| Title: | Elastic-Net for Sparse Estimation and Sparse PCA |
|---|---|
| Description: | Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA. |
| Authors: | Hui Zou <[email protected]> and Trevor Hastie <[email protected]> |
| Maintainer: | Hui Zou <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3 |
| Built: | 2024-11-13 06:16:06 UTC |
| Source: | CRAN |
Sparse PC by iterative SVD and soft-thresholding
arrayspc(x,K=1,para,use.corr=FALSE, max.iter=200,trace=FALSE,eps=1e-3)arrayspc(x,K=1,para,use.corr=FALSE, max.iter=200,trace=FALSE,eps=1e-3)
x |
The microarray matrix. |
K |
Number of components. Default is 1. |
para |
The thresholding parameters. A vector of length K. |
use.corr |
Perform PCA on the correlation matrix? This option is only effective when the argument type is set "data". |
max.iter |
Maximum number of iterations. |
trace |
If TRUE, prints out its progress. |
eps |
Convergence criterion. |
The function is equivalent to a special case of spca() with the quadratic penalty=infinity. It is specifically designed for the case p>>n, like microarrays.
A "arrayspc" object is returned.
Hui Zou and Trevor Hastie
Zou, H., Hastie, T. and Tibshirani, R. (2006) "Sparse principal component analysis" Journal of Computational and Graphical Statistics, 15 (2), 265–286.
spca, princomp
Computes the K-fold cross-validated mean squared prediction error for elastic net.
cv.enet(x, y, K = 10, lambda, s, mode,trace = FALSE, plot.it = TRUE, se = TRUE, ...)cv.enet(x, y, K = 10, lambda, s, mode,trace = FALSE, plot.it = TRUE, se = TRUE, ...)
x |
Input to lars |
y |
Input to lars |
K |
Number of folds |
lambda |
Quadratic penalty parameter |
s |
Abscissa values at which CV curve should be computed. A value, or vector of values, indexing the path. Its values depends on the mode= argument |
mode |
Mode="step" means the s= argument indexes the LARS-EN step number. 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. Abbreviations allowed. If mode="norm", then s should be the L1 norm of the coefficient vector. If mode="penalty", then s should be the 1-norm penalty parameter. |
trace |
Show computations? |
plot.it |
Plot it? |
se |
Include standard error bands? |
... |
Additional arguments to |
Invisibly returns a list with components (which can be plotted using plotCVLars)
fraction |
Values of s |
cv |
The CV curve at each value of fraction |
cv.error |
The standard error of the CV curve |
Hui Zou and Trevor Hastie
Zou and Hastie (2005) "Regularization and Variable Selection via the Elastic Net" Journal of the Royal Statistical Society, Series B,76,301-320.
data(diabetes) attach(diabetes) ## use the L1 fraction norm as the tuning parameter cv.enet(x2,y,lambda=0.05,s=seq(0,1,length=100),mode="fraction",trace=TRUE,max.steps=80) ## use the number of steps as the tuning parameter cv.enet(x2,y,lambda=0.05,s=1:50,mode="step") detach(diabetes)data(diabetes) attach(diabetes) ## use the L1 fraction norm as the tuning parameter cv.enet(x2,y,lambda=0.05,s=seq(0,1,length=100),mode="fraction",trace=TRUE,max.steps=80) ## use the number of steps as the tuning parameter cv.enet(x2,y,lambda=0.05,s=1:50,mode="step") detach(diabetes)
The diabetes data frame has 442 rows and 3 columns.
These are the data used in the Efron et al "Least Angle Regression" paper.
This data frame contains the following columns:
a matrix with 10 columns
a numeric vector
a matrix with 64 columns
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.
http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.ps
Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics
Starting from zero, the LARS-EN algorithm provides the entire sequence of coefficients and fits.
enet(x, y, lambda, max.steps, normalize=TRUE, intercept=TRUE, trace = FALSE, eps = .Machine$double.eps)enet(x, y, lambda, max.steps, normalize=TRUE, intercept=TRUE, trace = FALSE, eps = .Machine$double.eps)
x |
matrix of predictors |
y |
response |
lambda |
Quadratic penalty parameter. lambda=0 performs the Lasso fit. |
max.steps |
Limit the number of steps taken; the default is |
trace |
If TRUE, prints out its progress |
normalize |
Standardize the predictors? |
intercept |
Center the predictors? |
eps |
An effective zero |
The Elastic Net methodology is described in detail in Zou and Hastie (2004). The LARS-EN algorithm computes the complete elastic net solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. The structure of enet() is based on lars() coded by Efron and Hastie. Some internel functions from the lars package are called. The user should install lars before using elasticnet functions.
An "enet" object is returned, for which print, plot and predict methods exist.
Hui Zou and Trevor Hastie
Zou and Hastie (2005) "Regularization and Variable Selection via the Elastic Net" Journal of the Royal Statistical Society, Series B, 67, 301-320.
print, plot, and predict methods for enet
data(diabetes) attach(diabetes) ##fit the lasso model (treated as a special case of the elastic net) object1 <- enet(x,y,lambda=0) plot(object1) ##fit the elastic net model with lambda=1. object2 <- enet(x,y,lambda=1) plot(object2) ##early stopping after 50 LARS-EN steps object4 <- enet(x2,y,lambda=0.5,max.steps=50) plot(object4) detach(diabetes)data(diabetes) attach(diabetes) ##fit the lasso model (treated as a special case of the elastic net) object1 <- enet(x,y,lambda=0) plot(object1) ##fit the elastic net model with lambda=1. object2 <- enet(x,y,lambda=1) plot(object2) ##early stopping after 50 LARS-EN steps object4 <- enet(x2,y,lambda=0.5,max.steps=50) plot(object4) detach(diabetes)
The pitprops data is a correlation matrix that was calculated from 180 observations. There are 13
explanatory variables.
data(pitprops)data(pitprops)
Jeffers (1967) tried to interpret the first six PCs. This is a classical example showing the difficulty of interpreting principal components.
Jeffers, J. (1967) "Two case studies in the application of principal component", Applied Statistics, 16, 225-236.
Produce a plot of an enet fit. The default is a complete coefficient path.
## S3 method for class 'enet' plot(x, xvar = c("fraction", "penalty", "L1norm", "step"), use.color = FALSE, ...)## S3 method for class 'enet' plot(x, xvar = c("fraction", "penalty", "L1norm", "step"), use.color = FALSE, ...)
x |
enet object |
xvar |
The type of x variable against which to
plot. |
use.color |
a colorful plot? |
... |
Additonal arguments for generic plot. |
NULL
Hui Zou and Trevor Hastie
Zou and Hastie (2005) "Regularization and Variable Selection via the Elastic Net" Journal of the Royal Statistical Society, Series B,67,301-320.
data(diabetes) attach(diabetes) object <- enet(x,y,lambda=1) par(mfrow=c(2,2)) plot(object) plot(object,xvar="step") detach(diabetes)data(diabetes) attach(diabetes) object <- enet(x,y,lambda=1) par(mfrow=c(2,2)) plot(object) plot(object,xvar="step") detach(diabetes)
While enet() produces the entire path of solutions, predict.enet allows one to extract a prediction at a particular point along the path.
## S3 method for class 'enet' predict(object, newx, s, type = c("fit", "coefficients"), mode = c("step","fraction", "norm", "penalty"),naive=FALSE, ...)## S3 method for class 'enet' predict(object, newx, s, type = c("fit", "coefficients"), mode = c("step","fraction", "norm", "penalty"),naive=FALSE, ...)
object |
A fitted enet 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"). |
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-EN 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. Abbreviations allowed. If mode="norm", then s should be the L1 norm of the coefficient vector. If mode="penalty", then s should be the 1-norm penalty parameter. |
naive |
IF naive is True, then the naive elastic net fit is returned. |
... |
Additonal arguments for generic print. |
Starting from zero, the LARS-EN algorithm provides the entire sequence of coefficients and fits.
Either a vector/matrix of fitted values, or a vector/matrix of coefficients.
Hui Zou and Trevor Hastie
Zou and Hastie (2005) "Regularization and Variable Selection via the Elastic Net" Journal of the Royal Statistical Society, Series B,67,301-320.
print, plot, enet
data(diabetes) attach(diabetes) object <- enet(x,y,lambda=0.1) ### make predictions at the values in x, at each of the ### steps produced in object fits <- predict.enet(object, x, type="fit") ### extract the coefficient vector with L1 norm=2000 coef2000 <- predict(object, s=2000, type="coef", mode="norm") ### extract the coefficient vector with L1 norm fraction=0.45 coef.45 <- predict(object, s=0.45, type="coef", mode="fraction") detach(diabetes)data(diabetes) attach(diabetes) object <- enet(x,y,lambda=0.1) ### make predictions at the values in x, at each of the ### steps produced in object fits <- predict.enet(object, x, type="fit") ### extract the coefficient vector with L1 norm=2000 coef2000 <- predict(object, s=2000, type="coef", mode="norm") ### extract the coefficient vector with L1 norm fraction=0.45 coef.45 <- predict(object, s=0.45, type="coef", mode="fraction") detach(diabetes)
Print out an arrayspc fit.
## S3 method for class 'arrayspc' print(x, ...)## S3 method for class 'arrayspc' print(x, ...)
x |
arrayspc object |
... |
Additonal arguments for generic print. |
NULL
Hui Zou and Trevor Hastie
Zou, H., Hastie, T. and Tibshirani, R. (2006) "Sparse principal component analysis" Journal of Computational and Graphical Statistics, 15 (2), 265–286.
Print out an enet fit.
## S3 method for class 'enet' print(x, ...)## S3 method for class 'enet' print(x, ...)
x |
enet object |
... |
Additonal arguments for generic print. |
NULL
Hui Zou and Trevor Hastie
Zou and Hastie (2005) "Regularization and Variable Selection via the Elastic Net" Journal of the Royal Statistical Society, Series B,67,301-320.
Print out a spca fit.
## S3 method for class 'spca' print(x, ...)## S3 method for class 'spca' print(x, ...)
x |
spca object |
... |
Additonal arguments for generic print. |
NULL
Hui Zou and Trevor Hastie
Zou, H., Hastie, T. and Tibshirani, R. (2006) "Sparse principal component analysis" Journal of Computational and Graphical Statistics, 15 (2), 265–286.
Using an alternating minimization algorithm to minimize the SPCA criterion.
spca(x, K, para, type=c("predictor","Gram"), sparse=c("penalty","varnum"), use.corr=FALSE, lambda=1e-6, max.iter=200, trace=FALSE, eps.conv=1e-3)spca(x, K, para, type=c("predictor","Gram"), sparse=c("penalty","varnum"), use.corr=FALSE, lambda=1e-6, max.iter=200, trace=FALSE, eps.conv=1e-3)
x |
A matrix. It can be the predictor matrix or the sample covariance/correlation matrix. |
K |
Number of components |
para |
A vector of length K. All elements should be positive. If sparse="varnum", the elements integers. |
type |
If type="predictor", x is the predictor matrix. If type="Gram", the function asks the user to provide the sample covariance or correlation matrix. |
sparse |
If sparse="penalty", para is a vector of 1-norm penalty parameters. If sparse="varnum", para defines the number of sparse loadings to be obtained. This option is not discussed in the paper given below, but it is convenient in practice. |
lambda |
Quadratic penalty parameter. Default value is 1e-6. |
use.corr |
Perform PCA on the correlation matrix? This option is only effective when the argument type is set "data". |
max.iter |
Maximum number of iterations. |
trace |
If TRUE, prints out its progress. |
eps.conv |
Convergence criterion. |
PCA is shown to be equivalent to a regression-type optimization problem, then sparse loadings are obtained by imposing the 1-norm constraint on the regression coefficients. If x is a microarray matrix, use arrayspc().
A "spca" object is returned. The below are some quantities which the user may be interested in:
loadings |
The loadings of the sparse PCs |
pev |
Percentage of explained variance |
var.all |
Total variance of the predictors |
Hui Zou and Trevor Hastie
Zou, H., Hastie, T. and Tibshirani, R. (2006) "Sparse principal component analysis" Journal of Computational and Graphical Statistics, 15 (2), 265–286.
princomp, arrayspc
data(pitprops) out1<-spca(pitprops,K=6,type="Gram",sparse="penalty",trace=TRUE,para=c(0.06,0.16,0.1,0.5,0.5,0.5)) ## print the object out1 out1 out2<-spca(pitprops,K=6,type="Gram",sparse="varnum",trace=TRUE,para=c(7,4,4,1,1,1)) out2 ## to see the contents of out2 names(out2) ## to get the loadings out2$loadingsdata(pitprops) out1<-spca(pitprops,K=6,type="Gram",sparse="penalty",trace=TRUE,para=c(0.06,0.16,0.1,0.5,0.5,0.5)) ## print the object out1 out1 out2<-spca(pitprops,K=6,type="Gram",sparse="varnum",trace=TRUE,para=c(7,4,4,1,1,1)) out2 ## to see the contents of out2 names(out2) ## to get the loadings out2$loadings