Nothing

```
# This function runs the lasso on a random data split
# Input: x - the matrix of predictors
# y - the vector of responses
# ncovae - the number of covariates
# pen - the penalty (covariates are not penalized)
# family - continuous or binary response
# i - the iteration number
# Output: out.sample - the second half sample
# selected.lasso.coeff - the indeces of the selected lasso coefficients
lasso.select <- function(x, y, ncovar, pen, family, i) {
# get the in.sample and out.sample
no.samples <- length(y)
no.snps <- dim(x)[2] - ncovar
in.sample.length <- floor(no.samples/2)
in.sample <- sort(sample(seq(1:no.samples),in.sample.length))
out.sample <- sort(setdiff(seq(1:no.samples),in.sample))
# perform lasso on the in.sample
lasso.output <- glmnet( x[in.sample,], y[in.sample],
family = family, standardize = FALSE,
intercept = TRUE, penalty.factor = pen)
# save the first n/6 regression coefficients that enter the lasso path
beta.matrix <- lasso.output$beta
lasso.coeff.path <- beta.matrix@i + 1
unique.lasso.coeff <- unique(lasso.coeff.path)
# check if we have covariates
if (ncovar > 0) {
unique.lasso.coeff <- setdiff(unique.lasso.coeff,seq(1,ncovar))
selected.lasso.coeff <- unique.lasso.coeff[1 : (no.samples/6)] - ncovar
} else {
selected.lasso.coeff <- unique.lasso.coeff[1 : (no.samples/6)]
}
return(list("out.sample" = out.sample, "sel.coeff" = selected.lasso.coeff))
}
```

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