cv.MTPS {MTPS}

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)