Optimal scaling or optimal quantification is an algorithmic approach to transform categorical variables into scale (interval) ones which would be "optimal" in some statistical sense (for example, their linear correlations will be maximized). There exist nonlinear "optimal scaling versions" of many ...

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Categorical Principal Component Analysis

I am planning to perform a Categorical Principal Component Analysis, I have 14 variables with categorical ordinal data (from a 5 point likert scale) and one variable with categorical nominal data. ...
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Scaling data, keeping relative numerical dispersion

I have values two vectors that are spread across ranges of [-5, -1] and [1, 5] respectively. They can be decimals. Here is a toy example: ...
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OSC-filter is a also a kind of normalization?

Before to aplicate an OSC filter (in SIMCA-P software) is necesary to have normalized my data? or does no matter? Even more, what is the effect of applicate log transform and a Pareto scale after OSC ...
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Normalization by decimal scaling

I came to this normalization technique Normalization by decimal scaling normalizes by moving the decimal point of values of attribute A. The number of decimal points moved depends on the maximum ...
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152 views

scaling for SVM destroys my results

I'm applying standard 0-1 scaling of features before SVM classification for financial data but the results are worse. This is the results before scaling ...
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Scaling and Rounding

x y 0.4 0 11.43 11 0.45 0 0.44 0 Total 12.72 11 I have a very large dataset of x values and rounded off values of x as y. Y cannot take ...
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What does R do with negative values in log() scale?

Some context of the problem: I am working on an analysis of some hypothetical donation data: I would like to investigate the differences between 'major donors' (those whose largest single donation is ...
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Likert Scale 3 2 1 0 1 2 3 possible?

In my questionnaire I want to ask people to score on a dimensional Likert-type scale their attitude toward a specific target group. E.g. People with .... are stupid.... intelligent. I want to use ...
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Which type of feature scaling to use [duplicate]

I've been writing some simple machine learning algorithms and looking at time-series data. In doing so, I have come across the use of feature scaling: rescaling and standardizing as they are referred ...
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383 views

Scaling categorical data in regression

It seems odd to scale a categorical variable, but I need to get the correct coefficients for each of my variables in linear regression. Is it correct to scale the same way you would with continuous ...
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Mathematics of simple performance testing [duplicate]

I have a set of sorted tables T that have known but different dimensions. There are two types of functions in this system: f(T) g(T, n), where n is an integer parameter. ... and two types of costs ...
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382 views

Similarities and dissimilarities between optimal scaling procedures in CATPCA and MCA

I have seen that multiple correspondence analysis (MCA) can also be seen as a generalization of principal component analysis when the variables to be analyzed are categorical instead of quantitative. ...
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should weights be scaled too?

I am using supervised learning algorithms (specificly SVM) on my data. I know that scaling was needed for my input data. however as I am also adding weights (using pairwise comparison), I am not sure ...
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Scaling in SVM (why and how to , plus references)

Hi I know why feature scaling is preferred in SVM, I have two questions: 1-does anyone know of legit articles of books explaining it. I am writing my thesis and I need references. It doesnt have to be ...
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Optimal scaling / CATREG (categorical regression) for imputed data

I have a data set with 5 different kinds of nutrient statuses and I want to see whether they are associated with categorical / ordinal grades at school. I have multiple covariates which I will ...
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What is PRINCIPAL HESSAIN Direction Model, how and where can I use it?

I'm a M.Tech student going through my academic project-work, here I have asked to develop a optimal design and a best equation to fit the data for a Leaching, Solvent extraction and Electrowinning ...
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57 views

Question on scaling data

I have been reading Elements of Statistical Learning. And I saw some example R codes written online. I realise that very often the explanatory variables $X$ (the data of the predictors) are ...
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371 views

Can I categorize the factor scores to use them as predictors of an ordinal logistic regression?

I was wondering if I can categorize the factor saved scores by taking their quartiles (or some other measures, I am not sure what should I use!) as cut points and use them as predictors in an ordinal ...
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Optimal scaling level for variables categorized from continuous data

A variable was categorized into 10 equally spaced intervals from a continuous variable which was originally in proportion. Now, we have to use this variable in CATREG procedure. If we choose the ...
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Can I use optimally scaled variables for a factor analysis to account for rotation? If I can then how?

I have discussed this issue several times in this site, but I am asking it again for a final justification from the experts of our community. I wanted to extract four factors (I should call dimensions ...
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How to obtain optimally scaled data from homals in R?

I am using the homals-package in R to optimally quantify my data that contains categorical (nominal and ordinal) as well as numerical variables. My goal is to ...
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What is the advantage of transforming variables from nominal to ordinal/numerical when it reduces variance explained in CatPCA?

Context I have a dataset of 8 categorical variables. And I want to apply Categorical Principal Component Analysis (CatPCA). Before doing that, I have been advised to look at the transformation plots ...
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How to calculate Rousseeuw’s and Croux’ (1993) Qn scale estimator for large samples?

Let $Q_n = C_n.\{|X_i-X_j|;i < j\}_{(k)}$ so for a very short sample like $\{1,3,6,2,7,5\}$ it can be calculated from finding the $k$th order static of pairwise differences: ...
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Parametric Surface Reconstruction from Contours with Quick Rescaling

I'm looking to construct a 3-D surface of a part of the brain based on 2-D contours from cross-sectional slices from multiple angles. Once I get this shape, I want to "fit" it to another set of ...
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How can I use optimal scaling to scale an ordinal categorical variable?

In an answer to this question about treating categorical data as continuous, optimal scaling was mentioned. How does this method work and how is it applied?