multiFit: Fit models on multiple outcomes

In MTPS: Multi-Task Prediction using Stacking Algorithms

Fit models on multiple outcomes

Description

This function fit individual models to predict each outcome separately.

Usage

multiFit(xmat, ymat, method, family)

Arguments

xmat	Matrix of predictors, each row is an observation vector
ymat	Matrix of outcomes. Quantitative for family = "gaussian" and a factor of two levels for family = "binomial"
method	Method for fitting models. 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().
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 of "gaussian" or "binomial" to indicate each response family

Value

It returns a multiFit object. It is a list of 5 parameters containing information about the fitted models and fitted values for each outcome.

Examples

data("HIV")
set.seed(1)
xmat <- as.matrix(XX)
ymat <- as.matrix(YY)
id <- createFolds(rowMeans(XX), k=5, list=FALSE)
training.id <- id != 1
y.train <- ymat[training.id, ]
y.test  <- ymat[!training.id, ]
x.train <- xmat[training.id, ]
x.test  <- xmat[!training.id, ]
fit <- multiFit(xmat = x.train, ymat = y.train,
                method = rpart1, family = "gaussian")
predict(fit, x.test)

# using different base learners for different outcomes
fit.mixOut <- multiFit(xmat = x.train, ymat = y.train,
                method = c(rpart1, rpart1, glmnet.ridge,lm1,lm1),
                family = "gaussian")
predict(fit.mixOut, x.test)

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