prob.class {TML}

Estimated probability for binary class assignment

Description

Estimates the probability that an observation x belongs to class 1.

Usage

prob.class(pars, x)

Arguments

pars

vector of parameters, which can be decomposed as two normal vectors and two scaling parameters and has dimension 2*e+2

x

vector of dimension e

Value

real number

Author(s)

Georgios Aliatimis [e-mail]

References

Aliatimis, Georgios, Ruriko Yoshida, Burak Boyaci and James A. Grant (2023). Tropical Logistic Regression on Space of Phylogenetic Trees

Examples

library(ROCR)
T0 = Sim_Trees15
T1 = Sim_Trees25
D  = rbind(T0,T1)
Y = c(rep(0,dim(T0)[1]), rep(1,dim(T1)[1]))
N = length(Y)
set.seed(1)
train_set = sample(N,floor(0.8 * N)) ## 80/20 train-test split
pars <- trop.logistic.regression(D[train_set,],Y[train_set], penalty=1e4)
test_set = (1:N)[-train_set]
Y.hat <- rep(0, length(test_set))
for(i in 1:length(test_set))   Y.hat[i] <- prob.class(pars, D[test_set[i],])
Logit.ROC <- performance(prediction(Y.hat, Y[test_set]), measure="tpr", x.measure="fpr")
plot(Logit.ROC, lwd = 2, main = "ROC Curve for Logistic Regression Model")
print(paste("Logit.AUC=", performance(prediction(Y.hat, Y[test_set]), measure="auc")@y.values))