Lecture 14: Penalized Regression II

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Penalized Regression, Part 2
Penalized Regression
Recall in penalized regression, we re-write our loss function to
include not only the squared error loss but a penalty term
M q   L q x    p q 
Our goal then becomes to minimize our a loss function (i.e. SS)
In the regression setting we can write M(q ) in terms of our
regression parameters b as follows
M  b    y  Xb   y  Xb    p  b 
'
The penalty function takes the form
p  b    j 1 b j , for q  0
r
q
Ridge Regression
Last class we discussed ridge regression as an
alternative to OLS when covariates are collinear
Ridge regression can reduce the variability and improve
accuracy of a regression model
However, there is not a means of variable selection in
ridge regression
Ideally we want to be able to reduce the variability in a
model but also be able to select which variables are
most strongly associated with our outcome
The Lasso versus Ridge Regression
In ridge regression, the new function is

n
i i

Yi  b0   j 1 X ij b j
p

2
  j 1 b j2
p
Consider instead the estimator which minimizes

n
i i

Yi  b0   j 1 X ij b j
p

2
  j 1 b j
p
The only change is to the penalty function and while the change
is subtle, is has a big impact on our regression estimator
The Lasso
The name lasso stands for “Least Absolute Shrinkage and
Selection Operator”
Like ridge regression, penalizing the absolute values of the
coefficients shrinks them towards zero
But in the lasso, some coefficients are shrunk completely to
zero
Solutions where multiple coefficient estimates are
identically zero are called sparse
Thus the penalty performs a continuous variable selection,
hence the name
Geometry of Ridge versus Lasso
2-dimensional case
b2
b2
βˆ
βˆ
b1
2
2
Solid areas represent the constraint regions b1  b2  t & b1  b2  t
The ellipses represent the contours of the least square error function
b1
The Lasso
Because the lasso penalty has an absolute value
operation, the objective function is not differentiable
and therefore lacks a closed form
As a result, we must use optimization algorithms to find
the minimum
Examples of these algorithms include
-Quadratic programming (limit ~100 predictors)
-Least Angle Regression/LAR (limit ~10,000 predictors)
Selection of 
Since lasso is not a linear estimator, we have no H matrix such
that yˆ  Hy
Thus determining the degrees of freedom are more difficult to
estimate
One means is to estimate the degrees of freedom based on the
number of non-zero parameters in the model and then use
AIC, BIC or Cp to select the best 
Alternatively (and often more preferred) we could select  via
cross-validation
Forward Stagewise Selection
Alternative method for variable subset selection designed to
handle correlated predictors
Iterative process that begins with all coefficients equal to zero
and build regression function in successive small steps
Similar algorithm to forward selection in that predictors added
successively to the model
However, it is much more cautious than forward stepwise model
selection
-e.g. for a model with 10 possible predictors stepwise takes 10 steps at
most, stagewise may take 5000+
Forward Stagewise Selection
Stagewise algorithm:
(1) Initialize model such that
βˆ  0  ˆ  y  0
and r  y  y
(2) Find the predictor Xj1 that is most correlated with r and
add it to the model (here j1  arg max j cˆ j )
(3) Update bˆ j  bˆ j   j where  j  h  sign  cˆ j 
-Note, h is a small constant controlling step-length
1
1
1
1
1
(4) Update μˆ  μˆ   j X j and r  r   j X j
1
1
(5) Repeat steps 2 thru 4 until cˆ  0
1
1
Stagewise versus Lasso
Although the algorithms look entirely different, their results are
very similar!
They will trace very similar paths for addition of predictors to the
model
They both represent special cases of a method called least angle
regression (LAR)
Least Angle Regression
LAR algorithm:
(1) Initialize model such that βˆ  0  ˆ  y  0 and r  y  y
Also initialize an empty “active set” A (subset of indices)
(2) Find the predictor X j1 that is most correlated with r where
j1  arg max j cˆ j ; update the active set to include X j  A1   X j 
1
1
(3) Move bˆ j toward sign  cˆ j  until some other covariate X j has the
same correlation with r that bˆ j does. Update the active set to
include X j2  A2  X j1 , X j2
1
1


2
1
(4) Update r and move along  bˆ j , bˆ j  towards the joint OLS
direction for the regression of r on  X j , X j  until a third covariate
X j2 is as correlated with r as the first two predictors. Update the
active set to include X j  A3   X j , X j , X j 
1
2
1
3
1
2
2
3
(5) Continue until all k covariates have been added to the model
In Pictures
Consider a case where we have 2 predictors…
x2
x2
y2
yˆ 2
yˆ 0
yˆ 1
x1
y1
Efron et al. 2004
Relationship Between LAR and Lasso
LAR is a more general method than lasso
A modification of the LAR algorithm produces the entire lasso
path for  varied from 0 to infinity
Modification occurs if a previously non-zero coefficient
estimated to be zero at some point in the algorithm
If this occurs, the LAR algorithm is modified such that the
coefficient is removed from the active set and the joint
direction is recomputed
This modification is the most frequently implements version of
LAR
Relationship Bt/ LAR and Stagewise
LAR is also a more general method than stagewise selection
Can also reproduce stagewise results using modified LAR
Start with the LAR algorithm and determine the best direction at
each stage
If the direction for any predictor in the active set doesn’t agree
in sign with the correlation between r and Xj, adjust to move
in the direction of corr(r, Xj)
As step sizes go to 0, we get a modified version of the LAR
algorithm
Summary of the Three Methods
• LARS
– Uses least square directions in the active set of variables
• Lasso
– Uses the least square directions
– If the variable crosses 0, it is removed from the active set
• Forward stagewise
– Uses non-negative least squares directions in the active set
Degrees Freedom in LAR and lasso
Consider fitting a LAR model with k < p parameters
Equivalently use a lasso bound t that constrains the full
regression fit
General definition for the effective degrees of freedom (edf) for
an adaptively fit model:
df  yˆ    2  i 1 Cov  yˆi , yi 
1
N
For LARS at the kth step, the edf for the fit vector is exactly k
For lasso, at any stage in the fit the effective degrees of freedom
is approximately the number of predictors in the model
Software Packages
What if we consider lasso, forward stagewise, or LAR as
alternatives?
There are 2 packages in R that will allow us to do this
-lars
-glmnet
The lars package has the advantage of being able to fit all
three model types (plus a typical forward stepwise
selection algorithm)
However, the glmnet package can fit lasso regression
models for different types of regression
-linear, logistic, cox-proportional hazards, multinomial, and poisson
Body Fat Example
Recall our regression model
> summary(mod13)
Call:
lm(formula = PBF ~ Age + Wt + Ht + Neck + Chest + Abd + Hip + Thigh + Knee + Ankle + Bicep + Arm + Wrist,
data = bodyfat, x = T)
Coefficients:
Estimate
(Intercept) -18.18849
Age
0.06208
Wt
-0.08844
Ht
-0.06959
Neck
-0.47060
Chest
-0.02386
Abd
0.95477
Hip
-0.20754
Thigh
0.23610
Knee
0.01528
Ankle
0.17400
Bicep
0.18160
Arm
0.45202
Wrist
-1.62064
Std. Error
17.34857
0.03235
0.05353
0.09601
0.23247
0.09915
0.08645
0.14591
0.14436
0.24198
0.22147
0.17113
0.19913
0.53495
t value
-1.048
1.919
-1.652
-0.725
-2.024
-0.241
11.04
-1.422
1.636
0.063
0.786
1.061
2.270
-3.030
Pr(>|t|)
0.29551
0.05618 .
0.09978 .
0.46925
0.04405 *
0.81000
< 2e-16 ***
0.15622
0.10326
0.94970
0.43285
0.28966
0.02410 *
0.00272 **
Residual standard error: 4.305 on 238 degrees of freedom. Multiple R-squared: 0.749,
Adjusted R-squared: 0.7353 . F-statistic: 54.65 on 13 and 238 DF, p-value: < 2.2e-16
Body Fat Example
LAR:
>library(lars)
>par(mfrow=c(2,2))
>object <- lars(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), type="lasso")
>plot(object, breaks=F)
>object2 <- lars(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), type="lar")
>plot(object2, breaks=F)
>object3 <- lars(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), type=“for")
>plot(object3, breaks=F)
>object4 <- lars(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]),
type=“stepwise")
>plot(object4, breaks=F)
Body Fat Example
A closer look at the model:
>object <- lars(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), type="lasso")
> names(object)
[1] "call"
"type"
"df"
"lambda" "R2"
"RSS"
"Cp"
[9] "entry" "Gamrat" "arc.length" "Gram"
"beta"
"mu"
"actions"
"normx" "meanx"
> object$beta
Age
Wt
Ht
Neck
Chest
Abd
Hip
Thigh
Knee
Ankle
0 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.0000000 0.000000000 0.00000000 0.00000000 0.0000000
1 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.5164924 0.000000000 0.00000000 0.00000000 0.0000000
2 0.00000000 0.00000000 -0.04395065 0.00000000 0.00000000 0.5314218 0.000000000 0.00000000 0.00000000 0.0000000
3 0.01710504 0.00000000 -0.13752803 0.00000000 0.00000000 0.5621288 0.000000000 0.00000000 0.00000000 0.0000000
4 0.04880181 0.00000000 -0.15894236 0.00000000 0.00000000 0.6550929 0.000000000 0.00000000 0.00000000 0.0000000
5 0.04994577 0.00000000 -0.15905246 -0.02624509 0.00000000 0.6626603 0.000000000 0.00000000 0.00000000 0.0000000
6 0.06499276 0.00000000 -0.15911969 -0.25799496 0.00000000 0.7079872 0.000000000 0.00000000 0.00000000 0.0000000
7 0.06467180 0.00000000 -0.15921694 -0.26404701 0.00000000 0.7118167 -0.004720494 0.00000000 0.00000000 0.0000000
8 0.06022586 -0.01117359 -0.14998300 -0.29599536 0.00000000 0.7527298 -0.022557736 0.00000000 0.00000000 0.0000000
9 0.05710956 -0.02219531 -0.14039586 -0.32675736 0.00000000 0.7842966 -0.035675017 0.00000000 0.00000000 0.0000000
10 0.05853733 -0.04577935 -0.11203059 -0.39386199 0.00000000 0.8425758 -0.101022340 0.09657784 0.00000000 0.0000000
11 0.06132775 -0.07889636 -0.07798153 -0.45141574 0.00000000 0.9142944 -0.171178163 0.20141924 0.00000000 0.1259630
12 0.06214695 -0.08452690 -0.07220347 -0.46528070 -0.01582661 0.9402896 -0.194491760 0.22553958 0.00000000 0.1586161
13 0.06207865 -0.08844468 -0.06959043 -0.47060001 -0.02386415 0.9547735 -0.207541123 0.23609984 0.01528121 0.1739954
Bicep
Arm
Wrist
0 0.00000000 0.0000000 0.000000
1 0.00000000 0.0000000 0.000000
2 0.00000000 0.0000000 0.000000
3 0.00000000 0.0000000 0.000000
4 0.00000000 0.0000000 -1.169755
5 0.00000000 0.0000000 -1.198047
6 0.00000000 0.2175660 -1.535349
7 0.00000000 0.2236663 -1.538953
8 0.00000000 0.2834326 -1.535810
9 0.04157133 0.3117864 -1.534938
10 0.09096070 0.3635421 -1.522325
11 0.15173471 0.4229317 -1.587661
12 0.17055965 0.4425212 -1.607395
13 0.18160242 0.4520249 -1.620639
Body Fat Example
A closer look at the model:
> names(object)
[1] "call"
"type"
"df"
"lambda" "R2"
"RSS"
"Cp"
"actions"
[9] "entry" "Gamrat" "arc.length" "Gram"
"beta"
"mu"
"normx" "meanx"
> object$df
Intercept
1
12
2
13
3
4
5
6
7
8
9
10
11
14
> object$Cp
0
1
2
3
4
5
6
7
8
9
10
698.4 93.62 85.47 65.41 30.12 30.51 19.39 20.91 18.68 17.41 12.76
11
12
13
10.47 12.06 14.00
Body Fat Example
Glmnet:
>fit<-glmnet(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), alpha=1)
>fit.cv<-cv.glmnet(x=as.matrix(bodyfat[,3:15]), y=as.vector(bodyfat[,2]), alpha=1)
>plot(fit.cv, sign.lambda=-1)
>fit<-glmnet(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), alpha=1,
0.02123575)
Body Fat Example
Glmnet:
>fit<-glmnet(x=as.matrix(bodyfat[,3:15]),y=as.vector(bodyfat[,2]), alpha=1)
>names(fit)
[1] "a0"
"beta"
[10] "offset" "call"
"df"
"dim"
"nobs"
"lambda" "dev.ratio" "nulldev" "npasses" "jerr"
> fit$lambda
[1] 6.793883455 6.190333574 5.640401401 5.139323686 4.682760334 4.266756812 3.887709897
3.542336464 3.227645056
[10] 2.940909965 2.679647629 2.441595119 2.224690538 2.027055162 1.846977168 1.682896807
1.533392893 1.397170495
[19] 1.273049719 1.159955490 1.056908242 0.963015426 0.877463790 0.799512325 0.728485854
0.663769178 0.604801754
[28] 0.551072833 0.502117041 0.457510347 0.416866389 0.379833128 0.346089800 0.315344136
0.287329832 0.261804242
[37] 0.238546274 0.217354481 0.198045308 0.180451508 0.164420694 0.149814013 0.136504949
0.124378225 0.113328806
[46] 0.103260988 0.094087566 0.085729086 0.078113150 0.071173793 0.064850910 0.059089734
0.053840365 0.049057335
[55] 0.044699216 0.040728261 0.037110075 0.033813318 0.030809436 0.028072411 0.025578535
0.023306209 0.021235749
[64] 0.019349224 0.017630292 0.016064066 0.014636978 0.013336669 0.012151876 0.011072337
0.010088701 0.009192449
[73] 0.008375817 0.007631733 0.006953750 0.006335998 0.005773126 0.005260257
Body Fat Example
Glmnet:
>fit.cv<-cv.glmnet(x=as.matrix(bodyfat[,3:15]), y=as.vector(bodyfat[,2]), alpha=1)
> names(fit.cv)
[1] "lambda" "cvm"
"cvsd"
"cvup"
"cvlo"
"nzero" "name"
"glmnet.fit"
[9] "lambda.min" "lambda.1se"
> fit.cv$lambda.min
[1] 0.02123575
Ridge versus Lasso Coefficient Paths
Ridge
Trace Plot
Lasso
LARS
Stagewise
Stepwise
Body Fat Example
Variable
OLS
Ridge
Lasso
Age
0.0635
0.0743
0.0607
Weight
-0.0824
-0.0668
0.0000
Height
-0.2391
-0.0922
-0.2639
Neck
-0.3881
-0.4667
-0.3140
Chest
-0.1321
0.0071
-0.0916
Abdomen
0.9017
0.8703
0.8472
Hip
-0.2129
-0.1750
-0.1408
Thigh
0.2026
0.2301
0.1499
Knee
-0.0082
-0.0108
0.0000
Ankle
-0.0085
0.1374
0.0000
Bicep
0.1902
0.1561
0.1792
Arm
0.1913
0.4329
0.0563
Wrist
-1.6053
-1.6678
-1.5348
If we remove the outliers and clean up the data before analysis…
Variable
OLS
Ridge
Lasso
Age
0.0653
0.0953
0.0473
Weight
-0.0024
0.02867
0.0000
Height
-0.2391
-0.3987
-0.2926
Neck
-0.3881
-0.2171
-0.1379
Chest
-0.1321
0.0643
0.0000
Abdomen
0.9017
0.5164
0.7368
Hip
-0.2129
0.0428
0.0000
Thigh
0.2026
0.1894
0.2710
Knee
-0.0082
0.0584
0.0000
Ankle
-0.0085
-0.1798
0.0000
Bicep
0.1902
0.1436
0.0560
Arm
0.1913
-0.0932
0.0000
Wrist
-1.6053
-1.4160
-1.4049
Body Fat Example
What can we do in SAS?
SAS can also do cross-validation
However, it only fits linear regression
Here’s the basic SAS code
ods graphics on;
proc glmselect data=bf plots=all;
model pbf=age wt ht neck chest abd hip thigh knee ankle
bicep arm wrist/selection=lasso(stop=none choose=AIC);
run;
ods graphics off;
The GLMSELECT Procedure
LASSO Selection Summary
Effect
Effect Number
Step
Entered Removed Effects In
AIC
0
Intercept
1
1
1325.7477
----------------------------------------------------------------------------------------------1
Abd
2
2
1070.4404
2
Ht
3
3
1064.8357
3
Age
4
4
1049.4793
4
Wrist
5
5
1019.1226
5
Neck
6
6
1019.6222
6
Arm
7
7
1009.0982
7
Hip
8
8
1010.6285
8
Wt
9
9
1008.4396
9
Bicep
10
10
1007.1631
10
Thigh
11
11
1002.3524
11
Ankle
12
12
999.8569*
12
Chest
13
13
1001.4229
13
Knee
14
14
1003.3574
Penalized regression methods are most useful when
-high collinearity exists
-when p >> n
Keep in mind you still need to look at the data first
Could also consider other forms of penalized regression,
though in practice alternatives are not used

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