1
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1answer
53 views

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 ...
0
votes
0answers
12 views

Calculate the correlation between a principal component and a feature of the original data set in R?

I am interested in seeing the correlation between a particular principal component and a particular independent variable in my 'original' data set, that is, I'd like to calculate $\rho_{Y_{i},X_{k}} ...
1
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0answers
15 views

Method for analysing effect of model input

I work with a mechanistic model that uses climate data to simulate some specific output for different time steps and spatial locations. Now I want to investigate the effect of different climate data ...
1
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0answers
21 views

What does the predict() function on a PCA model return? [closed]

Specifically, when I add "newdata": ...
0
votes
0answers
20 views

Programming Multiple Variable PCA Ratios

I would like to generalize Paul Teetor's A Better Hedge Ratio, which uses prcomp() to determine a TLS ratio between two variables. I am hoping to extend this to multiple variables, but am having ...
1
vote
0answers
24 views

Comparing isomap residual variance to pca variance

I am using R princomp function (from stats package) to run a PCA on a data set and I want to compare its output to that of the nonlinear dimensionality reduction method ISOMAP, which I am using under ...
0
votes
0answers
16 views

KPCA in R proportion of variability explained

I'm using kpca function from kernlab and try to get the proportion of variance explained by each component as in standard pca. I ...
1
vote
0answers
42 views

PCA figure formatting options in R

I've completed PCA analysis, in R with VEGAN package, of some ecological data on tree health. There are 80 trees total (so, 80 'sites') divided into four treatment categories. I've got the data ...
0
votes
0answers
35 views

Significant difference between two correlation matrices

We have two huge correlation matrices at different experimental conditions. If we want to identify the significant differences between these matrices , what would be the ideal method. I have ...
7
votes
3answers
252 views

A toy model of Principal Components Analysis in R

I'm working in R through an excellent PCA tutorial by Lindsay I Smith and am getting stuck in the last stage. The R script below takes us up to the stage (on p.19) where the original data is being ...
0
votes
0answers
11 views

Error trying to reduce my data dimentions [duplicate]

I'm trying to produce a linear regression model, but I only have 25 observations and 34 predictors. I'm trying feature selection, ...
7
votes
2answers
374 views

Why do PCA scores differ in sign?

Consider this: ...
1
vote
2answers
78 views

PCA of hyperspectral image data

I have some hyperspectral image data similar to this, and I want to do a PCA on it. The problem: I've never done a PCA, and its specially difficult for me to do it on 3D data. How can I do it in ...
1
vote
2answers
63 views

Predict only the first N principal components in a PCA analysis

I'm using R to analyze a very large dataset. I conduct a PCA on one dataset, PCA <- prcomp(formula = ~., data = train, scale = T, na.action=na.exclude) and ...
1
vote
1answer
75 views

Computing scalar/dot product between principal component and data

I am very new to R and statistics so this may be a simple question. I have a matrix (1000,756) containing 1000 years of winter sea-level pressure data (SLP) at 756 locations in the North Atlantic. I ...
1
vote
2answers
64 views

Reconstruction error of PCA when space dimensionality is larger than number of points

I've got a question and I have done several experiments in R, yet couldn't figure out why. The question is for a data set of N*D, N for number of data points and D for dimension, the maximum number ...
1
vote
1answer
79 views

What variables explain which PCA components?

Using this data: head(USArrests) nrow(USArrests) I can do a PCA as thus: plot(USArrests) otherPCA <- princomp(USArrests) ...
1
vote
1answer
112 views

Doing EOF analysis in R

I am very new to R and statistics as a whole so this may be a very simple question. I am trying to carry out empirical orthogonal function (EOF) analysis of sea-level pressure (SLP) data to determine ...
5
votes
1answer
112 views

Interpret clustering plotted in the first two principal components

I got this plot when I plotted a clustering object in R. If I run km <- clara(data, 2), then plot(km), I get a similar ...
0
votes
0answers
50 views

princomp loadings

My main goal is a model of the form Y ~ D + N . But lets say N and D correlate strongly so I decide to do a PCA on them I can do.. ...
1
vote
1answer
75 views

Using Principal Components Analysis for feature selection

I have a dataset D made of m samples, and n features with n >> m. For each sample I have a score s which I would like to ...
5
votes
1answer
374 views

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 ...
2
votes
0answers
93 views

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: ...
3
votes
1answer
190 views

What are the units in this biplot?

This is a plot of my data These are the values: ...
2
votes
0answers
45 views

PCA for predictors in lmm or glmm

Imagine we have the following mixed model mod <- lmer(y ~ x + z +(1|g), data = dat) where: ...
0
votes
1answer
58 views

Project feature vectors into 2D plane for visualization

I would like to project my observations which consist of more than 2 variables into a scatter plot. Some time ago I saw an R package that does this by reducing the dimensions (possibly using PCA and ...
1
vote
0answers
197 views

Principal component analysis with time series

When you do a Principal Component Analysis (PCA), your dataset generally looks like the following one: ...
0
votes
2answers
90 views

R or python implementation of sparse PCA for p>n

According to this paper, there are 2 algorithms to perform sparse PCA. One is better if $p>n$. I need to run SPCA on a $2000\times12000$ matrix so I am looking for an implementation of this ...
0
votes
0answers
99 views

Which variables to retain in order to preserve the same clustering pattern?

Suppose I have 50 scale parameters, these are all genes measured for one sample from a subject at the clinic, after data reduction by PCA, two meaningful components were extracted. This was followed ...
3
votes
1answer
122 views

Follow up of cluster analysis with membership prediction

I have 11 scale parameters for each of 218 observations belonging to subjects, I did standardized PCA to reduce dimensionality of the data and found two meaningful components. Using Euclidean ...
1
vote
2answers
54 views

The size of my reduced data set is greater than the original

I have an original data set with a number of features N equal to 135 and a number of rows equal to 32000. The last column of the data set ( column 136 ) can take either -1 or 1 depending on the class ...
0
votes
0answers
53 views

Time-series data evaluation by PCA / EOF

I have got time series data of a crop attribute, each time step with 500 data points on a spatial grid. 2 measurements were conducted in the first year, 3 in the consecutive year. I want to evaluate ...
2
votes
2answers
2k views

How to use R prcomp results for prediction?

I have a data.frame with 800 obs. of 40 variables, and would like to use Principle Component Analysis to improve the results of my prediction (which so far is working best with Support Vector Machine ...
11
votes
2answers
543 views

Showing spatial and temporal correlation on maps

I have data for a network of weather stations across the United States. This gives me a data frame that contains date, latitude, longitude, and some measured value. Assume that data are collected once ...
3
votes
1answer
211 views

Using principal components analysis vs correspondence analysis

I am analyzing a data set concerning intertidal communities. The data are percent cover (of seaweed, barnacles, mussels, etc) in quadrats. I am used to thinking about correspondence analysis (CA) in ...
1
vote
0answers
153 views

Estimate area of 95% confidence ellipse in R

I have data from a PCA and would like to know the area of the 95% confidence ellipse of the first two PCs. Does anyone know how to estimate the area of a 95% confidence ellipse in R?
0
votes
0answers
61 views

Would rotation of extracted components/factors after PCA/EFA affect results of a subsequent regression analysis?

To use the scores of the extracted components/factors in a further regression analysis, like mixed effects model regression as predictors to an outcome variable or DV. Would be there any discrepancies ...
1
vote
2answers
190 views

R function PRCOMP Doesn't project my 2D cloud onto the principal vector as expected

I have generated 100 2D correlated MVN variables in R, on which I run prcomp. When I plot the projected points along the first principal component (in the original ...
0
votes
1answer
97 views

PCA (NIPALS method) [duplicate]

I am trying to apply PCA (NIPALS method) using Excel. The problem is my results are not exactly matching with the results of R. ...
1
vote
0answers
296 views

Principal component analysis in 3D Scatterplot

I am new to principal component analysis (PCA). I performed PCA for a dataset with 54 samples. When I project them in 3D scatterplot, I can see samples with similar characteristics are grouped ...
0
votes
0answers
348 views

PCA, LDA (Linear Discriminant Analysis), proportion of variance

I have some basic questions regarding PCA and LDA (Linear Discriminant Analysis ) but I am a bit lost and I will appreciate the help. In PCA there is a way to calculate the proportion of variance ...
1
vote
1answer
109 views

Binary classification on imbalance data with missing values and almost equal dimensions?

I have a data set with following qualities: Attributes: 500 Instances: 1500 Class Ratio: 1:15 Missing Values: Yes ~5% How should I perform classification on ...
4
votes
2answers
316 views

Why such a poor result from sparse PCA R package?

I'm preparing to use R to perform sparse analysis on my data. I tried to get started with an ad hoc example, but the reconstruction result turned out really poor. I'm wondering if I was making any ...
1
vote
1answer
352 views

How to use SVD for dimensionality reduction [duplicate]

After reading several "tutorials" on SVD I am left still wondering how to use it for dimensionality reduction. Here is my confusion in an applied setting. If I limit svd to only considering the first ...
1
vote
1answer
327 views

Providing starting values for a Generalized Linear Mixed Model with glmmPQL

I am trying to run a Generalized Linear Mixed Model on some data. What I am trying to do is use distances from habitat features to predict a distance between 2 animal locations. I ran a PCA on the ...
5
votes
2answers
182 views

Iterative PCA R

I have a largish data set (400,000 variables of 1000 samples). I would like to identify what is the best set of these variables for capturing most of the variance between samples. What's the best ...
1
vote
1answer
1k views

How to output varimax-rotated data in R?

I have ran PCA on 25 variables and selected the top 7 PCs using 'prcomp'. prc <- prcomp(pollutions, center=T, scale=T) I have then done varimax rotation on ...
6
votes
4answers
2k views

How to perform dimension reduction after doing PCA in R?

I have a big dataset of +- 40000 observations, each containing 784 variables. Because this dataset is extremely large I want to perform a dimension reduction. Now everywhere I read that I can use ...
0
votes
0answers
76 views

PCA replicate data/index R

I have a set of date covering petrol prices. My example has two columns where each row represents a sequential date. ...
1
vote
1answer
457 views

Kernel PCA (in R)

I am attempting to use the kernel PCA features in kernlab but am having trouble understanding the output. In particular, it's unclear what scale the results are in ...