predict {penalizedSVM} | R Documentation |
This function predicts values based upon a model trained by svm. If class assigment is provided, confusion table, missclassification table, sensitivity and specificity are calculated.
## S3 method for class 'penSVM': predict(object, newdata, newdata.labels = NULL, ...)
object |
Object of class "penSVM", created by 'svm.fs' |
newdata |
A matrix containing the new input data, samples in rows, features in columns |
newdata.labels |
optional, new data class labels |
... |
additional argument(s) |
returns a list of prediction values for classes
pred.class |
predicted class |
tab |
confusion table |
error |
missclassification error |
sensitivity |
sensitivity |
specificity |
specificity |
Natalia Becker
my.seed<- 123 train<-sim.data(n = 200, ng = 100, nsg = 10, corr=FALSE, seed=my.seed ) print(str(train)) #train standard svm my.svm<-svm(x=t(train$x), y=train$y, kernel="linear") # test with other data test<- sim.data(n = 200, ng = 100, nsg = 10, seed=(my.seed+1) ) # Check accuracy standard SVM my.pred <-ifelse( predict(my.svm, t(test$x)) >0,1,-1) # Check accuracy: table(my.pred, test$y) # 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! fit.scad<- svm.fs(x=t(train$x),y=train$y, fs.method="scad", cross.outer= 0, lambda1.set=lambda1.scad[2:3], seed=my.seed) # SCAD test.error.scad<-predict(fit.scad, newdata=t(test$x),newdata.labels=test$y ) # Check accuracy SCAD SVM print(test.error.scad$tab) ######################################### # analog for 1-norm SVM epsi.set<-vector(); for (num in (1:9)) epsi.set<-sort(c(epsi.set, c(num*10^seq(-5, -1, 1 ))) ) lambda1.1norm <- epsi.set[c(3,5)] # 2 params # train 1norm SVM # time consuming: for presentation only for the first 100 samples ## DON'T RUN : fit.1norm<- svm.fs(x=t(train$x),y=train$y, fs.method="1norm", cross.outer= 0, lambda1.set=lambda1.1norm, seed=my.seed) fit.1norm<- svm.fs(x=t(train$x)[1:100,],y=train$y[1:100], fs.method="1norm", cross.outer= 0, lambda1.set=lambda1.1norm, seed=my.seed) # L1-norm SVM test.error.1norm<-predict(fit.1norm, newdata=t(test$x),newdata.labels=test$y ) # Check accuracy L1-norm SVM print(test.error.1norm$tab)