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Results tagged with pca
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user 22518
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
5
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1
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How to interpret this loadings statement in PCA given the example in R?
I am reading in the fabulous book of "Exploratory Multivariate Analysis by Example Using R" 2nd edition by Husson, however when I came across this sentence about PCA loadings and their calculation I couldn't … When I use the FactoMineR package which the above book deals with, I even get more confused as the meaning of the statement in question, see the code below:
library(FactoMineR)
res.pca <- FactoMineR::PCA …
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22
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1
answer
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Is there any required amount of variance captured by PCA in order to do later analyses?
I have a dataset with 11 variables and PCA (orthogonal) was done to reduce the data. … Question
Is there any required value of how much variance should be captured by PCA to be valid? Is it not dependent on the domain knowledge and methodology in use? …
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Minimum sample size for PCA or FA when the main goal is to estimate only few components?
I hope this might be helpful:
for both FA and PCA
''The methods described in this chapter require large samples to derive stable solutions. …
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1
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0
answers
138
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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. …
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Would rotation of extracted components/factors after PCA/EFA affect results of a subsequent ...
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 …
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5
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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. …
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0
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Understanding estimates of qualitative supplementary variables by dimdesc() of FactoMineR pa...
According to the author of the FactoMineR package, this has something to do with how contrasts are set when using ANOVA test. According to the book and the given dataset, the two categories Olympic Ga …
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3
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1
answer
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How to explain the numerical discrepancy between FactoMineR::PCA() and the svd() in their ou...
I am comparing the output of two functions in R to do Principal Component Analysis (PCA), the FactoMineR::PCA() and the base::svd() using the R built-in data set mtcars, given that the former function … () can be accessed and viewed by:
FactoMineR:::PCA …
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Accepted
How to explain the numerical discrepancy between FactoMineR::PCA() and the svd() in their ou...
The difference between FactoMineR:::PCA() and base::svd() is the scaling and negative signs for some columns in the dataset. … The below code is a proof of the above:
# PCA using FactoMineR::PCA()
library(FactoMineR)
res.pca <- FactoMineR::PCA(mtcars[, c(1:11)], ncp = 9, quali.sup = c(8, 9), graph = F) # variable 8 and 9 are supplementary …
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Understanding estimates of qualitative supplementary variables by dimdesc() of FactoMineR pa...
FactoMineR package is helpful when doing PCA and much more. … =11:12,quali.sup=13)
Output of PCA by dimdesc() function
dimdesc(res.pca, proba = 0.2)
The estimates according to FactoMineR
Link between the variable and the categorical variable (1-way anova)
===== …
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1
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How to compute the left singular eigenvector matrix (U) from the output of prcomp() for PCA ...
I am examining the output of the prcomp() function in R for PCA in light of the singular value decomposition equation:
$X = U \cdot \Sigma \cdot V^{T}$, where:
$X$: is the standardized original data matrix …
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1
vote
Accepted
How to compute the left singular eigenvector matrix (U) from the output of prcomp() for PCA ...
This can be achieved by computing the $\Sigma$ first from the prcomp() function to make it numerically equivalent to that of the svd()and it turned out after inspecting the source code of prcomp() thi …
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Is PCA followed by a rotation (such as varimax) still PCA?
In psych::principal() you can do different types of rotations/transformations to your extracted Principal Component(s) or ''PCs'' using the rotate= argument, like:
"none", "varimax" (Default), "quatim …
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