Description Usage Arguments Value Author(s) References Examples

This function produce the stability selection probability and the estimated coefficients

1 |

`x` |
This is the n by p design matrix with missing entries. |

`y` |
This is a n by 1 vector of the outcome, the outcome should be non missing. Please delete any sample with missing outcome. |

`q2` |
This is the number of variables to be bootstraped, recommended size is p/2. |

`im` |
Number of multiple imputation, increase of im will increase time cost. Default is 5. |

`E` |
You can use the 'mice' function in the mice package to generate this E which is a 'mids' data type, if E is entered, E will be used and x will be ignored. |

`lam` |
The vector of tunning parameter for each lasso implemented. |

`Probabilty` |
This is the selection probability for each covariate. The larger the probability, the more significant the variable is related to the outcome. Notice that the probability and coef are p+1 vectors and the first coef is the intercept term, where the probability is always zero. |

`coef ` |
the coefficient estimated |

Ying Liu

Liu Y, Wang Y, Feng Y, Wall MM. VARIABLE SELECTION AND PREDICTION WITH INCOMPLETE HIGH-DIMENSIONAL DATA. The annals of applied statistics. 2016;10(1):418-450. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4872715/

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 27 28 29 30 | ```
#This example is a similar simulation setting in the reference paper.
cor=0.6
prob=0.02
p=10
n=200 #sample size
Sigma=matrix(cor,p,p)#correlation of predictors
diag(Sigma)=1
mu=numeric(p)
set.seed(3)
#C is the complete design matrix without missing
C=mvrnorm(n,mu,Sigma)
#The missing indicator matrix
A<-matrix(rbinom(n*p,size=1,prob),n,p)
A[,c(1,4,6)]=0 #columns without missing
p1=inv.logit(C[,1]+C[,6]-2)
A[,5]=rbinom(n,size=1,p1) #Missing at Random
p2=inv.logit(-C[,1]-0.5*C[,6]-2)
A[,10]=rbinom(n,size=1,p2)
p3=inv.logit(C[,4]-2)
A[,9]=rbinom(n,size=1,p3)
beta=numeric(p)
beta[1:6]=c(0.1,0.2,0.5,-0.3,-.4,-0.5)*5
ct=c(0,beta)
#generating Y
Y=C%*%beta+rnorm(n)
B=C
B[A==1]=NA
fit<-mirl(B,Y,p/2,im=5)
cbind(fit$coef,fit$Probability)
``` |

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