Description Usage Arguments Details Examples
This defines the method "discretize" which will discretize a new input dataset given a discretization scheme of S4 class glmdisc
This defines the method "predict" which will predict the discretization of a new input dataset given a discretization scheme of S4 class glmdisc
1 2 3 4 5 6 | predict(object, ...)
predict.glmdisc(object, predictors)
## S4 method for signature 'glmdisc'
predict(object, predictors)
|
object |
The S4 discretization object. |
... |
Essai |
predictors |
The test dataframe to discretize and for which we wish to have predictions. |
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 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
).
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"
)
predict(sem_disc, data.frame(x))
|
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