Description Usage Arguments Details Value Author(s) Examples
Computes a K-fold cross validation for
1  | cv.sisVIVE(Y, D, Z, lambdaSeq, K = 10, intercept = TRUE, normalize = TRUE)
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Y | 
 A numeric vector of outcomes  | 
D | 
 A numeric vector of exposures  | 
Z | 
 A numeric matrix of instruments, with each column referring to one instrument  | 
lambdaSeq | 
 A numeric vector of lambdas to cross-validate from. Cross-validation will be performed only on these sequence of lambdas. You can either supply lambdaSeq or nLambda. See Details  | 
K | 
 Number of cross-validation folds  | 
intercept | 
 A logical declaring whether the intercept be included. Default is TRUE  | 
normalize | 
 A logical declaring whether the columns of Z should be scaled with variance 1. Default is TRUE  | 
Performs K-fold cross validiation to select lambda and returns the "best" lambda based on this cross-validation. If lambdaSeq is unspecified, the algorithm defaults to using the sequence of lambdas selected by sisVIVE. If lambdaSeq is specified, the algorithm will only evaluate its cross-validation on the specified lambdaSeq.
A list is returned, which contains the estimates of alpha, beta, and the set of invalid instruments for the "best" lambda chosen by cross validation
lambda | 
 "best" lambda as chosen by cross validation  | 
estCVError | 
 Estimated cross-validated error at this lambda  | 
alpha | 
 Estimate of alpha at the said lambda  | 
beta | 
 Estimate of beta, the causal effect of exposure on outcome, at the said lambda  | 
whichInvalid | 
 Estimate of set of invalid instruments at the said lambda  | 
Hyunseung Kang
1 2 3 4 5 6 7 8 9 10 11  | library(MASS)
library(lars)
n = 1000; L = 10; s= 3;
Z <- matrix(rnorm(n*L),n,L)
error <- mvrnorm(n,rep(0,2),matrix(c(1,0.8,0.8,1),2,2))
intD = rnorm(1); ZtoD =   rnorm(L,0,1); ZtoY = c(rnorm(s),rep(0,L-s)); DtoY = 1; intY = rnorm(1)
D = intD + Z %*% ZtoD + error[,1]
Y = intY + Z %*% ZtoY + D * DtoY + error[,2]
result = cv.sisVIVE(Y,D,Z,K=10)
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