td_logistic  R Documentation 
This function does classification of incomplete events. The events grow with time. The input vector t
denotes the age of the event. The classifier takes the growing event features, X
and combines with a L2
penalty for smoothness.
td_logistic( t, X, Y, lambda = 1, scale = TRUE, num_bins = 4, quad = TRUE, interact = FALSE, logg = TRUE )
t 
The age of events. 
X 
The event features. 
Y 
The class labels. 
lambda 
The penalty coefficient. Default is 1. 
scale 
If 
num_bins 
The number of time slots to use. 
quad 
If 
interact 
if 
logg 
If 
A list with following components:

The parameters of the incompleteeventclassifier, after its fitted. 

The difference between the final two output values. 

If 

The age of events 

The value of 

The value of 
predict_tdl
for prediction.
# Generate data N < 1000 t < sort(rep(1:10, N)) set.seed(821) for(kk in 1:10){ if(kk==1){ X < seq(11,9,length=N) }else{ temp < seq((11kk+1),(9kk+1),length=N) X < c(X,temp) } } real.a.0 < seq(2,20, by=2) real.a.1 < rep(2,10) Zstar <real.a.0[t] + real.a.1[t]*X + rlogis(N, scale=0.5) Z < 1*(Zstar > 0) # Plot data for t=1 and t=8 oldpar < par(mfrow=c(1,2)) plot(X[t==1],Z[t==1], main="t=1 data") abline(v=1, lty=2) plot(X[t==8],Z[t==8],main="t=8 data") abline(v=8, lty=2) par(oldpar) # Fit model model_td < td_logistic(t,X,Z)
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