partialPlot {boostmtree} | R Documentation |

Partial dependence plot of x against adjusted predicted y.

partialPlot(object, M = NULL, xvar.names, tm.unq, xvar.unq = NULL, npts = 25, subset, prob.class = FALSE, conditional.xvars = NULL, conditional.values = NULL, plot.it = FALSE, Variable_Factor = FALSE, path_saveplot = NULL, Verbose = TRUE, useCVflag = FALSE, ...)

`object` |
A boosting object of class |

`M` |
Fixed value for the boosting step number. If NULL, then use Mopt if it is available from the object, else use M |

`xvar.names` |
Names of the x-variables to be used. By default, all variables are plotted. |

`tm.unq` |
Unique time points used for the plots of x against y. By default, the deciles of the observed time values are used. |

`xvar.unq` |
Unique values used for the partial plot. Default is NULL in which case
unique values are obtained uniformaly based on the range of variable. Values must
be provided using list with same length as lenght of |

`npts` |
Maximum number of points used for x. Reduce this value if plots are slow. |

`subset` |
Vector indicating which rows of the x-data to be used for the analysis. The default is to use the entire data. |

`prob.class` |
In case of ordinal response, use class probability rather than cumulative probability. |

`conditional.xvars` |
Vector of character values indicating names of the x-variables
to be used for further conditioning (adjusting) the predicted y values. Variable names
should be different from |

`conditional.values` |
Vector of values taken by the variables from |

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

`Variable_Factor` |
Default is FALSE. Use TRUE if the variable specified
in |

`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? |

`useCVflag` |
Should the predicted value be based on the estimate derived from oob sample? |

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

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

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

. Analysis can be restricted to
a subset of the data using `subset`

. Further conditioning can be
imposed using `conditional.xvars`

.

Hemant Ishwaran, Amol Pande and Udaya B. Kogalur

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

## Not run: ##------------------------------------------------------------ ## Synthetic example (Response is continuous) ## high correlation, quadratic time with quadratic interaction ##------------------------------------------------------------- #simulate the data dta <- simLong(n = 50, N = 5, rho =.80, model = 2,family = "Continuous")$dtaL #basic boosting call boost.grow <- boostmtree(dta$features, dta$time, dta$id, dta$y,family = "Continuous",M = 300) #plot results #x1 has a linear main effect #x2 is quadratic with quadratic time trend pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",plot.it = TRUE) pp.obj <- partialPlot(object = boost.grow, xvar.names = "x2",plot.it = TRUE) #partial plot using "x2" as the conditional variable pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1", conditional.xvar = "x2", conditional.values = 1,plot.it = TRUE) pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1", conditional.xvar = "x2", conditional.values = 2,plot.it = TRUE) ##------------------------------------------------------------ ## Synthetic example (Response is binary) ## high correlation, quadratic time with quadratic interaction ##------------------------------------------------------------- #simulate the data dta <- simLong(n = 50, N = 5, rho =.80, model = 2,family = "Binary")$dtaL #basic boosting call boost.grow <- boostmtree(dta$features, dta$time, dta$id, dta$y,family = "Binary",M = 300) #plot results #x1 has a linear main effect #x2 is quadratic with quadratic time trend pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",plot.it = TRUE) pp.obj <- partialPlot(object = boost.grow, xvar.names = "x2",plot.it = TRUE) ##---------------------------------------------------------------------------- ## spirometry data ##---------------------------------------------------------------------------- data(spirometry, package = "boostmtree") #boosting call: cubic B-splines with 15 knots spr.obj <- boostmtree(spirometry$features, spirometry$time, spirometry$id, spirometry$y, family = "Continuous",M = 300, nu = .025, nknots = 15) #partial plot of double-lung group at 5 years dltx <- partialPlot(object = spr.obj, xvar.names = "AGE", tm.unq = 5, subset=spr.obj$x$DOUBLE==1,plot.it = TRUE) #partial plot of single-lung group at 5 years sltx <- partialPlot(object = spr.obj, xvar.names = "AGE", tm.unq = 5, subset=spr.obj$x$DOUBLE==0,plot.it = TRUE) #combine the two plots: we use lowess smoothed values dltx <- dltx$l.obj[[1]] sltx <- sltx$l.obj[[1]] plot(range(c(dltx[, 1], sltx[, 1])), range(c(dltx[, -1], sltx[, -1])), xlab = "age", ylab = "predicted y (adjusted)", type = "n") lines(dltx[, 1], dltx[, -1], lty = 1, lwd = 2, col = "red") lines(sltx[, 1], sltx[, -1], lty = 1, lwd = 2, col = "blue") legend("topright", legend = c("DLTx", "SLTx"), lty = 1, fill = c(2,4)) ## End(Not run)

[Package *boostmtree* version 1.5.0 Index]