Transcript Slide 1

Tackling Lack of
Determination
Douglas M. Hawkins
Jessica Kraker
School of Statistics
University of Minnesota
NISS Metabolomics, Jul 15, 2005
The Modeling Problem
• We have a dependent variable y, which is
categorical; numeric; or binary.
• We have p ‘predictors’ of ‘features’ x
• We seek relationship between x and y
– so we can predict future y values
– to understand ‘mechanism’ of x driving y
• We have n ‘cases’ to fit and diagnose
model, giving n by p+1 data array
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Classic Approaches
• Linear regression model. Apart from error
y = S j bj x j
Generalized additive model / Neural Net
y = S j gj (xj )
Generalized linear model
y = g( Sj bj xj )
Nonlinear models, Recursive partitioning
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Classical Setup
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•
•
•
Number of features p is small.
Number of cases n is much larger.
Diagnosis, fitting, verification fairly easy.
Ordinary/weighted least squares, GLM,
GAM, Neural net straightforward
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The Evolving Setup
• Huge numbers of features p,
• Modest sample size n, giving rise to
n<<p problem, seen in
– molecular descriptors QSAR
– microarrays
– spectral data
– and now metabolomics
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Implications
• Detailed model checking (linearity,
scedasticity) much harder,
• If even simple models (eg linear) are hard,
more complex ones (eg nonlinear) much
harder.
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Linear Model Paradox
• The larger p, the less you believe linear
normal regression model.
• But simple linear is surprisingly sturdy.
– is best for linear homoscedastic
– is OK with moderate heteroscedasticity
– works for generalized linear model
– ‘Street smarts’ like log transforming badly
skew features take care of much nonlinearity.
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Leading You to Idea
• Using linear models is smart, even if for no
more than benchmark of other methods.
So we concentrate on fitting the linear
model
y = Sj bj xj = bTx in vector/matrix form
• Standard criterion is ordinary least
squares (OLS), minimizing
– S = Si (yi – bTxi)2
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Linear Models with n<<p
• Classical OLS regression fails if n<p+1
(the ‘undetermined’ setting).
• Even if n is large enough, linearly related
predictors create headache (different b
vectors give same predictions.)
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Housekeeping Preliminary
• Many methods are scale-dependent. You
want to treat all features alike.
• To do this, ‘autoscale’ each feature.
Subtract its average over all cases, and
divide by the standard deviation over all
cases.
• Some folks also autoscale y; some do not.
Either way works.
NISS Metabolomics, Jul 15, 2005
Solutions Proposed
• Dimension reduction approaches:
– Principal Component Regression (PCR)
replaces p features by k<<p linear
combinations that it hopes capture all relevant
information in the features.
– Partial Least Squares / Projection to Latent
Spaces (PLS) uses k<<p linear combinations
of features. Unlike PCR, these are found
looking at y as well as x.
NISS Metabolomics, Jul 15, 2005
Variable Selection
• Feature selection (eg stepwise regression)
seeks handful of relevant features, keeps
them, tosses all others.
– Or, we can think, keeps all predictors, but
forces ‘dropped’ ones to have b = 0.
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Evaluation
• Variable subset selection is largely
discredited. Overstates value of retained
predictors; eliminates potentially useful
ones.
• PCR is questionable. No law of nature
says its first few variables capture the
dimensions relevant to predicting y
• PLS is effective; computationally fast.
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Regularization
• These methods keep all predictors, retain
least squares criterion, ‘tame’ fitted model
by ‘imposing a charge’ on coefficients.
• Particular cases
– ridge charges by square of the coefficient
– lasso charges by absolute value of coefficient.
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Regularization Criteria
•
Ridge: Minimize
S + l Sj b2j
• Lasso: Minimize
S + m S j | bj |
where l, m are the ‘unit prices’ charged for a
unit increase in the coefficient’s square
or absolute value.
NISS Metabolomics, Jul 15, 2005
Qualitative Behavior - Ridge
• Ridge, lasso both ‘shrinkage estimators’.
The larger the unit price of coefficient, the
smaller the coefficient vector overall.
• Ridge shrinks smoothly toward zero.
Usually coefficients stay non-zero.
NISS Metabolomics, Jul 15, 2005
Qualitative Behavior - Lasso
• Lasso gives ‘soft thresholding’. As unit
price increases, more and more
coefficients become zero
• For large m all coefficients will be zero; there
is no model
• The lasso will never have more than n
non-zero coefficients (so can be thought of
as giving feature selection.)
NISS Metabolomics, Jul 15, 2005
Correlated predictors
• Ridge, lasso very different with highly
correlated predictors. If y depends on x
through some ‘general factor’
– Ridge keeps all predictors, shrinking them
– Lasso finds one representative, drops
remainder.
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Example
• Little data set. A general factor involves
features x1, x2 x3, x5 while x4 is
uncorrelated. The dependent y involves
the general factor and x4.
Here are the traces of the 5 fitted
coefficients as functions of l (ridge) and m
(lasso)
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Correlated predictors - Ridge
0.6
0.4
x1
x2
x4
x5
0.2
x6
0.0
0
50
100
lambda
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150
Correlated predictors - Lasso
0.6
0.4
x1
x2
x4
x5
0.2
x6
0.0
0
3
20
60
100
140
mu
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180
200
Comments - Ridge
• Note that ridge is egalitarian; it spreads
the predictive work pretty evenly between
the 4 related factors.
• Although all coefficients go to zero, they
do so slowly.
NISS Metabolomics, Jul 15, 2005
Comments - Lasso
• Lasso coefficients piecewise constant, so
look only at m where coefficients change.
• Coefficients decrease overall as m goes
up; individual coefficients can increase.
• General factor term coeffs do not
coalesce; x6 carries can for them all.
• Note occasions where coeff increases
when m increases.
NISS Metabolomics, Jul 15, 2005
Elastic Net
• Combining ridge and lasso using criterion
S + l Sj b2j + m Sj |bj |
gives the ‘Elastic Net’.
• More flexible than either ridge or lasso;
has strengths of both.
• For general idea, same example, l=20,
here are coeffs as function of m. Note
smooth near-linear decay to zero.
NISS Metabolomics, Jul 15, 2005
Elastic; lambda=20
0.28
0.21
x1
x2
0.14
x3
x4
x5
0.07
0.00
0
50
100
mu
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150
200
Finding Constants
• Ridge, Lasso, Elastic Net need choices of
l, m. Commonly done with cross-validation
– randomly split data into 10 groups.
– Analyze full data set.
– Do 10 analyses in which one group is held
out, and predicted from the remaining 9.
– Pick the l, m. minimizing prediction sum of
squares
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Verifying Model
• Use a double-cross validation
– Hold out one tenth of sample
– Apply cross-validation to remaining ninetenths to pick a l, m
– Predict hold-out group
– Repeat for all 10 holdout groups
– Get prediction sum of squares
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(If-Pigs-Could-Fly Approach)
• (If you have a huge value of n you can de
novo split sample into a learning portion
and a validation portion; fit the model to
the learning portion, check it on the
completely separate validation portion.
• This may give high comfort level, but is an
inefficient use of limited sample.
• Inevitably raises suspicion you carefully
picked halves that support hypothesis.)
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Diagnostics
• Regression case diagnostics involve:
– Influence: How much to answers change if
this case is left out
– Outliers: Is this case compatible with model
fitting the remaining cases.
• Tempting to throw up hands when n<<p,
but deletion diagnostics, studentized
residuals still available, still useful
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Robustification
• If outliers are a concern, potentially go to
L1 norm. For robust elastic net minimize
Si |yi - bTxi| + l Sj b2j + m Sj |bj |
• This protects against regression outliers
on low-leverage cases; still has decent
statistical efficiency.
• Unaware of publicly-available code that
does this.
NISS Metabolomics, Jul 15, 2005
Imperfect Feature Data
• A final concern is feature data. Features
form matrix X of order n x p.
• Potential problems are:– Some entries may be missing,
– Some entries may be below detection limit,
– Some entries may be wrong, potentially
outlying.
NISS Metabolomics, Jul 15, 2005
Values Below Detection Limit
• Often, no harm replacing values below
detection limit by the detection limit.
• If features are log-transformed, this can
become flakey.
• For a thorough analysis, use E-M (see
rSVD below); replace BDL by the smaller
of imputed value and detection limit.
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Missing Values
• are a different story; do not confuse BDL
with missing.
• Various imputation methods available;
tend to assume some form of ‘missing at
random’.
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Singular Value Decomposition
• We have had good results using singular
value decomposition
X = G HT + E
where matrix G are ‘row markers’, H are
‘column markers’, E is an error matrix.
• You keep k<min(n,p) columns in G, H (but
be careful to keep ‘enough’ columns; recall
warning about PCR.)
NISS Metabolomics, Jul 15, 2005
Robust SVD
• SVD is entirely standard, classical; the
matrix G is the matrix of principal
components.
• Robust SVD differs in two ways:
– alternating fit algorithm accommodates
missing values,
– robust criterion resists outlying entries in X
NISS Metabolomics, Jul 15, 2005
Use of rSVD
• The rSVD has several uses:
– Gives way to get PCs (columns of G) despite
missing information and/or outliers.
– GHT gives ‘fitted values’ you can use as fill-ins
for missing values in X.
– E is matrix of residuals. A histogram can flag
apparently outlier cells for diagnosis. Maybe
replace outliers by fitted values or winsorize
NISS Metabolomics, Jul 15, 2005
SVD and Spectra
• A special case is where features are a
function (eg spectral data). So think xit
where i is sample and t is ‘time’.
• Logic says finer resolution adds
information, should give better answers.
• Experience says finer resolution dilutes
signal, adds noise, raises overfitting
concern.
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Functional Data Analysis
• Functions are to some degree smooth.
t, t-h, t+h ‘should’ give similar x.
• Approaches – pre-process x – smoothing,
peakhunting etc.
• Another approach – use modeling
methods that reflect smoothness.
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Example: regression
• Instead of plain OLS, use criterion like
S + S f (bt-1-2bt+bt+1)2 (S is sum of squares)
f is a smoothness penalty.
• The same idea carries over to SVD, where
we want our principal components to be
suitably smooth functions of t.
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Summary
• Linear modeling methods remain valuable
tools in the analysis armory
• Several current methods are effective, and
have theoretical support.
• The least-squares-with-regularization
methods are effective, even in the n<<p
setting, and involve tolerable computation.
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Some references
Cook, R.D., and Weisberg, S. (1999). Applied Regression Including Computing and Graphics, John Wiley & Sons Inc.: New York.
Dobson, A. J. (1990). An Introduction to Generalized Linear Models, Chapman and Hall: London.
Li, K-C. and Duan, N., (1989), “Regression analysis under link violation”, Annals of Statistics, 17, 1009-1052.
St. Laurent, R.T., and Cook, R.D. (1993). “Leverage, Local Influence, and Curvature in Nonlinear Regression”, Biometrika, 80, 99-106.
Wold, S. (1993). “Discussion: PLS in Chemical Practice”, Technometrics, 35, 136-139.
Wold, H. (1966). “Estimation of Principal Components and Related Models by Iterative Least Squares”, in Multivariate Analysis, ed. P.R.
Krishnaiah, Academic Press: New York, 391-420.
Rencher, A.C., and Pun, F. (1980). “Inflation of R2 in Best Subset Regression”, Technometrics, 22, 49-53. .
Miller, A. J. (2002). Subset Selection in Regression, 2nd ed., Chapman and Hall: London.
Tibshirani, R. (1996). “Regression Shrinkage and Selection via the LASSO”, J. R. Statistical Soc. B, 58, 267-288.
Zou, H., and Hastie, T. (2005). “Regularization and Variable Selection via the Elastic Net”, J. R. Statistical Soc. B, 67, 301-320.
Shao, J. (1993). “Linear Model Selection by Cross-Validation”, Journal of the American Statistical Association, 88, 486-494.
Hawkins, D.M., Basak, S.C., and Mills, D. (2003). “Assessing Model Fit by Cross-Validation”, Journal of Chemical Information and Computer
Sciences, 43, 579-586.
Walker, E., and Birch, J.B. (1988). “Influence Measures in Ridge Regression”, Technometrics, 30, 221-227.
Efron, B. (1994). “Missing Data, Imputation, and the Bootstrap”, Journal of the American Statistical Association, 89, 463-475.
Rubin, D.B. (1976). “Inference and Missing Data”, Biometrika, 63, 581-592.
Liu, L., Hawkins, D.M., Ghosh, S., and Young, S.S. (2003). “Robust Singular Value Decomposition Analysis of Microarray Data”, Proceeding of
the National Academy of Sciences, 100, 13167-13172.
Elston, D. A., and Proe, M.F. (1995). “Smoothing Regression Coefficients in an Overspecified Regression Model with Interrelated Explanatory
Variables”, Applied Statistics, 44, 395-406.
Tibshirani, R., Saunders, M., Rosset, S., Zhi, J, and Knight, K., (2005), “Sparsity and smoothness via the fused lasso”, , J. R. Statistical Soc. B,
67, 91-108.
Ramsay, J. O. , and Silverman, B. W. (2002), ``Applied functional data analysis: methods and case studies'', Springer-Verlag Inc (Berlin; New
York)
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