vimp.BoostMLR {BoostMLR} | R Documentation |

Calculate standardized variable importance (VIMP) for each covariate or a joint VIMP of multiple covariates.

```
## S3 method for class 'BoostMLR'
vimp(Object,
xvar.names = NULL,
joint = FALSE,
setting_seed = FALSE,
seed_value = 100L)
```

`Object` |
A boosting object of class |

`xvar.names` |
Names of the x-variables for which VIMP is requested. If NULL, VIMP is calcuated for all the covariates. |

`joint` |
Whether to estimate VIMP for each covariate from |

`setting_seed` |
Set |

`seed_value` |
Seed value. |

Standardized variable importance (VIMP) is calcuated for each covariate or a joint VIMP is calculated for all the covariates specified in `xvar.names`

.

If `joint`

= FALSE, a standardized VIMP for each covariate is obtained otherwisea joint VIMP for all the covariates is obtained.
The result consists 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 (in case of joint VIMP, the matrix will have a single row) 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.

Amol Pande and Hemant Ishwaran

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

Friedman J.H. Greedy function approximation: a gradient
boosting machine, *Ann. of Statist.*, 5:1189-1232, 2001.

```
##-----------------------------------------------------------------
## Calculate individual and joint VIMP
##-----------------------------------------------------------------
# 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 = FALSE)
# Individual VIMP
Ind_vimp <- vimp.BoostMLR(boost.pred)
# Joint VIMP
Joint_vimp <- vimp.BoostMLR(boost.pred,joint = TRUE)
```

[Package *BoostMLR* version 1.0.3 Index]