cv.MTPS: Evaluation using Cross-Validation

In MTPS: Multi-Task Prediction using Stacking Algorithms

Evaluation using Cross-Validation

Description

Use cross-validation to evaluate model performance.

Usage

cv.MTPS(xmat, ymat, family, nfolds = 5,
                   cv = FALSE, residual = TRUE,
                   cv.stacking.nfold = 5, method.step1, method.step2,
                   resid.type=c("deviance", "pearson", "raw"),
                   resid.std=FALSE)

Arguments

xmat	Predictor matrix, each row is an observation vector
ymat	Responses matrix. Quantitative for family = "gaussian" and a factor of two levels for family = "binomial"
family	Response type for each response. If all response variable are within the same family it can be "gaussian" or "binomial", otherwise it is a vector with elements "gaussian" and "binomial" to indicate each response family
nfolds	Integer, number of folds for Cross-Validation to evaluate the performance of stacking algorithms.
cv	Logical, indicate if use Cross-Validation Stacking algorithm
residual	Logical, indicate if use Residual Stacking algorithm
cv.stacking.nfold	Integer, number of folds for Cross-Validation Stacking algorithm. The default value is 5
method.step1	Base Learners for fitting models in Step 1 of Stacking Algorithm. It can be one base learner function for all outcomes or a list of base learner functions for each outcome. The list of all base learners can be obtained by list.learners()
method.step2	Base Learners for fitting models in Step 2 of Stacking Algorithm. (see above)
resid.type	The residual type for Residual Stacking
resid.std	Logical, whether or not use standardized residual

Value

It returns the mean squared error of continuous outcomes. AUC, accuracy, recall and precision for binary outcomes of predictions using cross-validation.

Examples

data("HIV")
cv.MTPS(xmat=XX, ymat=YY, family="gaussian", nfolds=2,
        method.step1=rpart1, method.step2=lm1)

MTPS documentation built on July 9, 2023, 7:46 p.m.