Scoring Multiple Variables, Too Many Variables and Too Few Observations: Data Reduction

variability

data-reduction

2017

This article addresses data reduction, also called unsupervised learning.

Author

Affiliation

Frank Harrell

Vanderbilt University
School of Medicine
Department of Biostatistics

Published

November 21, 2017

This post will grow to cover questions about data reduction methods, also known as unsupervised learning methods. These are intended primarily for two purposes:

Comments

collapsing correlated variables into an overall score so that one does not have to disentangle correlated effects, which is a difficult statistical task
reducing the effective number of variables to use in a regression or other predictive model, so that fewer parameters need to be estimated

The latter example is the “too many variables too few subjects” problem. Data reduction methods are covered in Chapter 4 of my book Regression Modeling Strategies, and in some of the book’s case studies.

Sacha Varin writes

I want to add/sum some variables having different units. I decide to standardize (Z-scores) the values and then, once transformed in Z-scores, I can sum them. The problem is that my variables distributions are non Gaussian (my distributions are not symmetrical (skewed), they are long-tailed, I have all types of weird distributions, I guess we can say the distributions are intractable. I know that my distributions don’t need to be gaussian to calculate Z-scores, however, if the distributions are not close to gaussian or at least symmetrical enough, I guess the classical Z-score transformation: (Value - Mean)/SD is not valid, that’s why I decide, because my distributions are skewed and long-tailed to use the Gini’s mean difference (robust and efficient estimator).

If the distributions are skewed and long-tailed, can I standardize the values using that formula (Value - Mean)/GiniMd ? Or the mean is not a good estimator in presence of skewed and long-tailed distributions? What about (Value - Median)/GiniMd ? Or what else with GiniMd for a formula to standardize?
In presence of outliers, skewed and long-tailed distributions, for standardization, what formula is better to use between (Value - Median)/MAD (=median absolute deviation) or Value - Mean)/GiniMd ? And why? My situation is not the predictive modeling case, but I want to sum the variables.

These are excellent questions and touch on an interesting side issue. My opinion is that standard deviations (SDs) are not very applicable to asymmetric (skewed) distributions, and that they are not very robust measures of dispersion. I’m glad you mentioned Gini’s mean difference, which is the mean of all absolute differences of pairs of observations. It is highly robust and is surprisingly efficient as a measure of dispersion when compared to the SD, even when normality holds.

The questions also touch on the fact that when normalizing more than one variable so that the variables may be combined, there is no magic normalization method in statistics. I believe that Gini’s mean difference is as good as any and better than the SD. It is also more precise than the mean absolute difference from the mean or median, and the mean may not be robust enough in some instances. But we have a rich history of methods, such as principal components (PCs), that use SDs.

What I’m about to suggest is a bit more applicable to the case where you ultimately want to form a predictive model, but it can also apply when the goal is to just combine several variables. When the variables are continuous and are on different scales, scaling them by SD or Gini’s mean difference will allow one to create unitless quantities that may possibly be added. But the fact that they are on different scales begs the question of whether they are already “linear” or do they need separate nonlinear transformations to be “combinable”.

I think that nonlinear PCs may be a better choice than just adding scaled variables. When the predictor variables are correlated, nonlinear PCs learn from the interrelationships, even occasionally learning how to optimally transform each predictor to ultimately better predict Y. The transformations (e.g., fitted spline functions) are solved for to maximize predictability of a predictor, from the other predictors or PCs of them. Sometimes the way the predictors move together is the same way they relate to some ultimate outcome variable that this undersupervised learning method does not have access to. An example of this is in Section 4.7.3 of RMS.

With a little bit of luck, the transformed predictors have more symmetric distributions, so ordinary PCs computed on these transformed variables, with their implied SD normalization, work pretty well. PCs take into account that some of the component variables are highly correlated with each other, and so are partially redundant and should not receive the same weights (“loadings”) as other variables.

The R transcan function in the Hmisc package has various options for nonlinear PCs, and these ideas are generalized in the R homals package.

How do we handle the case where the number of candidate predictors p is large in comparison to the effective sample size n? Penalized maximum likelihood estimation (e.g., ridge regression) and Bayesian regression typically have the best performance, but data reduction methods are competitive and sometimes more interpretable. For example, one can use variable clustering and redundancy analysis as detailed in the RMS book and course notes. Principal components (linear or nonlinear) can also be an excellent approach to lowering the number of variables than need to be related to the outcome variable Y. Two example approaches are:

Use the 15:1 rule of thumb to estimate how many predictors can reliably be related to Y. Suppose that number is k. Use the first k principal components to predict Y.
Enter PCs in decreasing order of variation (of the system of Xs) explained and chose the number of PCs to retain using AIC. This is far from stepwise regression which enters variables according to their p-values with Y. We are effectively entering variables in a pre-specified order with incomplete principal component regression.

Once the PC model is formed, one may attempt to interpret the model by studying how raw predictors relate to the principal components or to the overall predicted values.

Returning to Sacha’s original setting, if linearity is assumed for all variables, then scaling by Gini’s mean difference is reasonable. But psychometric properties should be considered, and often the scale factors need to be derived from subject matter rather than statistical considerations.

Discussion Archive (2017)

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--- title: "Scoring Multiple Variables, Too Many Variables and Too Few Observations: Data Reduction" author: - name: Frank Harrell url: https://hbiostat.org affiliation: Vanderbilt University<br>School of Medicine<br>Department of Biostatistics date: 2017-11-21 modified: 2017-11-26 categories: [variability, data-reduction, 2017] description: "This article addresses data reduction, also called unsupervised learning." --- This post will grow to cover questions about data reduction methods, also known as *unsupervised learning* methods. These are intended primarily for two purposes: [[Comments](https://hbiostat.org/comment.html)]{.aside} - collapsing correlated variables into an overall score so that one does not have to disentangle correlated effects, which is a difficult statistical task - reducing the effective number of variables to use in a regression or other predictive model, so that fewer parameters need to be estimated The latter example is the "too many variables too few subjects" problem. Data reduction methods are covered in Chapter 4 of my book *Regression Modeling Strategies*, and in some of the book's case studies. ------------------------------------------------------------------------ ### Sacha Varin writes ::: {.quoteit} I want to add/sum some variables having different units. I decide to standardize (Z-scores) the values and then, once transformed in Z-scores, I can sum them. The problem is that my variables distributions are non Gaussian (my distributions are not symmetrical (skewed), they are long-tailed, I have all types of weird distributions, I guess we can say the distributions are intractable. I know that my distributions don't need to be gaussian to calculate Z-scores, however, if the distributions are not close to gaussian or at least symmetrical enough, I guess the classical Z-score transformation: (Value - Mean)/SD is not valid, that's why I decide, because my distributions are skewed and long-tailed to use the Gini's mean difference (robust and efficient estimator). 1. If the distributions are skewed and long-tailed, can I standardize the values using that formula (Value - Mean)/GiniMd ? Or the mean is not a good estimator in presence of skewed and long-tailed distributions? What about (Value - Median)/GiniMd ? Or what else with GiniMd for a formula to standardize? 2. In presence of outliers, skewed and long-tailed distributions, for standardization, what formula is better to use between (Value - Median)/MAD (=median absolute deviation) or Value - Mean)/GiniMd ? And why? My situation is not the predictive modeling case, but I want to sum the variables. ::: These are excellent questions and touch on an interesting side issue. My opinion is that standard deviations (SDs) are not very applicable to asymmetric (skewed) distributions, and that they are not very robust measures of dispersion. I'm glad you mentioned [Gini's mean difference](https://arxiv.org/pdf/1405.5027.pdf), which is the mean of all absolute differences of pairs of observations. It is highly robust and is surprisingly efficient as a measure of dispersion when compared to the SD, even when normality holds. The questions also touch on the fact that when normalizing more than one variable so that the variables may be combined, there is no magic normalization method in statistics. I believe that Gini's mean difference is as good as any and better than the SD. It is also more precise than the mean absolute difference from the mean or median, and the mean may not be robust enough in some instances. But we have a rich history of methods, such as principal components (PCs), that use SDs. What I'm about to suggest is a bit more applicable to the case where you ultimately want to form a predictive model, but it can also apply when the goal is to just combine several variables. When the variables are continuous and are on different scales, scaling them by SD or Gini's mean difference will allow one to create unitless quantities that may possibly be added. But the fact that they are on different scales begs the question of whether they are already "linear" or do they need separate nonlinear transformations to be "combinable". I think that nonlinear PCs may be a better choice than just adding scaled variables. When the predictor variables are correlated, nonlinear PCs learn from the interrelationships, even occasionally learning how to optimally transform each predictor to ultimately better predict Y. The transformations (e.g., fitted spline functions) are solved for to maximize predictability of a predictor, from the other predictors or PCs of them. Sometimes the way the predictors move together is the same way they relate to some ultimate outcome variable that this undersupervised learning method does not have access to. An example of this is in Section 4.7.3 of [RMS](https://hbiostat.org/rms). With a little bit of luck, the transformed predictors have more symmetric distributions, so ordinary PCs computed on these transformed variables, with their implied SD normalization, work pretty well. PCs take into account that some of the component variables are highly correlated with each other, and so are partially redundant and should not receive the same weights ("loadings") as other variables. The R transcan function in the Hmisc package has various options for nonlinear PCs, and these ideas are generalized in the R [homals](https://cran.r-project.org/web/packages/homals) package. How do we handle the case where the number of candidate predictors p is large in comparison to the effective sample size n? Penalized maximum likelihood estimation (e.g., ridge regression) and Bayesian regression typically have the best performance, but data reduction methods are competitive and sometimes more interpretable. For example, one can use variable clustering and redundancy analysis as detailed in the RMS book and course notes. Principal components (linear or nonlinear) can also be an excellent approach to lowering the number of variables than need to be related to the outcome variable Y. Two example approaches are: 1. Use the 15:1 rule of thumb to estimate how many predictors can reliably be related to Y. Suppose that number is k. Use the first k principal components to predict Y. 2. Enter PCs in decreasing order of variation (of the system of Xs) explained and chose the number of PCs to retain using AIC. This is far from stepwise regression which enters variables according to their p-values with Y. We are effectively entering variables in a pre-specified order with incomplete principal component regression. Once the PC model is formed, one may attempt to interpret the model by studying how raw predictors relate to the principal components or to the overall predicted values. Returning to Sacha's original setting, if linearity is assumed for all variables, then scaling by Gini's mean difference is reasonable. But psychometric properties should be considered, and often the scale factors need to be derived from subject matter rather than statistical considerations. ---- ## [Discussion Archive](disqus.pdf) (2017)