prediction.Regression: Prediction from MKL model

In cwilso6/RMKL: Multiple Kernel Learning for Classification, Regression Problems, and Survival Settings

Description

This makes prediction for multiple kernel regression

Usage

prediction.Regression(model, ktest, outcome)

Arguments

model	MKL model
ktest	Gramm matrix of training data and test data
outcome	Outcome for the training data

Value

yhat Predicted value for each test point

predicted Sign of yhat, which is the final predicted outcome

Examples

## Not run: 
library(kernlab)
x=as.matrix(10*runif(200),ncol=1)
y=x*sin(x)+rnorm(200)
plot(x,y)


model=SEMKL.regression(k=K$K.train, outcome = y[1:150], epsilon = 0.01, penalty=100)
pred=prediction.Regression(model=model, ktest=K$K.test,outcome=y)
plot(y[151:200],pred)
abline(a=0,b=1)
plot(x[151:200],y[151:200])
points(x[151:200],pred,col='red')

## End(Not run)

cwilso6/RMKL documentation built on May 18, 2021, 9:58 a.m.