Decision tree in r -

my dataset :

x=data.frame(v1=c(97 ,  97 ,  85 ,  84 ,  90 ,  80 ,  81 ,  90 ,  80,    70,    90 ,   90,    90    ,95  ,  88 ,   99), + v2=c(99  , 91  , 91   ,83  , 99  , 95  , 74  , 88  , 82   , 80   , 96  ,  87  ,  92 ,   96  ,  88,    95), + v3=c( 89   ,93  , 87  , 80  , 96  , 96  , 75  , 90  , 78,    86  ,  92    ,88  ,  80,    88   , 98    ,98), + v4=c( 89  , 97   ,91  , 86  , 95 ,  95  , 89 ,  88  , 75,    82   , 99,    92  ,  95,    92   , 90,    98), + v5=c( 99   ,90  , 93   ,91  , 90  , 90  , 77  , 92  , 85,    76  ,  90,    96  ,  90,    90   , 90,    92)) > x    v1 v2 v3 v4 v5 1  97 99 89 89 99 2  97 91 93 97 90 3  85 91 87 91 93 4  84 83 80 86 91 5  90 99 96 95 90 6  80 95 96 95 90 7  81 74 75 89 77 8  90 88 90 88 92 9  80 82 78 75 85 10 70 80 86 82 76 11 90 96 92 99 90 12 90 87 88 92 96 13 90 92 80 95 90 14 95 96 88 92 90 15 88 88 98 90 90 16 99 95 98 98 92 

i used rpart package apply decision tree follows :

# classification tree rpart library(rpart) fit <- rpart(v5 ~ v1+v2+v3+v4,               method="class", data=x)  printcp(fit) # display results   classification tree: rpart(formula = v5 ~ v1 + v2 + v3 + v4, data = x, method = "class")  variables used in tree construction: character(0)  root node error: 9/16 = 0.5625  n= 16       cp nsplit rel error xerror xstd 1 0.01      0         1      0    0   > summary(fit) # detailed summary of splits  call: rpart(formula = v5 ~ v1 + v2 + v3 + v4, data = x, method = "class")   n= 16       cp nsplit rel error xerror xstd 1 0.01      0         1      0    0  node number 1: 16 observations   predicted class=90  expected loss=0.5625  p(node) =1     class counts:     1     1     1     7     1     2     1     1     1    probabilities: 0.062 0.062 0.062 0.438 0.062 0.125 0.062 0.062 0.062  

plot tree

 # plot tree   plot(fit, uniform=true,  +      main="classification tree ")  error in plot.rpart(fit, uniform = true, main = "classification tree ") :    fit not tree, root   text(fit, use.n=true, all=true, cex=.8)  error in text.rpart(fit, use.n = true, = true, cex = 0.8) :    fit not tree, root 

what wrong while applied rpart ? why give me error tree plot? how fix error error :

fit not tree, root

you use method="class" if building classification tree , method="anova" if building regression tree. looks have continuous response, should building regression tree (i.e. method="anova").