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Predict method for big-memory PLS-Cox models

Source: R/predict-big_pls.R
predict.big_pls_cox.Rd

Predict method for big-memory PLS-Cox models

Usage

# S3 method for class 'big_pls_cox'
predict(
  object,
  newdata = NULL,
  type = c("link", "risk", "response", "components"),
  comps = NULL,
  coef = NULL,
  ...
)

# S3 method for class 'big_pls_cox_gd'
predict(
  object,
  newdata = NULL,
  type = c("link", "risk", "response", "components"),
  comps = NULL,
  coef = NULL,
  ...
)

Arguments

object

A model fitted with big_pls_cox().

newdata

Optional matrix, data frame or bigmemory::big.matrix containing predictors to project on the latent space. When NULL the training scores are used.

type

Type of prediction: "link" for the linear predictor, "risk" or "response" for the exponential of the linear predictor, or "components" to obtain latent scores.

comps

Integer vector indicating which components to use. Defaults to all available components.

coef

Optional coefficient vector overriding the fitted Cox model coefficients.

...

Unused.

Value

Depending on type, either a numeric vector of predictions or a matrix of component scores.

References

Maumy, M., Bertrand, F. (2023). PLS models and their extension for big data. Joint Statistical Meetings (JSM 2023), Toronto, ON, Canada.

Maumy, M., Bertrand, F. (2023). bigPLS: Fitting and cross-validating PLS-based Cox models to censored big data. BioC2023 — The Bioconductor Annual Conference, Dana-Farber Cancer Institute, Boston, MA, USA. Poster. https://doi.org/10.7490/f1000research.1119546.1

Bastien, P., Bertrand, F., Meyer, N., & Maumy-Bertrand, M. (2015). Deviance residuals-based sparse PLS and sparse kernel PLS for censored data. Bioinformatics, 31(3), 397–404. doi:10.1093/bioinformatics/btu660

Bertrand, F., Bastien, P., Meyer, N., & Maumy-Bertrand, M. (2014). PLS models for censored data. In Proceedings of UseR! 2014 (p. 152).

See also

big_pls_cox(), big_pls_cox_gd(), select_ncomp(), computeDR().