predictBoostMLR {BoostMLR} | R Documentation |

Function returns predicted values for the response. Also, if the response is provided, function returns test set performance, optimal boosting iteration, and variable importance (VIMP).

predictBoostMLR(Object, x, tm, id, y, M, importance = FALSE, eps = 1e-5, setting_seed = FALSE, seed_value = 100L, ...)

`Object` |
A boosting object obtained using the function |

`x` |
Data frame (or matrix) containing the test set x-values (covariates).
Covariates can be time-varying or time-invariant.
If |

`tm` |
Vector of test set time values.
If |

`id` |
Vector of test set subject identifier.
If |

`y` |
Data frame (or matrix) containing the test set y-values
(response) in case of multivariate response or a
vector of y-values in case of univariate response.
If |

`M` |
Number of boosting iterations. Value should be less than or equal
to the value specified in the |

`importance` |
Whether to calculate standardized variable importance (VIMP) for each covariate? |

`eps` |
Tolerance value used for determining the optimal |

`setting_seed` |
Set |

`seed_value` |
Seed value. |

`...` |
Further arguments passed to or from other methods. |

The predicted response and performance values are obtained for
the test data using the `Object`

grown using function `BoostMLR`

on
the training data.

`Data` |
A list with elements |

`x_Names` |
Variable names of |

`y_Names` |
Variable names of |

`mu` |
Estimate of conditional expectation of |

`mu_Mopt` |
Estimate of conditional expectation of |

`Error_Rate` |
Test set error rate for each multivariate response across the boosting iterations. |

`Mopt` |
The optimal number of boosting iteration. |

`nu` |
Regularization parameter. |

`rmse` |
Test set standardized root mean square error (sRMSE) at the |

`vimp` |
Standardized VIMP for each covariate. This consist of a list of length equal to the number of multivariate response. Each element from the list represents a matrix with number of rows equal to the number of covariates and the number of columns equal to the number of overlapping time intervals + 1 where the first column contains covariate main effects and all other columns contain covariate-time interaction effects. |

`Pred_Object` |
Useful for internal calculation. |

Amol Pande and Hemant Ishwaran

Pande A., Ishwaran H., Blackstone E.H. (2020). Boosting for multivariate longitudinal response.

Pande A., Li L., Rajeswaran J., Ehrlinger J., Kogalur U.B.,
Blackstone E.H., Ishwaran H. (2017). Boosted multivariate trees for
longitudinal data, *Machine Learning*, 106(2): 277–305.

Pande A. (2017). *Boosting for longitudinal data*. Ph.D. Dissertation,
Miller School of Medicine, University of Miami.

`BoostMLR`

,
`updateBoostMLR`

,
`simLong`

##----------------------------------------------------------------- ## Multivariate Longitudinal Response ##----------------------------------------------------------------- # Simulate data involves 3 response and 4 covariates dta <- simLong(n = 100, ntest = 100 ,N = 5, rho =.80, model = 1, q_x = 0, q_y = 0,type = "corCompSym") dtaL <- dta$dtaL trn <- dta$trn # Boosting call: Raw values of covariates, B-spline for time, # no shrinkage, no estimate of rho and phi boost.grow <- BoostMLR(x = dtaL$features[trn,], tm = dtaL$time[trn], id = dtaL$id[trn], y = dtaL$y[trn,], M = 100, VarFlag = FALSE) boost.pred <- predictBoostMLR(Object = boost.grow, x = dtaL$features[-trn,], tm = dtaL$time[-trn], id = dtaL$id[-trn], y = dtaL$y[-trn,], importance = TRUE) # Plot test set error plotBoostMLR(boost.pred$Error_Rate,xlab = "m",ylab = "Test Set Error", legend_fraction_x = 0.2)

[Package *BoostMLR* version 1.0.3 Index]