Description Usage Arguments Value Examples

Fits a TANDEM model by performing a two-stage regression. In the first stage, all upstream features (x[,upstream]) are regressed on the output y. In the second stage, the downstream features (x[,!upstream]) are regressed on the residuals of the first stage. In both stages Elastic Net regression (as implemented in cv.glmnet() from the glmnet package) is used to perform the regression.

1 2 3 4 5 6 7 8 9 10 11 |

`x` |
A feature matrix, where the rows correspond to samples and the columns to features. |

`y` |
A vector containing the response. |

`upstream` |
A boolean vector that indicates for each feature whether it's upstream (TRUE) or downstream (FALSE). |

`family` |
The family parameter that's passed to cv.glmnet(). Currently, only family='gaussian' is supported. |

`nfolds` |
Number of cross-validation folds (default is 10) used to determine the optimal lambda in cv.glmnet(). |

`foldid` |
An optional vector indicating in which cross-validation fold each sample should be. Overrides nfolds when used. |

`lambda_upstream` |
For the first stage (using the upstream features), should glmnet use lambda.min or lambda.1se? Default is lambda.1se. |

`lambda_downstream` |
For the second stage (using the downstream features), should glmnet use lambda.min or lambda.1se? Default is lambda.1se. |

`...` |
Other parameters that are passed to cv.glmnet(). |

A tandem-object.

1 2 3 4 5 6 7 8 9 | ```
# unpack example data
x = example_data$x
y = example_data$y
upstream = example_data$upstream
# fit a tandem model, determine the coefficients and create a prediction
fit = tandem(x, y, upstream, alpha=0.5)
beta = coef(fit)
y_hat = predict(fit, newx=x)
``` |

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