Description Usage Arguments Author(s) Examples
This function discretizes a user-provided test dataset given a discretization scheme provided by an S4 glmdisc
object.
It then applies the learnt logistic regression model and outputs its prediction (see predict.glm
).
This defines the method "discretize" which will discretize a new input dataset given a discretization scheme of S4 class glmdisc
1 2 3 4 | discretize(object, data)
## S4 method for signature 'glmdisc'
discretize(object, data)
|
object |
glmdisc object |
data |
the data to discretize according to the provided discretization scheme |
Adrien Ehrhardt.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # Simulation of a discretized logit model
set.seed(1)
x <- matrix(runif(300), nrow = 100, ncol = 3)
cuts <- seq(0, 1, length.out = 4)
xd <- apply(x, 2, function(col) as.numeric(cut(col, cuts)))
theta <- t(matrix(c(0, 0, 0, 2, 2, 2, -2, -2, -2), ncol = 3, nrow = 3))
log_odd <- rowSums(t(sapply(seq_along(xd[, 1]), function(row_id) {
sapply(
seq_along(xd[row_id, ]),
function(element) theta[xd[row_id, element], element]
)
})))
y <- rbinom(100, 1, 1 / (1 + exp(-log_odd)))
sem_disc <- glmdisc(x, y,
iter = 50, m_start = 4, test = FALSE,
validation = FALSE, criterion = "aic"
)
discretize(sem_disc, data.frame(x))
|
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