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Predict Methods for ASCA-family Objects

Source: R/predict_asca_family.R
predict_asca_family.Rd

Reconstructs the HDANOVA base on newdata via predict.hdanova() and then applies the class-specific decomposition step: sca() for ASCA/APCA/MSCA and pls() for APLS. By default, decomposition is done by projection onto training component spaces. A refit mode is also available.

Usage

# S3 method for class 'asca'
predict(object, newdata, decomposition = c("project", "refit"), ...)

# S3 method for class 'apca'
predict(object, newdata, decomposition = c("project", "refit"), ...)

# S3 method for class 'msca'
predict(object, newdata, decomposition = c("project", "refit"), ...)

# S3 method for class 'apls'
predict(object, newdata, decomposition = c("project", "refit"), ...)

# S3 method for class 'limmpca'
predict(object, newdata, decomposition = c("project", "refit"), ...)

Arguments

object

A fitted asca, apca, msca, apls, or limmpca object.

newdata

A data frame containing variables from the original model formula.

decomposition

Decomposition mode: "project" (default) projects onto training component spaces; "refit" recomputes decomposition on predicted LS matrices.

...

Reserved for compatibility; forwarded to predict.hdanova().

Value

A predicted object of the same high-level class as object.

See also

Base prediction engine: predict.hdanova. Related model constructors: asca, apca, apls, msca and limmpca.

Examples

data(candies)
test_idx  <- seq(3, nrow(candies), by = 3)
train_idx <- setdiff(seq_len(nrow(candies)), test_idx)
candies_train <- candies[train_idx, ]
candies_test  <- candies[test_idx, ]

mod_asca <- asca(assessment ~ candy * assessor, data = candies_train)
pred_asca <- predict(mod_asca, newdata = candies_test)
scoreplot(mod_asca, factor="candy", legend=TRUE)
with(pred_asca$projected, points(candy[,1], candy[,2], pch="x", cex=0.8,
                                 col=as.numeric(pred_asca$model.frame$candy)))


pred_asca_refit <- predict(mod_asca, newdata = candies_test, decomposition = "refit")

mod_apca <- apca(assessment ~ candy + assessor, data = candies_train)
pred_apca <- predict(mod_apca, newdata = candies_test)

mod_msca <- msca(assessment ~ candy, data = candies_train)
pred_msca <- predict(mod_msca, newdata = candies_test)

mod_apls <- apls(assessment ~ candy + assessor, data = candies_train)
pred_apls <- predict(mod_apls, newdata = candies_test)

mod_limmpca <- limmpca(assessment ~ candy + r(assessor),
                       data = candies_train, pca.in = 3)
pred_limmpca <- predict(mod_limmpca, newdata = candies_test)