partial.BoostMLR {BoostMLR} | R Documentation |

Partial dependence plot of x and time against adjusted predicted y.

```
## S3 method for class 'BoostMLR'
partial(Object,
xvar.name,
n.x = 10,
n.tm = 10,
x.unq = NULL,
tm.unq = NULL,
Mopt,
plot.it = TRUE,
path_saveplot = NULL,
Verbose = TRUE,
...)
```

`Object` |
A boosting object of class |

`xvar.name` |
Name of the x-variable to be used for partial plot. |

`n.x` |
Maximum number of unique points used for |

`n.tm` |
Maximum number of unique points used for |

`x.unq` |
Unique values used for the partial plot for variable |

`tm.unq` |
Unique time points used for the partial plots of x against y.
Default is NULL in which case
unique values are obtained uniformaly based on the range of |

`Mopt` |
The optimal number of boosting iteration. If missing, the value from
the |

`plot.it` |
Should partial plot be displayed? |

`path_saveplot` |
Provide the location where plot should be saved. By default the plot will be saved at temporary folder. |

`Verbose` |
Display the path where the plot is saved? |

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

Partial dependence plot (Friedman, 2001) of x values specified by
`xvar.name`

against the adjusted predicted y-values over a set
of time points specified by `tm.unq`

.

`x.unq` |
Unique values used for the partial plot for variable |

`tm.unq` |
Unique time points used for the partial plots of x against y. |

`pList` |
List with number of elements equal to number of multivariate response.
Each element of the list is a matrix with number of rows equal to length of |

`sList` |
List with number of elements equal to number of multivariate response.
Each element is a matrix with the same dimension as described in |

Amol Pande and Hemant Ishwaran

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

```
##------------------------------------------------------------
## Generate partial plot for covariate x1
##-------------------------------------------------------------
dta <- simLong(n = 100, N = 5, rho =.80, model = 1, q_x = 0,
q_y = 0,type = "corCompSym")$dtaL
# Boosting call: Raw values of covariates, B-spline for time,
# no shrinkage, no estimate of rho and phi
boost.grow <- BoostMLR(x = dta$features, tm = dta$time, id = dta$id,
y = dta$y, M = 100, VarFlag = FALSE)
Partial_Plot_x1 <- partial.BoostMLR(Object = boost.grow, xvar.name = "x1",plot.it = FALSE)
```

[Package *BoostMLR* version 1.0.3 Index]