plot.mvtb: Plots the model implied effect of 1 predictor for one outcome
In mvtboost: Tree Boosting for Multivariate Outcomes

Description Usage Arguments Details Value See Also

Plots the model implied effect of 1 predictor for one outcome

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## S3 method for class 'mvtb'
plot(x, predictor.no = 1, response.no = 1, n.trees = NULL,
  X = NULL, xlab = NULL, ylab = NULL, return.grid = FALSE, ...)

`x`	`mvtb` output object
`predictor.no`	index of the predictor variable
`response.no`	index of the response variable
`n.trees`	desired number of trees. Defaults to the minimum number of trees by CV, test, or training error
`X`	optional vector, matrix, or data.frame of predictors. If included, a 'rug' (a small vertical line for each observation) is plotted on the x-axis showing the density of `predictor.no`.
`xlab`	label of the x axis
`ylab`	label of the y axis
`return.grid`	`TRUE/FALSE` return the prediction grid from `gbm`. Default is `FALSE`.
`...`	extra arguments are passed to plot. See `?par`

This is the classic partial dependence plot, where the model implied effect of the chosen predictor is plotted controlling for the other predictors. In addition to the model-implied effect, the relative influence of the predictor is included in the x-axis label. If this is not desired, a custom label can be provided via xlab.

Produces a plot of the model implied effect along with the relative influence of the predictor. If return.grid=TRUE, returns the plotting matrix as well.

plot.gbm, mvtb.perspec, for other plots, mvtb.heat to plot the covariance explained by predictors in a heatmap

mvtboost documentation built on May 2, 2019, 2:14 p.m.