partial.dependence.plot {BoomSpikeSlab} | R Documentation |
Plot the relationship between Y and a single X variable, averaging over the values of the other X's.
PartialDependencePlot(model, which.variable, burn = SuggestBurn(model), data.fraction = .2, gridsize = 50, mean.only = FALSE, show.points = TRUE, xlab = NULL, ylab = NULL, ylim = NULL, report.time = FALSE, ...)
model |
An object of class |
which.variable |
Either an integer denoting the position of the X variable in the data frame used to fit the model, or a character string naming that variable. |
burn |
The number of MCMC iterations to discard as burn-in. |
data.fraction |
The fraction of observations in the predictor matrix to use when constructing the partial dependence plot. A random sub-sample of this fraction will be taken (without replacement) for the purposes of marginalizing over the remaining predictors. |
gridsize |
The number of grid points to use on the X axis. |
mean.only |
Logical. If |
show.points |
If |
xlab |
Label for the X axis. NULL produces a default label. Use "" for no label. |
ylab |
Label for the Y axis. NULL produces a default label. Use "" for no label. |
ylim |
Limits on the vertical axis. If NULL then the plot will default to its natural vertical limits. |
report.time |
Print the time required to produce the plot. |
... |
Extra arguments are passed either to 'plot' (if mean.only
is |
A partial dependence plot shows the relationship between Y and a single X variable, averaging over the values of the other X's in a possibly nonlinear regression model. Partial dependence plots are a generalization of the "added variable plot" idea from linear regression models.
A partial dependence plot is more expensive to produce than most other plots, because a set of predictions must be generated at each point on the X axis. This is done by taking a random subset of the training data, and evaluating the posterior predictive distribution with each observation's target X value set to each value of X on the grid.
Steven L. Scott
# Please see the code in ?BayesNnet