlying formula or pattern that displays such results? Please guide.
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11 views

Principal component analysis on neural datasets

I have dataset file contains binary bits (0s and 1s) for all attributes Do i still need to apply the Principal component analysis to my dataset to train an autoencoder neural network and when is PCA ...
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18 views

Is it essential to use a correlation matrix when the scales of variables are different in PCA?

I am doing PCA on two variables which have completely different units and scale. More exactly, I am doing multivariate-EOF analysis as the columns of the data matrix represent 12 grid points of the ...
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1answer
53 views

Combining principal component regression and stepwise regression

I want to use a combination of principal component analysis (PCA) and stepwise regression to develop a predictor model. I have 5 independent variables (which are correlated among each other to ...
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12 views

Detecting linear connection between only several measurements in PCA (or any orther method)

$X$ is an $n\times p$ data matrix that has $n-d$ random measurements and $d$ highly correlated measurements, i.e. $X_{n-d+1+i}=b_i+a_iX_{n-d+1}+W_n$ for $i=1,2,...,d-1$, $a_i,b_i$ are unknown ...
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30 views

Next step after principal component analysis

I have a data set that consists of different characteristics of communities. What I want to do is to see how those characteristics influence each other. As in, for example I have the income and ...
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42 views

Can I create an Index score using factor scores as weights?

I am creating an Overall Customer Satisfaction Index score based off of 4 factors that comprise satisfaction for callers to a call center: A representatives concern for your needs; ease of navigating ...
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1answer
39 views

How to compute classification accuracy of PCA?

How to check classification accuracy for principal component analysis (PCA)? In order to compare it with different methods, for example with random forest classification, how can I compute accuracy? ...
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24 views

Difference in the interpretation of PCA and CA biplots

I would be grateful if someone could please explain the potential differences (if any) in the interpretation of a biplot acquired from PCA and Correspondence Analysis (CA) please.
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1answer
17 views

Can PCA explained variance be computed from the components (or from SVD matrices)?

I'm looking to use Spark to calculate PCAs. However I need to get the explained variance for each component and the PCAModel class doesn't appear to provide that. Is there a way to calculated the ...
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17 views

Biplot diagram of PCA analysis differs from plot of variables [closed]

In R I made the following PCA analysis: pca_<- dudi.pca(df=rg,scannf='F',nf=2) then plotted the result: scatter(pca_) I ...
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1answer
34 views

K-means cluster significance after PCA and hierarchical clusters in R with FactoMineR

I am using FactomineR to explore a set of continuous variables in a large set of sites (ecological data). I did a PCA and found the relevant principal components and their scores and such. Afterwards ...
4
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1answer
28 views

What to do in PCA when one variable has similar values in several principal component eigenvectors?

I'm performing a principal component analysis (PCA) using some economic variables of a region. I have six variables and I want to reduce them to two principal components. Most of the variables have ...
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1answer
38 views

Given that principal components are orthogonal, can one say that they show opposite patterns?

I've conducted principal component analysis (PCA) with FactoMineR R package on my data set. I have a general question: Given that the first and the second ...
4
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1answer
39 views

How to identify variables with significant loadings in principal component anlaysis

I have following example of principal component analysis using first 4 variables of iris data set (code in R): ...
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1answer
130 views

What is the “horseshoe effect” and/or the “arch effect” in PCA / correspondence analysis?

There are many techniques in ecological statistics for exploratory data analysis of multidimensional data. These are called 'ordination' techniques. Many are the same or closely related to common ...
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49 views

All samples are concentrated in one part of the PCA scatter plot, how is that possible?

I have an unusual result of a principal component analysis (PCA) that I am unsure how to explain or rationalise. I seem to have component #1 sending all the samples in one direction of the plot ...
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20 views

How to understand PCA output in R? [duplicate]

I have problem on understanding the PCA output. I found two ways of doing PCA: 1) pca <- prcomp(inputdata); 2) do it myself: ...
4
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71 views

How do random data eigenvalues change, as random variables are added?

I am using parallel analysis (Horn 1965) to determine how many principal components I can extract from my data. I can add more variables to my dataset, but I cannot add more cases (I know, that's ...
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1answer
50 views

Can PCA be applied for time series data?

I understand that Principal Component Analysis (PCA) can be applied basically for cross sectional data. Can PCA be used for time series data effectively by specifying year as time series variable and ...
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37 views

Principal Component Analysis interpretation

My issue is with the gaps in the loadings, what do they mean? For example grain size does not have a value in the component 1 column. Does this mean it is insignificant in the grand scheme of things? ...
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How to test time series data using principal component analysis SPSS?

First of all I am very much beginner please understand :) I've seen tutorials on how to do principal component analysis on questionnaire datas, however I cannot find any tutorial using time series ...
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29 views

Leading components in principal component regression explain little variance, what else can I try?

I am trying to use principal component analysis (PCA) to reduce dimensionality before applying linear regression. The problem is that my first 10 components are so weak (explaining only tiny variances ...
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27 views

For a low-rank regularized PCA, what is the limit of dimension reduction for a given p and n of data?

Here p is the dimension of data, and n is the number of data rows, so the data matrix is a $n∗p$, and if we use PCA for dimension reduction, and in this case it is a low-rank regularized PCA, what ...
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1answer
81 views

How PCA would help the K-mean clustering analysis?

Background: I want to classify the residential areas of a city into groups based on their social-economic characteristics, including housing unit density, population density, green space area, housing ...
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34 views

Regression model syntax

this is also posted here but I'm hoping some of the statistics experts can weigh in as well. It is a domain-specific question but heavily relevant to statistical methods as well. I'm following the ...
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36 views

When and why to use scale function (in R) in PCA analysis?

I understand that if the scale of the different variables varies(for example, some expressed in absolute form while other in percentages), that will cause problem in Principal component analysis ...
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17 views

Clustering before or after ordination

Can someone explain the implications of performing clustering either before or after performing NMDS? I have some ecological data and I am performing a clustering analysis to identify communities of ...
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56 views

What is the rationale behind the “eigenvalue > 1” criterion in factor analysis or PCA?

What is the meaning of "eigenvalue > 1" criterion? I understand what eigenvalues and eigenvectors are. This question is w.r.t. this link and this statement there: By default, VARCLUS stops ...
2
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1answer
93 views

Why do all the PLS components together explain only a part of the variance of the original data?

I have a dataset consisting of 10 variables. I ran partial least squares (PLS) to predict a single response variable by these 10 variables, extracted 10 PLS components, and then computed the variance ...
2
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1answer
35 views

“Balancing” principal components

I apologize in advance for the poorness of my statistics and mathematics. I am doing PCA on data (emission spectra) that I know a priori should have two strong components (there are two fluorescent ...
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16 views

Which statistical test to use with a continuous dependent variable and categorical and continuous independent variables?

I have a dependent variable (which is continuous) and it depends on a number of independent variables (both continuous and categorical). In order to find out which independent variables significantly ...
2
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1answer
72 views

Regression performed using Principal Component Analysis

I have a dataset consisting of 10 correlated variables. I need to explain a response variable using these 10 variables, so I am using PCA to reduce dimensionality. Say, I use the first 3 components ...
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1answer
19 views

Principal component/Partial least-squares regression: can we use test data to calculate the factors?

I would like to make a PC/PLS regression and assess the resulting model's predictive power. The strategy is the classical splitting into training/validation/test sets, and using training/validation ...
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12 views

Finding class determination from PLS-DA/PCA

So I'm using PLS-DA via Metaboanalyst. I have two outcome classes (controls vs affected) and using the output of metaboanalyst (coefficient, loading, score, and VIP), am trying to find an "equation" ...
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21 views

How to construct a composite measure using several items in SPSS?

For my master thesis, I have to do a regression analysis. But, as an independent variable, I have to construct a composite measure, being perceived importance (of interest groups). I have 9 variables ...
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1answer
49 views

PCA dramatically reduce the accuracy of classification

I am doing classification of this UCI Dataset in Matlab. I represented dataset as matrix (instances x dimensions) and 2nd matrix as (instances x label [instances x 1]). With Naive Bayess I get ...
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1answer
36 views

How can one apply regression on PCA components to predict output variable?

I read Principle component analysis basics from tutorial1 , link1 and link2. I have data set of 100 variables(including output variable Y) , I want to reduce the variables to 40 by PCA , and then ...
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24 views

Reversing PCA back to original data without any prior information on the latter !

I have a set of data that has n samples described by m variables. I do a PCA to reduce it to just 2 dimensions. I understood that what I did is basically PC=VX, where PC are the principal components, ...
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2answers
99 views

How to do Principal Components Analysis from start to finish in Python or R? [closed]

I'm a Software Engineer trying to learn how to do a Principal Components Analysis in Python or R. I've found a few links which do a good job of explaining the concept from a high-level. However, I ...
3
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1answer
47 views

LDA, Tukey HSD: little questions from a chemist

As you may know, analytic methods like PCA or LDA are used everywhere in science. In my case, I'm a chemist, and I use it to differentiate molecules. I have 4 different systems (let's call them A, B, ...
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1answer
20 views

Projecting a new entry onto the two largest eigenvectors of a PCA model

I have a bit of problems understanding how PCA and SVD works, as most materials focus on calculating the factors rather than the classification of new entries. In order to provide some context of my ...
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40 views

Estimating static factor model for h-step ahead forecasting (using R)

I am trying to estimate a static factor model of the following form $$y_{t+1} = \beta'F_t+\gamma(L)y_t+\epsilon_{t+1}, \\ X_t=\Lambda F_t+e_t$$ where $F_t=(f'_t,...,f'_{t-q})'$ is $r\times 1$, where ...
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49 views

Correlation between principal components

I have two matrices a, b of dimensions (100x500), (100x15000) and I am trying to find associations between sets of variables in both matrices. When I perform principal component analysis on matrix ...
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0answers
37 views

Using SVD or PCA for reducing dimensionality [duplicate]

I have always heard that I can reduce dimensionality of a matrix using SVD. So, I'd like to ask something hypothetically. Suppose that the following matrix A has a high dimensionality and I want to ...
3
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1answer
91 views

PCA: Eigenvectors of opposite sign and all eigenvectors are zero in R

I'm learning PCA in R language. I met two problems right now that I don't understand. 1) I am performing a PCA analysis in R on a 318×17 dataset using some custom code. I take eigen function in R ...
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58 views

Meaning of negative elements in Principal Component Analysis(PCA) rotated component matrix

Suppose that we have this rotated component matrix from PCA (SPSS output): ...
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14 views

Using PCA to evaluate overall influence of input-parameters on model-output

I have a relatively simple spatially explicit model based on 4 input-parameters and would like to know which of these input-parameters cause the largest fraction of the total output variance. As I'm ...