4.2. data analysis

4.2.1. pqrs

  • Goal: inference - conclusion or opinion formed from evidence

  • PQRS

    • P - population

    • Q - question - 2 types

      1. hypothesis driven - does a new drug work

      2. discovery driven - find a drug that works

    • R - representative data colleciton

      • simple random sampling = SRS

        • w/ replacement: \(var(\bar{X}) = \sigma^2 / n\)

        • w/out replacement: \(var(\bar{X}) = (1 - \frac{n}{N}) \sigma^2 / n\)

    • S - scrutinizing answers

4.2.2. visualization

First 5 parts here are based on the book storytelling with data by cole nussbaumer knaflic

  • difference between showing data + storytelling with data understand the context (1)

  • who is your audience? what do you need them to know/do?

  • exploratory vs explanatory analysis

  • slides (need little details) vs email (needs lots of detail) - usually need to make both in slideument

  • should know how much nonsupporting data to show

  • distill things down into a 3-minute story or a 1-sentence Big Idea

  • easiest to start things on paper/post-it notes choose an effective visual (2)

Screen Shot 2020-09-28 at 8.08.38 PM

Screen Shot 2020-09-28 at 8.08.30 PM

Screen Shot 2020-09-28 at 8.23.47 PM

Screen Shot 2020-09-28 at 8.24.37 PM

Screen Shot 2020-09-28 at 8.29.34 PM

Screen Shot 2020-09-29 at 9.50.15 AM

Screen Shot 2020-09-29 at 9.56.44 AM

Screen Shot 2020-09-29 at 11.20.07 AM

  • generally avoid pie/donut charts, 3D charts, 2nd y-axes

  • tables

    • best for when people will actually read off numbers

    • minimalist is best

  • bar charts should basically always start at 0

    • horizontal charts typically easy to read

  • on axes, retain things like dollar signs, percent, etc. eliminate clutter (3)

  • gestalt principles of vision

    • proximity - close things are grouped

    • similarity - similar things are grouped

    • connection - connected things are grouped

    • enclosure

    • closure

    • continuity

  • generally good to have titles and such at top-left!

  • diagonal lines / text should be avoided

    • center-aligned text should be avoided

  • label lines directly focus attention (4)

  • visual hierarchy - outlines what is important tell a story / think like a designer (5)

  • affordances - aspects that make it obvious how something will be used (e.g. a button affords pushing)

  • “You know you’ve achieved perfection, not when you have nothing more to add, but when you have nothing to take away” (Saint‐Exupery, 1943)

  • stories have different parts, which include conflict + tension

    • beginning - introduce a problem / promise

    • middle - what could be

    • end - call to action

  • horizontal logic - people can just read title slides and get out what they need

  • can either convince ppl through conventional rhetoric or through a story visual summaries

  • numerical summaries

    • mean vs. median

    • sd vs. iq range

  • visual summaries

    • histogram

    • kernel density plot - Gaussian kernels

      • with bandwidth h \(K_h(t) = 1/h K(t/h)\)

  • plots

    1. box plot / pie-chart

    2. scatter plot / q-q plot

    • q-q plot = probability plot - easily check normality

      • plot percentiles of a data set against percentiles of a theoretical distr.

      • should be straight line if they match

    1. transformations = feature engineering

    • log/sqrt make long-tail data more centered and more normal

    • delta-method - sets comparable bw (wrt variance) after log or sqrt transform: \(Var(g(X)) \approx [g'(\mu_X)]^2 Var(X)\) where \(\mu_X = E(X)\)

    • if assumptions don’t work, sometimes we can transform data so they work

    • transform x - if residuals generally normal and have constant variance

      • corrects nonlinearity

    • transform y - if relationship generally linear, but non-constant error variance

      • stabilizes variance

    • if both problems, try y first

    • Box-Cox: Y’ = \(Y^l \: if \: l \neq 0\), else log(Y)

    1. least squares

    • inversion of pxp matrix ~O(p^3)

    • regression effect - things tend to the mean (ex. bball children are shorter)

    • in high dims, l2 worked best

    1. kernel smoothing + lowess

    • can find optimal bandwidth

    • nadaraya-watson kernel smoother - locally weighted scatter plot smoothing

      • $\(g_h(x) = \frac{\sum K_h(x_i - x) y_i}{\sum K_h (x_i - x)}\)$ where h is bandwidth

    • loess - multiple predictors / lowess - only 1 predictor

      • also called local polynomial smoother - locally weighted polynomial

      • take a window (span) around a point and fit weighted least squares line to that point

      • replace the point with the prediction of the windowed line

      • can use local polynomial fits rather than local linear fits

    1. silhouette plots - good clusters members are close to each other and far from other clustersf

      1. popular graphic method for K selection

      2. measure of separation between clusters \(s(i) = \frac{b(i) - a(i)}{max(a(i), b(i))}\)

      3. a(i) - ave dissimilarity of data point i with other points within same cluster

      4. b(i) - lowest average dissimilarity of point i to any other cluster

      5. good values of k maximize the average silhouette score

    2. lack-of-fit test - based on repeated Y values at same X values

4.2.3. imbalanced data

  1. randomly oversample minority class

  2. randomly undersample majority class

  3. weighting classes in the loss function - more efficient, but requires modifying model code

  4. generate synthetic minority class samples

    1. smote (chawla et al. 2002) - interpolate betwen points and their nearest neighbors (for minority class) - some heuristics for picking which points to interpolatesmote

      1. adasyn (he et al. 2008) - smote, generate more synthetic data for minority examples which are harder to learn (number of samples is proportional to number of nearby samples in a different class)

    2. smrt - generate with vae

  5. selectively removing majority class samples

    1. tomek links (tomek 1976) - selectively remove majority examples until al lminimally distanced nearest-neighbor pairs are of the same class

    2. near-miss (zhang & mani 2003) - select samples from the majority class which are close to the minority class. Example: select samples from the majority class for which the average distance of the N closest samples of a minority class is smallest

    3. edited nearest neighbors (wilson 1972) - “edit” the dataset by removing samples that don’t agree “enough” with their neighborhood

  6. feature selection and extraction

    1. minority class samples can be discarded as noise - removing irrelevant features can reduce this risk

    2. feature selection - select a subset of features and classify in this space

    3. feature extraction - extract new features and classify in this space

    4. ideas

      1. use majority class to find different low dimensions to investigate

      2. in this dim, do density estimation

      3. residuals - iteratively reweight these (like in boosting) to improve performance

  7. incorporate sampling / class-weighting into ensemble method (e.g. treat different trees differently)

    1. ex. undersampling + ensemble learning (e.g. IFME, Becca’s work)

  8. algorithmic classifier modifications

  9. misc papers

    1. enrichment (jegierski & saganowski 2019) - add samples from an external dataset

  10. ref

  11. imblanced-learn package with several methods for dealing with imbalanced data

  12. good blog post

  13. Learning from class-imbalanced data: Review of methods and applications (Haixiang et al. 2017)

  14. sample majority class w/ density (to get best samples)

  15. log-spline - doesn’t scale

4.2.4. missing-data imputation

  • Missing value imputation: a review and analysis of the literature (lin & tsai 2019)

  • Purposeful Variable Selection and Stratification to Impute Missing FAST Data in Trauma Research (fuchs et al. 2014)

  • Causal Inference: A Missing Data Perspective (ding & li, 2018)

  • different missingness mechanisms (little & rubin, 1987)

    • MCAR - missing completely at random - no relationship between the missingness of the data and any values, observed or missing

    • MAR - missing at random - propensity of missing values depends on observed data, but not the missing data

      • can easily test for this vs MCAR

    • MNAR - missing not at random - propensity of missing values depends both on observed and unobserved data

    • connections to causal: MCAR is much like randomization, MAR like ignorability (although slightly more general), and MNAR like unmeasured unconfounding

  • imputation problem: propensity of missing values depends on the unobserved values themselves (not ignorable)

    • simplest approach: drop rows with missing vals

    • mean/median imputation

    • probabilistic approach

      • EM approach, MCMC, GMM, sampling

    • matrix completion: low-rank, PCA, SVD

    • nearest-neighbor / matching: hot-deck

    • (weighted) prediction approaches

      • linear regr, LDA, naive bayes, regr. trees, LDA

      • can do weighting using something similar to inverse propensities, although less common to check things like covariate balance

    • multiple imputation: impute multiple times to get better estimates

      • MICE (passes / imputes data multiple times sequentially)

  • can perform sensitivity analysis to evaluate the assumption that things are not MNAR

    • two standard models for nonignorable missing data are the selection models and the pattern-mixture models (Little and Rubin, 2002, Chapter 15)

  • performance evaluation

    • acc at finding missing vals

    • acc in downstream task

4.2.5. feature engineering

4.2.6. principles breiman

  • conversation

    • moved sf -> la -> caltech (physics) -> columbia (math) -> berkeley (math)

    • info theory + gambling

    • CART, ace, and prob book, bagging

    • ucla prof., then consultant, then founded stat computing at berkeley

    • lots of cool outside activities

      • ex. selling ice in mexico

  • 2 cultures paper

    1. generative - data are generated by a given stochastic model

    • stat does this too much and needs to move to 2

    • ex. assume y = f(x, noise, parameters)

    • validation: goodness-of-fit and residuals

    1. predictive - use algorithmic model and data mechanism unknown

    • assume nothing about x and y

    • ex. generate P(x, y) with neural net

    • validation: prediction accuracy

    • axioms

      1. Occam

      2. Rashomon - lots of different good models, which explains best?

      • ex. rf is not robust at all

      1. Bellman - curse of dimensionality

      • might actually want to increase dimensionality (ex. svms embedded in higher dimension)

    • industry was problem-solving, academia had too much culture box + tukey

  • questions

    1. what points are relevant and irrelevant today in both papers?

    • relevant

      • box

        • thoughts on scientific method

        • solns should be simple

        • necessity for developing experimental design

        • flaws (cookbookery, mathematistry)

      • tukey

        • separating data analysis and stats

        • all models have flaws

        • no best models

        • lots of goold old techniques (e.g. LSR)

    • irrelevant

      • some of the data techniques (I think)

      • tukey multiple-response data has been better attacked (graphical models)

    1. how do you think the personal traits of Tukey and Box relate to the scientific opinions expressed in their papers?

    • probably both pretty critical of the science at the time

    • box - great respect for Fisher

    • both very curious in different fields of science

    1. what is the most valuable msg that you get from each paper?

    • box - data analysis is a science

    • tukey - models must be useful

      • no best models

      • find data that is useful

      • no best models

  • box_79 “science and statistics”

    • scientific method - iteration between theory and practice

      • learning - discrepancy between theory and practice

      • solns should be simple

    • fisher - founder of statistics (early 1900s)

      • couples math with applications

      • data analysis - subiteration between tentative model and tentative analysis

      • develops experimental design

    • flaws

      • cookbookery - forcing all problems into 1 or 2 routine techniques

      • mathematistry - development of theory for theory’s sake

  • tukey_62 “the future of data analysis”

    • general considerations

      • data analysis - different from statistics, is a science

      • lots of techniques are very old (LS - Gauss, 1803)

      • all models have flaws

      • no best models

      • must teach multiple data analysis methods

    • spotty data - lots of irregularly non-constant variability

      • could just trim highest and lowest values

        • winzorizing - replace suspect values with closest values that aren’t

      • must decide when to use new techniques, even when not fully understood

      • want some automation

      • FUNOP - fulll normal plot

        • can be visualized in table

    • spotty data in more complex situations


    • multiple-response data

      • understudied except for factor analysis

      • multiple-response procedures have been modeled upon how early single-response procedures were supposed to have been used, rather than upon how they were in fact used

      • factor analysis

        1. reduce dimensionality with new coordinates

        2. rotate to find meaningful coordinates

        • can use multiple regression factors as one factor if they are very correlated

      • regression techniques always offer hopes of learning more from less data than do variance-component techniques

    • flexibility of attack

      • ex. what unit to measure in models

  • normative - fully interpretable + modelled

    • idealized

    • probablistic

  • ~mechanistic - somewhere in between

  • descriptive - based on reality

    • empirical exaggerated claims

  • video by Rob Kass

  • concepts are ambiguous and have many mathematical instantiations

    • e.g. “central tendency” can be mean or median

    • e.g. “information” can be mutual info (reduction in entropy) or squared correlation (reduction in variance)

    • e.g. measuring socioeconomic status and controlling for it

  • regression “when controlling for another variable” makes causal assumptions

    • must make sure that everything that could confound is controlled for

  • Idan Segev: “modeling is the lie that reveals the truth”

    • picasso: “art is the lie that reveals the truth”

  • box: “all models are wrong but some are useful” - statistical pragmatism

    • moves from true to useful - less emphasis on truth

    • “truth” is contingent on the purposes to which it will be put

  • the scientific method aims to provide explanatory models (theories) by collecting and analyzing data, according to protocols, so that

    • the data provide info about models

    • replication is possible

    • the models become increasingly accurate

  • scientific knowledge is always uncertain - depends on scientific method