predict.dsda

An object returned by <code>dsda</code> or <code>msda</code> with binary setting.

object

An n by p matrix containing the predictors.

newx

Input training covariates matrix. <code>z</code> can be omitted if there is no covariate.

Input testing covariates matrix. <code>ztest</code> can be omitted if there is no covariate.

ztest

Coefficients of covariates obtained from <code><a rd-options="" href="/link/adjvec?package=TULIP&version=1.0.2" data-mini-rdoc="TULIP::adjvec">adjvec</a></code>. <code>gamma</code> is <code>NULL</code> if there is no covariate.

gamma

Other arguments that can be passed to <code>predict</code>.

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Predict the class labels by direct sparse discriminant analysis.

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Yuqing Pan

TULIP

A Toolbox for Linear Discriminant Analysis with Penalties

predict.dsda function

Coefficients of covariates obtained from <code><a rd-options='' href='adjvec'>adjvec</a></code>. <code>gamma</code> is <code>NULL</code> if there is no covariate.

predict.dsda: Prediction for direct sparse discriminant analysis

Description

Usage

Arguments

Value

References

See Also