Description Usage Arguments Details Value Author(s) Examples
This implements the out-of-sample prediction for an ‘jtharm’ object.
1 2 |
object |
an existing ‘jtharm’ object. |
xnew |
an object of class ‘data.frame’, ‘vector’, or ‘matrix’. This is not always necessary and depends on call context (refer to details below). |
gnew |
the ‘matrix’ of new graph links between the data to predict and the data used for training. This is not always necessary and depends on call context (refer to details below). |
type |
the type of prediction to return. |
pow |
tuning parameter for the weighted power in the interpolation predictions. |
... |
mop up additional arguments. |
The prediction inputs are dependent upon how one calls the original jtharm
generic function.
The cases are discussed next:
1) y~.: This is the default and most common case. Set xnew to your new hold-out data set and do not initialize gnew.
2) y~dG(G): The gnew argument will [most likely] be a non-symmetric ‘matrix’ of adjacencies between some new set of observations and the original x data.
3) y~sG(G): The gnew argument will [most likely] be a non-symmetric ‘matrix’ of similarity adjacencies [most likely] observed directly.
4) Non-formula call: gnew will have to provided in this case but xnew is ignored.
If type
(object) is ‘r’, a vector of predicted values is
returned. If type
(object) is ‘c’, the object returned depends
on the type argument.
Mark Vere Culp
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ## Prediction depends on the nature of the call. Consider some examples.
library(mlbench)
data(Sonar)
n=dim(Sonar)[1]
p=dim(Sonar)[2]
nu=0.2
set.seed(100)
L=sort(sample(1:n,ceiling(nu*n)))
U=setdiff(1:n,L)
U1=sample(U,ceiling(0.5*n))
y.true<-Sonar$Class
Sonar$Class[U]=NA
## Typical, call to jtharm and predict
g.jtharm1<-jtharm(Class~.,data=Sonar[c(L,U1),])
p.jtharm1<-predict(g.jtharm1,xnew=Sonar[U,-p])
tab=table(y.true[U],p.jtharm1)
1-sum(diag(tab))/sum(tab)
## Predict the graph only case Debug later
Dij<-x.scaleL(Sonar[,-p],L)
Dij<-as.matrix(cosineDist(Dij))
Dij1<-Dij[c(L,U1),c(L,U1)]
attr(Dij1,"metric")=attr(Dij,"metric")
attr(Dij1,"distance.graph")=attr(Dij,"distance.graph")
g.jtharm2<-jtharm(Class~dG(Dij1),data=Sonar[c(L,U1),])
p.jtharm2<-predict(g.jtharm2,gnew=Dij[U,c(L,U1)])
tab=table(y.true[U],p.jtharm2)
1-sum(diag(tab))/sum(tab)
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