Description Usage Arguments Details Value References Author(s) Examples

Cross-validation for selecting the number of binary rules in the interaction AIM with binary outcomes.

1 | ```
cv.logistic.interaction(x, trt, y, K.cv=5, num.replicate=1, nsteps, mincut=0.1, backfit=F, maxnumcut=1, dirp=0, weight=1)
``` |

`x` |
n by p matrix. The covariate matrix |

`trt` |
n vector. The treatment indicator |

`y` |
n 0/1 vector. The binary response variable |

`K.cv` |
K.cv-fold cross validation |

`num.replicate` |
number of independent replications of K-fold cross validations. |

`nsteps` |
the maximum number of binary rules to be included in the index |

`mincut` |
The minimum cutting proportion for the binary rule at either end. It typically is between 0 and 0.2. |

`backfit` |
T/F. Whether the existing split points are adjusted after including a new binary rule |

`maxnumcut` |
The maximum number of binary splits per predictor |

`dirp` |
p vector. The given direction of the binary split for each of the p predictors. 0 represents "no pre-given direction"; 1 represents "(x>cut)"; -1 represents "(x<cut)". Alternatively, "dirp=0" represents that there is no pre-given direction for any of the predictor. |

`weight` |
a positive value. The weight given to responses: "weight=0" means that all observations are equally weighted. |

`cv.logistic.interaction`

implements the K-fold cross-validation for the interaction logistic AIM. It estimates the score test statistics in the test set for testing the treatment*index interaction. It also provides pre-validated fits for each observation and pre-validated score test statistic. The output can be used to select the optimal number of binary rules.

`cv.logistic.interaction`

returns

`kmax` |
the optimal number of binary rules based the cross-validation |

`meanscore` |
nsteps-vector. The cross-validated score test statistics (significant at 0.05, if greater than 1.96) for the treatment*index interaction. |

`pvfit.score` |
nsteps-vector, the pre-validated score test statistics (significant at 0.05, if greater than 1.96) for the treatment*index interaction. |

`preval` |
nsteps by n matrix. Prevalidated fits for individual observation |

L Tian and R Tibshirani Adaptive index models for marker-based risk stratification, Tech Report, available at http://www-stat.stanford.edu/~tibs/AIM.

R Tibshirani and B Efron, Pre-validation and inference in microarrays, Statist. Appl. Genet. Mol. Biol., 1:1-18, 2002.

Lu Tian and Robert Tibshirani

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
## generate data
set.seed(1)
n=400
p=10
x=matrix(rnorm(n*p), n, p)
z=(x[,1]<0.2)+(x[,5]>0.2)
trt=rbinom(n,1, 0.5)
beta=1
prb=1/(1+exp(trt-beta*trt*z-0.5))
y=rbinom(n,1,prb)
## cross-validate the logistic interaction AIM
a=cv.logistic.interaction(x, trt, y, nsteps=10, K.cv=4, num.replicate=5)
## examine score test statistics in the test set
par(mfrow=c(1,2))
plot(a$meanscore, type="l")
plot(a$pvfit.score, type="l")
## construct the index with the optimal number of binary rules
k.opt=a$kmax
a=logistic.interaction(x, trt, y, nsteps=k.opt)
print(a)
``` |

```
Loading required package: survival
$res
$res[[1]]
jmax cutp maxdir maxsc
[1,] 1 0.3065579 -1 3.308045
$res[[2]]
jmax cutp maxdir maxsc
[1,] 1 0.3065579 -1 3.308045
[2,] 5 0.2854559 1 4.734028
$res[[3]]
jmax cutp maxdir maxsc
[1,] 1 0.3065579 -1 3.308045
[2,] 5 0.2854559 1 4.734028
[3,] 9 0.9669120 1 5.224655
$maxsc
[1] 3.308045 4.734028 5.224655
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.