This function aims to make predictions combining a fitted RGCCA object and a prediction model for classification or regression.
Usage
rgcca_predict(
rgcca_res,
blocks_test = rgcca_res$call$blocks,
prediction_model = "lm",
metric = NULL,
...
)Arguments
- rgcca_res
A fitted RGCCA object (see
rgcca).- blocks_test
A list of test blocks from which we aim to predict the associated response block. If the test response block is present among blocks_test, metrics are computed by comparing the predictions and the true values.
- prediction_model
A string giving the model used for prediction. Please see caret::modelLookup() for a list of the available models.
- metric
A string indicating the metric of interest. It should be one of the following scores:
For classification: "Accuracy", "Kappa", "F1", "Sensitivity", "Specificity", "Pos_Pred_Value", "Neg_Pred_Value", "Precision", "Recall", "Detection_Rate", "Balanced_Accuracy".
For regression: "RMSE", "MAE".
- ...
Additional parameters to be passed to prediction_model.
Value
A list containing the following elements:
- score
The score obtained on the testing block. NA if the test block is missing.
- model
A list of the models trained using caret to make the predictions and compute the scores.
- probs
A list of data.frames with the class probabilities of the test and train response blocks predicted by the prediction model. If the prediction model does not compute class probabilities, the data.frames are empty.
- metric
A list of data.frames containing the scores obtained on the training and testing sets.
- confusion
A list containing NA for regression tasks. Otherwise, the confusion summary produced by caret for train and test.
- projection
A list of matrices containing the projections of the test blocks using the canonical components from the fitted RGCCA object. The response block is not projected.
- prediction
A list of data.frames with the predictions of the test and train response blocks.
Examples
data("Russett")
blocks <- list(
agriculture = Russett[, 1:3],
industry = Russett[, 4:5],
politic = Russett[, 6:8]
)
X_train <- lapply(blocks, function(x) x[seq(1, 30), ])
X_test <- lapply(blocks, function(x) x[seq(31, 47), ])
fit <- rgcca(X_train,
tau = 1, ncomp = c(3, 2, 3), response = 3
)
res <- rgcca_predict(fit, X_test)