svmfs

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Fits SVM mit variable selection using penalties.

Fits SVM with variable selection (clone selection) using penalties SCAD, L1 norm, Elastic Net (L1 + L2 norms) and ELastic SCAD (SCAD + L1 norm). Additionally tuning parameter search is presented by two approcaches: fixed grid or interval search. NOTE: The name of the function has been changed: svmfs instead of svm.fs!

Keywords
multivariate, models, optimize
Usage
# S3 method for default
svmfs(x,y,
				fs.method = c("scad", "1norm", "scad+L2", "DrHSVM"),
				grid.search=c("interval","discrete"),
				lambda1.set=NULL,  
				lambda2.set=NULL,
				bounds=NULL, 
				parms.coding= c("log2","none"),
				maxevals=500, 
				inner.val.method = c("cv", "gacv"),
				cross.inner= 5,
				show= c("none", "final"),
				calc.class.weights=FALSE,
				class.weights=NULL, 
				seed=123, 
				maxIter=700, 
				verbose=TRUE,
				…)
Arguments
x

input matrix with genes in columns and samples in rows!

y

numerical vector of class labels, -1 , 1

fs.method

feature selection method. Availible 'scad', '1norm' for 1-norm, "DrHSVM" for Elastic Net and "scad+L2" for Elastic SCAD

grid.search

chose the search method for tuning lambda1,2: 'interval' or 'discrete', default: 'interval'

lambda1.set

for fixed grid search: fixed grid for lambda1, default: NULL

lambda2.set

for fixed grid search: fixed grid for lambda2, default: NULL

bounds

for interval grid search: fixed grid for lambda2, default: NULL

parms.coding

for interval grid search: parms.coding: none or log2 , default: log2

maxevals

the maximum number of DIRECT function evaluations, default: 500.

calc.class.weights

calculate class.weights for SVM, default: FALSE

class.weights

a named vector of weights for the different classes, used for asymetric class sizes. Not all factor levels have to be supplied (default weight: 1). All components have to be named.

inner.val.method

method for the inner validation: cross validation, gacv , default cv

cross.inner

'cross.inner'-fold cv, default: 5

show

for interval search: show plots of DIRECT algorithm: none, final iteration, all iterations. Default: none

seed

seed

maxIter

maximal iteration, default: 700

verbose

verbose?, default: TRUE

additional argument(s)

Details

The goodness of the model is highly correlated with the choice of tuning parameter lambda. Therefore the model is trained with different lambdas and the best model with optimal tuning parameter is used in futher analysises. For very small lamdas is recomended to use maxIter, otherweise the algorithms is slow or might not converge.

The Feature Selection methods are using different techniques for finding optimal tunung parameters By SCAD SVM Generalized approximate cross validation (gacv) error is calculated for each pre-defined tuning parameter.

By L1-norm SVM the cross validation (default 5-fold) missclassification error is calculated for each lambda. After training and cross validation, the optimal lambda with minimal missclassification error is choosen, and a final model with optimal lambda is created for the whole data set.

Value

classes

vector of class labels as input 'y'

sample.names

sample names

class.method

feature selection method

%\item{cross.outer }{ outer cv }
seed

seed

model

final model

  • w - coefficients of the hyperplane

  • b - intercept of the hyperplane

  • xind - the index of the selected features (genes) in the data matrix.

  • index - the index of the resulting support vectors in the data matrix.

  • type - type of svm, from svm function

  • lam.opt - optimal lambda

  • gacv - corresponding gacv

References

Becker, N., Werft, W., Toedt, G., Lichter, P. and Benner, A.(2009) PenalizedSVM: a R-package for feature selection SVM classification, Bioinformatics, 25(13),p 1711-1712

See Also

predict.penSVM, svm (in package e1071)

Aliases
  • svmfs
  • svmfs.default
  • DrHSVM
  • scad_L2.svc
Examples
# NOT RUN {

# }
# NOT RUN {
		
		seed<- 123
		
		train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=seed )
		print(str(train)) 
		
		
		### Fixed grid ####
		
		# train SCAD SVM ####################
		# define set values of tuning parameter lambda1 for SCAD 
		lambda1.scad <- c (seq(0.01 ,0.05, .01),  seq(0.1,0.5, 0.2), 1 ) 
		# for presentation don't check  all lambdas : time consuming! 
		lambda1.scad<-lambda1.scad[2:3]
		# 
		# train SCAD SVM
		
		# computation intensive; for demostration reasons only for the first 100 features 
		# and only for 10 Iterations maxIter=10, default maxIter=700
		system.time(scad.fix<- svmfs(t(train$x)[,1:100], y=train$y, fs.method="scad", 
  		cross.outer= 0, grid.search = "discrete",  
  		lambda1.set=lambda1.scad,
  		parms.coding = "none", show="none",
  		maxIter = 10, inner.val.method = "cv", cross.inner= 5,
  		seed=seed, verbose=FALSE) 	)
			
		print(scad.fix)
			
		# train 1NORM SVM 	################	
		# define set values of tuning parameter lambda1 for 1norm
		#epsi.set<-vector(); for (num in (1:9)) epsi.set<-sort(c(epsi.set,
		#    c(num*10^seq(-5, -1, 1 ))) )
		## for presentation don't check  all lambdas : time consuming! 
		#lambda1.1norm <- 	epsi.set[c(3,5)] # 2 params
		#
		### train 1norm SVM
		## time consuming: for presentation only for the first 100 features    
		#norm1.fix<- svmfs(t(train$x)[,1:100], y=train$y, fs.method="1norm", 
		#			cross.outer= 0, grid.search = "discrete",  
		#			lambda1.set=lambda1.1norm,
		#			parms.coding = "none", show="none",
		#			maxIter = 700, inner.val.method = "cv", cross.inner= 5,
		#			seed=seed, verbose=FALSE ) 	
		#	
		#	print(norm1.fix)   
		
		### Interval  search  ####
		
		
		seed <- 123
		
		train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=seed )
		print(str(train)) 
		
		
		test<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=seed+1 )
		print(str(test)) 
		
				
		bounds=t(data.frame(log2lambda1=c(-10, 10)))
						colnames(bounds)<-c("lower", "upper")	
		
		# computation intensive; for demostration reasons only for the first 100 features 
		# and only for 10 Iterations maxIter=10, default maxIter=700
		print("start interval search")
			system.time( scad<- svmfs(t(train$x)[,1:100], y=train$y,
			 fs.method="scad", bounds=bounds, 
			 cross.outer= 0, grid.search = "interval",  maxIter = 10, 
			 inner.val.method = "cv", cross.inner= 5, maxevals=500,
			 seed=seed, parms.coding = "log2", show="none", verbose=FALSE ) )
		print("scad final model")
		print(str(scad$model))
				
		(scad.5cv.test<-predict.penSVM(scad, t(test$x)[,1:100], newdata.labels=test$y)   )
		
		
		print(paste("minimal 5-fold cv error:", scad$model$fit.info$fmin, 
		"by log2(lambda1)=", scad$model$fit.info$xmin))
		
		print(" all lambdas with the same minimum? ")
		print(scad$model$fit.info$ points.fmin) 
		
		print(paste(scad$model$fit.info$neval, "visited points"))
		
		
		print(" overview: over all visitied points in tuning parameter space 
		with corresponding cv errors")
		print(data.frame(Xtrain=scad$model$fit.info$Xtrain, 
					cv.error=scad$model$fit.info$Ytrain))
		# 						 
		
		# create  3 plots on one screen: 
		# 1st plot: distribution of initial points in tuning parameter space
		# 2nd plot: visited lambda points vs. cv errors
		# 3rd plot: the same as the 2nd plot, Ytrain.exclude points are excluded. 
		# The value cv.error = 10^16 stays for the cv error for an empty model ! 
		.plot.EPSGO.parms (scad$model$fit.info$Xtrain, scad$model$fit.info$Ytrain,
				bound=bounds, Ytrain.exclude=10^16, plot.name=NULL )
		
# }
# NOT RUN {
 # end of \donttest

# }
Documentation reproduced from package penalizedSVM, version 1.1.2, License: GPL (>= 2)

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