The purpose of this function is to make the process of LASSO modelling as simple as possible.

This is a simple wrapper on two glmnet functions which, when given input matrix X and response vector y, and a criterion for model selection, will estimate the lambda parameter, and return the LASSO results as a glmnet model. This model can then be used to find coefficients and predictions.

1 | ```
easyLASSO(X, y, criterion = "lambda.1se")
``` |

`X` |
Predictor matrix, nXp, with n observations and p features. |

`y` |
Response vector, or column or row matrix. Must have length n. |

`criterion` |
String describing which lambda criterion to use in selecting a LASSO model. Choices currently are c("lambda.1se","lambda.min"). |

a glmnet model

`glmnet`

and
`cv.glmnet`

1 2 3 4 5 6 7 8 9 10 11 | ```
set.seed(1)
nObs <- 100
X <- distMat(nObs,6)
A <- cbind(c(1,0,-1,rep(0,3)))
# Y will only depend on X[,1] and X[,3]
Y <- X %*% A + 0.1*rnorm(nObs)
lassoObj <- easyLASSO(X=X,y=Y) # LASSO fitting
Yhat <- predict(lassoObj,newx=X)
yyHatPlot(Y,Yhat)
coef( lassoObj ) # Sparse coefficients
coefPlot( lassoObj )
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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