Predict with a CLR + MLP Classifier

Generates positive-class probabilities for new samples from a fit created by [train_clr_mlp()].

Usage

predict_clr_mlp(fit, X_new)

Arguments

fit

A fitted `"omicselector_clr_mlp"` object.

X_new

Numeric matrix of new samples.

Value

A numeric vector of positive-class probabilities.

References

Aitchison J. (1986). The Statistical Analysis of Compositional Data. Chapman and Hall.

Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C. (2003). Isometric logratio transformations for compositional data analysis. Mathematical Geology, 35(3), 279-300.

Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. (2017). Microbiome datasets are compositional: and this is not optional. Frontiers in Microbiology, 8, 2224.

Quinn TP, Erb I, Richardson MF, Crowley TM. (2020). Understanding sequencing data as compositions: an outlook and review. Bioinformatics, 36(16), 4424-4432.

Examples

X_train <- matrix(rnorm(14 * 20), nrow = 20)
y_train <- factor(rep(c("control", "case"), each = 10))
fit <- train_clr_mlp(X_train, y_train, backend = "glm", verbose = FALSE)
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
predict_clr_mlp(fit, X_train[1:3, , drop = FALSE])
#> [1] 1 1 1