Fit predictive model using outcome of supervised principal components, via either coxph (for surival data) or lm (for regression data)

1 | ```
superpc.fit.to.outcome(fit, data.test, score, competing.predictors = NULL, print=TRUE, iter.max = 5)
``` |

`fit` |
Object returned by superpc.train |

`data.test` |
Data object for prediction. Same form as data object documented in superpc.train. |

`score` |
Supervised principal component score, from superpc.predict |

`competing.predictors` |
Optional- list of competing predictors to be included in the model |

`print` |
Should a summary of the fit be printed? Default TRUE |

`iter.max` |
Max number of iterations used in predictive model fit. Default 5. Currently only relevant for Cox PH model |

Returns summary of coxph or lm fit

Eric Bair and Robert Tibshirani

~put references to the literature/web site here ~

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
set.seed(332)
#generate some data
x<-matrix(rnorm(1000*20),ncol=20)
y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
censoring.status<- sample(c(rep(1,17),rep(0,3)))
censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
featurenames <- paste("feature",as.character(1:1000),sep="")
data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
a<- superpc.train(data, type="survival")
fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
superpc.fit.to.outcome(a, data, fit$v.pred)
``` |

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