Package 'lars'

Computes K-fold cross-validated error curve for lars

Description

Computes the K-fold cross-validated mean squared prediction error for lars, lasso, or forward stagewise.

Usage

cv.lars(x, y, K = 10, index, trace = FALSE, plot.it = TRUE, se = TRUE,
        type = c("lasso", "lar", "forward.stagewise", "stepwise"),
        mode=c("fraction", "step"), ...)

Arguments

x	Input to lars
y	Input to lars
K	Number of folds
index	Abscissa values at which CV curve should be computed. If mode="fraction" this is the fraction of the saturated |beta|. The default value in this case is index=seq(from = 0, to = 1, length =100). If mode="step", this is the number of steps in lars procedure. The default is complex in this case, and depends on whether N>p or not. In principal it is index=1:p. Users can supply their own values of index (with care).
trace	Show computations?
plot.it	Plot it?
se	Include standard error bands?
type	type of lars fit, with default "lasso"
mode	This refers to the index that is used for cross-validation. The default is "fraction" for type="lasso" or type="forward.stagewise". For type="lar" or type="stepwise" the default is "step"
...	Additional arguments to lars

Value

Invisibly returns a list with components (which can be plotted using plotCVlars)

index	As above
cv	The CV curve at each value of index
cv.error	The standard error of the CV curve
mode	As above

Author(s)

Trevor Hastie

References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics; see also https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf.

Examples

data(diabetes)
attach(diabetes)
cv.lars(x2,y,trace=TRUE,max.steps=80)
detach(diabetes)

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Blood and other measurements in diabetics

Description

The diabetes data frame has 442 rows and 3 columns. These are the data used in the Efron et al "Least Angle Regression" paper.

Format

This data frame contains the following columns:

x

a matrix with 10 columns

y

a numeric vector

x2

a matrix with 64 columns

Details

The x matrix has been standardized to have unit L2 norm in each column and zero mean. The matrix x2 consists of x plus certain interactions.

Source

https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf

References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics

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Fits Least Angle Regression, Lasso and Infinitesimal Forward Stagewise regression models

Description

These are all variants of Lasso, and provide the entire sequence of coefficients and fits, starting from zero, to the least squares fit.

Usage

lars(x, y, type = c("lasso", "lar", "forward.stagewise", "stepwise"), 
    trace = FALSE, normalize = TRUE, intercept = TRUE, Gram, eps = 1e-12,
     max.steps, use.Gram = TRUE)

Arguments

x	matrix of predictors
y	response
type	One of "lasso", "lar", "forward.stagewise" or "stepwise". The names can be abbreviated to any unique substring. Default is "lasso".
trace	If TRUE, lars prints out its progress
normalize	If TRUE, each variable is standardized to have unit L2 norm, otherwise it is left alone. Default is TRUE.
intercept	if TRUE, an intercept is included in the model (and not penalized), otherwise no intercept is included. Default is TRUE.
Gram	The X'X matrix; useful for repeated runs (bootstrap) where a large X'X stays the same.
eps	An effective zero, with default 1e-12. If lars() stops and reports NAs, consider increasing this slightly.
max.steps	Limit the number of steps taken; the default is 8 * min(m, n-intercept), with m the number of variables, and n the number of samples. For type="lar" or type="stepwise", the maximum number of steps is min(m,n-intercept). For type="lasso" and especially type="forward.stagewise", there can be many more terms, because although no more than min(m,n-intercept) variables can be active during any step, variables are frequently droppped and added as the algorithm proceeds. Although the default usually guarantees that the algorithm has proceeded to the saturated fit, users should check.
use.Gram	When the number m of variables is very large, i.e. larger than N, then you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE.

Details

LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the "lasso" option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. A "stepwise" option has recently been added to LARS.

Value

A "lars" object is returned, for which print, plot, predict, coef and summary methods exist.

Author(s)

Brad Efron and Trevor Hastie

References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics doi:10.1214/009053604000000067; see also https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.

See Also

print, plot, summary and predict methods for lars, and cv.lars

Examples

data(diabetes)
par(mfrow=c(2,2))
attach(diabetes)
object <- lars(x,y)
plot(object)
object2 <- lars(x,y,type="lar")
plot(object2)
object3 <- lars(x,y,type="for") # Can use abbreviations
plot(object3)
detach(diabetes)

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Plot method for lars objects

Description

Produce a plot of a lars fit. The default is a complete coefficient path.

Usage

## S3 method for class 'lars'
plot(x, xvar= c("norm", "df", "arc.length", "step"), breaks = TRUE,
 plottype = c("coefficients", "Cp"), omit.zeros = TRUE, eps = 1e-10, ...)

Arguments

x	lars object
xvar	The type of x variable against which to plot. xvar=norm (default) plots against the L1 norm of the coefficient vector, as a fraction of the maximal L1 norm. xvar=step plots against the step number (which is essentially degrees of freedom for LAR; not for LASSO or Forward Stagewise). xvar=arc.length plots against the arc.length of the fitted vector; this is useful for a LAR object, because the L1 norm of its coefficient vector need not be monotone in the steps. xvar=df plots against the estimated df, which is the size of the active set at each step.
breaks	If TRUE, then vertical lines are drawn at each break point in the piecewise linear coefficient paths
plottype	Either coefficients (default) or Cp. The coefficient plot shows the path of each coefficient as a function of the norm fraction or Df. The Cp plot shows the Cp curve.
omit.zeros	When the number of variables is much greater than the number of observations, many coefficients will never be nonzero; this logical (default TRUE) avoids plotting these zero coefficents
eps	Definition of zero above, default is 1e-10
...	Additonal arguments for generic plot. Can be used to set xlims, change colors, line widths, etc

Details

The default plot uses the fraction of L1 norm as the xvar. For forward stagewise and LAR, coefficients can pass through zero during a step, which causes a change of slope of L1 norm vs arc-length. Since the coefficients are piecewise linear in arc-length between each step, this causes a change in slope of the coefficients.

Value

NULL

Author(s)

Trevor Hastie

References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics; see also https://hastie.su.domains/Papers/LARS/LeastAngle_2002.pdf. Yann-Ael Le Borgne (private communication) pointed out the problems in plotting forward stagewise and LAR coefficients against L1 norm, and the solution we have implemented.

Examples

data(diabetes)
attach(diabetes)
object <- lars(x,y)
plot(object)
detach(diabetes)

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Make predictions or extract coefficients from a fitted lars model

Description

While lars() produces the entire path of solutions, predict.lars allows one to extract a prediction at a particular point along the path.

Usage

## S3 method for class 'lars'
predict(object, newx, s, type = c("fit", "coefficients"), mode = c("step", 
    "fraction", "norm", "lambda"), ...)
## S3 method for class 'lars'
coef(object, ...)

Arguments

object	A fitted lars object
newx	If type="fit", then newx should be the x values at which the fit is required. If type="coefficients", then newx can be omitted.
s	a value, or vector of values, indexing the path. Its values depends on the mode= argument. By default (mode="step"), s should take on values between 0 and p (e.g., a step of 1.3 means .3 of the way between step 1 and 2.)
type	If type="fit", predict returns the fitted values. If type="coefficients", predict returns the coefficients. Abbreviations allowed.
mode	Mode="step" means the s= argument indexes the lars step number, and the coefficients will be returned corresponding to the values corresponding to step s. If mode="fraction", then s should be a number between 0 and 1, and it refers to the ratio of the L1 norm of the coefficient vector, relative to the norm at the full LS solution. Mode="norm" means s refers to the L1 norm of the coefficient vector. Mode="lambda" uses the lasso regularization parameter for s; for other models it is the maximal correlation (does not make sense for lars/stepwise models). Abbreviations allowed.
...	Any arguments for predict.lars should work for coef.lars

Details

LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the "lasso" option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit.

Value

Either a vector/matrix of fitted values, or a vector/matrix of coefficients.

Author(s)

Trevor Hastie

References

Efron, Hastie, Johnstone and Tibshirani (2002) "Least Angle Regression" (with discussion) Annals of Statistics; see also doi:10.1214/009053604000000067. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.

See Also

print, plot, lars, cv.lars

Examples

data(diabetes)
attach(diabetes)
object <- lars(x,y,type="lasso")
### make predictions at the values in x, at each of the
### steps produced in object
fits <- predict.lars(object, x, type="fit")
### extract the coefficient vector with L1 norm=4.1
coef4.1 <- coef(object, s=4.1, mode="norm") # or
coef4.1 <- predict(object, s=4.1, type="coef", mode="norm")
detach(diabetes)

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Summary method for lars objects

Description

Produce an anova-type summary for a lars object.

Usage

## S3 method for class 'lars'
summary(object, sigma2=NULL, ...)

Arguments

object	lars object
sigma2	optional variance measure (for p>n)
...	Additional arguments for summary generic

Details

An anova summary is produced, with Df, RSS and Cp for each step. Df is tricky for some models, such as forward stagewise and stepwise, and is not likely to be accurate. When p>n, the user is responsible for supplying sigma2.

Value

An anova object is returned, with rownames the step number, and with components:

Df	Estimated degree of freedom
Rss	The Residual sum of Squares
Cp	The Cp statistic

Author(s)

Brad Efron and Trevor Hastie

References

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics; see also doi:10.1214/009053604000000067. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.

See Also

lars, and print, plot,and predict methods for lars, and cv.lars

Examples

data(diabetes)
attach(diabetes)
object <- lars(x,y)
summary(object)
detach(diabetes)

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